DataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La estructura del notebook consiste simplemente en 4 secciones donde se abordan las preguntas 1, 2, 3, 4 y 5, estas dos últimas en conjunto.\n",
    "\n",
    "Las librerías a utilizar se muestran en la siguiente celda, en caso de instalar una, basta con ocupar el comando *pip* o instalar de la manera que prefiera.\n",
    "\n",
    "Desarrollado por David Molina P. 01-01-2023."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # Manejo de datos\n",
    "import numpy as np  # Manejo de arrays\n",
    "import matplotlib.pyplot as plt # Visualización\n",
    "import sqlite3 # Sql\n",
    "import lxml.etree as etree\n",
    "import matplotlib.cm as cm\n",
    "from scipy.ndimage.filters import gaussian_filter\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "\n",
    "pd.set_option('display.max_columns', 500) # Para poder visualizar todas las columnas\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # Se eliminan las alertas para una mejor lectura del notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pregunta 1\n",
    "*Adjunto va un archivo .sqlite, comienza por crear una base de datos con este archivo y familiarízate con la\n",
    "data.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se utilizará la librería sqlite3, la cual permitirá establecer una conexión con la base de datos y realizar querys en el mismo jupyter notebook, de esta manera se podrán responder todas las preguntas en este mismo archivo.\n",
    "\n",
    "El primer paso es establecer una conexión con la base de datos y definir un cursor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para que no tengan que clonar todo el repositorio, se importa de manera local\n",
    "database_path = \"C:/Users/xdavi/OneDrive/Desktop/database.sqlite\"\n",
    "con = sqlite3.connect(database_path)\n",
    "\n",
    "# definición del cursor\n",
    "cur = con.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder familiarizarse con la base de datos se realiza la siguiente consulta con el objetivo de observar el nombre de las tablas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('sqlite_sequence',),\n",
       " ('Player_Attributes',),\n",
       " ('Player',),\n",
       " ('Match',),\n",
       " ('League',),\n",
       " ('Country',),\n",
       " ('Team',),\n",
       " ('Team_Attributes',)]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"\"\"SELECT name FROM sqlite_master  \n",
    "  WHERE type='table';\"\"\")\n",
    "cur.fetchall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observación:** Se puede notar que existen 7 tablas, las cuales probablemente estén relacionadas unas a otras (Player con Player_Attributes por ejemplo) por lo que en paralelo se construirá un modelo Entidad-Relación (se mostrará al final de la pregunta) que permita visualizar de mejor manera la base de datos.\n",
    "\n",
    "Teniendo el nombre de las tablas es posible realizar consultas, por lo que se explorará cada tabla con el fin de familiarizarse con la base de datos. Pero, como se notó en la consulta anterior, el formato de cómo se muestra no es muy amigable, por lo tanto se definirá la siguiente función:\n",
    "\n",
    "**Query**: \n",
    "\n",
    "Recibe una SQL Query en formato (str) y retorna un dataframe de pandas correspondiente a tal consulta. Se exhibe un ejemplo en la siguiente celda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>seq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Team</td>\n",
       "      <td>103916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Country</td>\n",
       "      <td>51958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>League</td>\n",
       "      <td>51958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Match</td>\n",
       "      <td>51958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Player</td>\n",
       "      <td>11075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Player_Attributes</td>\n",
       "      <td>183978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Team_Attributes</td>\n",
       "      <td>1458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name     seq\n",
       "0               Team  103916\n",
       "1            Country   51958\n",
       "2             League   51958\n",
       "3              Match   51958\n",
       "4             Player   11075\n",
       "5  Player_Attributes  183978\n",
       "6    Team_Attributes    1458"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def query(consulta: str):\n",
    "    df = pd.read_sql(consulta, con)\n",
    "    return df\n",
    "query(\"SELECT * FROM sqlite_sequence\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede notar que la tabla **sqlite_sequence** contiene una columna \"name\" la cual contiene el nombre de las tablas y otra columna denominada \"seq\" la cual no es posible establecer qué significa, se podría buscar en internet la base de datos, y encontrar desarrollos realizados anteriormente, sin embargo:\n",
    "\n",
    "**Durante todo el trabajo no se consultarán fuentes externas, por ejemplo, buscar trabajos ya realizados con esta base datos. Esto con el objetivo de simular un caso real, donde no necesariamente se posea tal información**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuamos explorando el resto de tablas..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(299, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>team_api_id</th>\n",
       "      <th>team_fifa_api_id</th>\n",
       "      <th>team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9987</td>\n",
       "      <td>673.0</td>\n",
       "      <td>KRC Genk</td>\n",
       "      <td>GEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9993</td>\n",
       "      <td>675.0</td>\n",
       "      <td>Beerschot AC</td>\n",
       "      <td>BAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10000</td>\n",
       "      <td>15005.0</td>\n",
       "      <td>SV Zulte-Waregem</td>\n",
       "      <td>ZUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9994</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Sporting Lokeren</td>\n",
       "      <td>LOK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9984</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>KSV Cercle Brugge</td>\n",
       "      <td>CEB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>49479</td>\n",
       "      <td>10190</td>\n",
       "      <td>898.0</td>\n",
       "      <td>FC St. Gallen</td>\n",
       "      <td>GAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>49837</td>\n",
       "      <td>10191</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>FC Thun</td>\n",
       "      <td>THU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>50201</td>\n",
       "      <td>9777</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Servette FC</td>\n",
       "      <td>SER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>50204</td>\n",
       "      <td>7730</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>FC Lausanne-Sports</td>\n",
       "      <td>LAU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>51606</td>\n",
       "      <td>7896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lugano</td>\n",
       "      <td>LUG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  team_api_id  team_fifa_api_id      team_long_name team_short_name\n",
       "0        1         9987             673.0            KRC Genk             GEN\n",
       "1        2         9993             675.0        Beerschot AC             BAC\n",
       "2        3        10000           15005.0    SV Zulte-Waregem             ZUL\n",
       "3        4         9994            2007.0    Sporting Lokeren             LOK\n",
       "4        5         9984            1750.0   KSV Cercle Brugge             CEB\n",
       "..     ...          ...               ...                 ...             ...\n",
       "294  49479        10190             898.0       FC St. Gallen             GAL\n",
       "295  49837        10191            1715.0             FC Thun             THU\n",
       "296  50201         9777             324.0         Servette FC             SER\n",
       "297  50204         7730            1862.0  FC Lausanne-Sports             LAU\n",
       "298  51606         7896               NaN              Lugano             LUG\n",
       "\n",
       "[299 rows x 5 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = query(\"SELECT * FROM Team\")\n",
    "print(df.shape)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta tabla contiene información demográfica acerca de cada jugador (Fecha de nacimiento, Altura, y peso) además de identificadores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Player_Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>potential</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>attacking_work_rate</th>\n",
       "      <th>defensive_work_rate</th>\n",
       "      <th>crossing</th>\n",
       "      <th>finishing</th>\n",
       "      <th>heading_accuracy</th>\n",
       "      <th>short_passing</th>\n",
       "      <th>volleys</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>curve</th>\n",
       "      <th>free_kick_accuracy</th>\n",
       "      <th>long_passing</th>\n",
       "      <th>ball_control</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>sprint_speed</th>\n",
       "      <th>agility</th>\n",
       "      <th>reactions</th>\n",
       "      <th>balance</th>\n",
       "      <th>shot_power</th>\n",
       "      <th>jumping</th>\n",
       "      <th>stamina</th>\n",
       "      <th>strength</th>\n",
       "      <th>long_shots</th>\n",
       "      <th>aggression</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>positioning</th>\n",
       "      <th>vision</th>\n",
       "      <th>penalties</th>\n",
       "      <th>marking</th>\n",
       "      <th>standing_tackle</th>\n",
       "      <th>sliding_tackle</th>\n",
       "      <th>gk_diving</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>gk_positioning</th>\n",
       "      <th>gk_reflexes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2016-02-18 00:00:00</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49</td>\n",
       "      <td>44</td>\n",
       "      <td>71</td>\n",
       "      <td>61</td>\n",
       "      <td>44</td>\n",
       "      <td>51</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>64</td>\n",
       "      <td>49</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>47</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>54</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-11-19 00:00:00</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49</td>\n",
       "      <td>44</td>\n",
       "      <td>71</td>\n",
       "      <td>61</td>\n",
       "      <td>44</td>\n",
       "      <td>51</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>64</td>\n",
       "      <td>49</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>47</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>54</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-09-21 00:00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>66</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49</td>\n",
       "      <td>44</td>\n",
       "      <td>71</td>\n",
       "      <td>61</td>\n",
       "      <td>44</td>\n",
       "      <td>51</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>64</td>\n",
       "      <td>49</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>47</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>63</td>\n",
       "      <td>41</td>\n",
       "      <td>45</td>\n",
       "      <td>54</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-03-20 00:00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>65</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>43</td>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>46</td>\n",
       "      <td>65</td>\n",
       "      <td>54</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>34</td>\n",
       "      <td>62</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>65</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>43</td>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>46</td>\n",
       "      <td>65</td>\n",
       "      <td>54</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>34</td>\n",
       "      <td>62</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  player_fifa_api_id  player_api_id                 date  overall_rating  \\\n",
       "0   1              218353         505942  2016-02-18 00:00:00              67   \n",
       "1   2              218353         505942  2015-11-19 00:00:00              67   \n",
       "2   3              218353         505942  2015-09-21 00:00:00              62   \n",
       "3   4              218353         505942  2015-03-20 00:00:00              61   \n",
       "4   5              218353         505942  2007-02-22 00:00:00              61   \n",
       "\n",
       "   potential preferred_foot attacking_work_rate defensive_work_rate  crossing  \\\n",
       "0         71          right              medium              medium        49   \n",
       "1         71          right              medium              medium        49   \n",
       "2         66          right              medium              medium        49   \n",
       "3         65          right              medium              medium        48   \n",
       "4         65          right              medium              medium        48   \n",
       "\n",
       "   finishing  heading_accuracy  short_passing  volleys  dribbling  curve  \\\n",
       "0         44                71             61       44         51     45   \n",
       "1         44                71             61       44         51     45   \n",
       "2         44                71             61       44         51     45   \n",
       "3         43                70             60       43         50     44   \n",
       "4         43                70             60       43         50     44   \n",
       "\n",
       "   free_kick_accuracy  long_passing  ball_control  acceleration  sprint_speed  \\\n",
       "0                  39            64            49            60            64   \n",
       "1                  39            64            49            60            64   \n",
       "2                  39            64            49            60            64   \n",
       "3                  38            63            48            60            64   \n",
       "4                  38            63            48            60            64   \n",
       "\n",
       "   agility  reactions  balance  shot_power  jumping  stamina  strength  \\\n",
       "0       59         47       65          55       58       54        76   \n",
       "1       59         47       65          55       58       54        76   \n",
       "2       59         47       65          55       58       54        76   \n",
       "3       59         46       65          54       58       54        76   \n",
       "4       59         46       65          54       58       54        76   \n",
       "\n",
       "   long_shots  aggression  interceptions  positioning  vision  penalties  \\\n",
       "0          35          71             70           45      54         48   \n",
       "1          35          71             70           45      54         48   \n",
       "2          35          63             41           45      54         48   \n",
       "3          34          62             40           44      53         47   \n",
       "4          34          62             40           44      53         47   \n",
       "\n",
       "   marking  standing_tackle  sliding_tackle  gk_diving  gk_handling  \\\n",
       "0       65               69              69          6           11   \n",
       "1       65               69              69          6           11   \n",
       "2       65               66              69          6           11   \n",
       "3       62               63              66          5           10   \n",
       "4       62               63              66          5           10   \n",
       "\n",
       "   gk_kicking  gk_positioning  gk_reflexes  \n",
       "0          10               8            8  \n",
       "1          10               8            8  \n",
       "2          10               8            8  \n",
       "3           9               7            7  \n",
       "4           9               7            7  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query(\"SELECT * FROM Player_Attributes LIMIT 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede notar que esta tabla posee información acerca de las habilidades que tiene cada jugador para una fecha en especifico (*date*), por ejemplo, la precisión al cabecear (heading_accuracy) o el control del balón (ball_control). Adicionalmente existen las llaves *player_fifa_api_id* *player_api_id* que nos ayudará a cruzar las tablas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id         name\n",
       "0       1      Belgium\n",
       "1    1729      England\n",
       "2    4769       France\n",
       "3    7809      Germany\n",
       "4   10257        Italy\n",
       "5   13274  Netherlands\n",
       "6   15722       Poland\n",
       "7   17642     Portugal\n",
       "8   19694     Scotland\n",
       "9   21518        Spain\n",
       "10  24558  Switzerland"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query(\"SELECT * FROM Country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta tabla consiste simplemente en el identificador de cada país y su respectivo nombre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### League"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>4769</td>\n",
       "      <td>France Ligue 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>10257</td>\n",
       "      <td>Italy Serie A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands Eredivisie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>15722</td>\n",
       "      <td>Poland Ekstraklasa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal Liga ZON Sagres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  country_id                      name\n",
       "0       1           1    Belgium Jupiler League\n",
       "1    1729        1729    England Premier League\n",
       "2    4769        4769            France Ligue 1\n",
       "3    7809        7809     Germany 1. Bundesliga\n",
       "4   10257       10257             Italy Serie A\n",
       "5   13274       13274    Netherlands Eredivisie\n",
       "6   15722       15722        Poland Ekstraklasa\n",
       "7   17642       17642  Portugal Liga ZON Sagres\n",
       "8   19694       19694   Scotland Premier League\n",
       "9   21518       21518           Spain LIGA BBVA\n",
       "10  24558       24558  Switzerland Super League"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query(\"SELECT * FROM League\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta tabla contiene simplemente el identificador de la liga, el id del país al que pertenece y el nombre de la liga. Notar que el identificador de la liga coincide con el del país correspondiente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>team_api_id</th>\n",
       "      <th>team_fifa_api_id</th>\n",
       "      <th>team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9987</td>\n",
       "      <td>673.0</td>\n",
       "      <td>KRC Genk</td>\n",
       "      <td>GEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9993</td>\n",
       "      <td>675.0</td>\n",
       "      <td>Beerschot AC</td>\n",
       "      <td>BAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10000</td>\n",
       "      <td>15005.0</td>\n",
       "      <td>SV Zulte-Waregem</td>\n",
       "      <td>ZUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9994</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Sporting Lokeren</td>\n",
       "      <td>LOK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9984</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>KSV Cercle Brugge</td>\n",
       "      <td>CEB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>49479</td>\n",
       "      <td>10190</td>\n",
       "      <td>898.0</td>\n",
       "      <td>FC St. Gallen</td>\n",
       "      <td>GAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>49837</td>\n",
       "      <td>10191</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>FC Thun</td>\n",
       "      <td>THU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>50201</td>\n",
       "      <td>9777</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Servette FC</td>\n",
       "      <td>SER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>50204</td>\n",
       "      <td>7730</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>FC Lausanne-Sports</td>\n",
       "      <td>LAU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>51606</td>\n",
       "      <td>7896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lugano</td>\n",
       "      <td>LUG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  team_api_id  team_fifa_api_id      team_long_name team_short_name\n",
       "0        1         9987             673.0            KRC Genk             GEN\n",
       "1        2         9993             675.0        Beerschot AC             BAC\n",
       "2        3        10000           15005.0    SV Zulte-Waregem             ZUL\n",
       "3        4         9994            2007.0    Sporting Lokeren             LOK\n",
       "4        5         9984            1750.0   KSV Cercle Brugge             CEB\n",
       "..     ...          ...               ...                 ...             ...\n",
       "294  49479        10190             898.0       FC St. Gallen             GAL\n",
       "295  49837        10191            1715.0             FC Thun             THU\n",
       "296  50201         9777             324.0         Servette FC             SER\n",
       "297  50204         7730            1862.0  FC Lausanne-Sports             LAU\n",
       "298  51606         7896               NaN              Lugano             LUG\n",
       "\n",
       "[299 rows x 5 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query(\"SELECT * FROM Team\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta tabla corresponde a clubes de futbol, con sus identificadores, el nombre completo y el nombre abreviado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>team_fifa_api_id</th>\n",
       "      <th>team_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>buildUpPlaySpeed</th>\n",
       "      <th>buildUpPlaySpeedClass</th>\n",
       "      <th>buildUpPlayDribbling</th>\n",
       "      <th>buildUpPlayDribblingClass</th>\n",
       "      <th>buildUpPlayPassing</th>\n",
       "      <th>buildUpPlayPassingClass</th>\n",
       "      <th>buildUpPlayPositioningClass</th>\n",
       "      <th>chanceCreationPassing</th>\n",
       "      <th>chanceCreationPassingClass</th>\n",
       "      <th>chanceCreationCrossing</th>\n",
       "      <th>chanceCreationCrossingClass</th>\n",
       "      <th>chanceCreationShooting</th>\n",
       "      <th>chanceCreationShootingClass</th>\n",
       "      <th>chanceCreationPositioningClass</th>\n",
       "      <th>defencePressure</th>\n",
       "      <th>defencePressureClass</th>\n",
       "      <th>defenceAggression</th>\n",
       "      <th>defenceAggressionClass</th>\n",
       "      <th>defenceTeamWidth</th>\n",
       "      <th>defenceTeamWidthClass</th>\n",
       "      <th>defenceDefenderLineClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>434</td>\n",
       "      <td>9930</td>\n",
       "      <td>2010-02-22 00:00:00</td>\n",
       "      <td>60</td>\n",
       "      <td>Balanced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Little</td>\n",
       "      <td>50</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Organised</td>\n",
       "      <td>60</td>\n",
       "      <td>Normal</td>\n",
       "      <td>65</td>\n",
       "      <td>Normal</td>\n",
       "      <td>55</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Organised</td>\n",
       "      <td>50</td>\n",
       "      <td>Medium</td>\n",
       "      <td>55</td>\n",
       "      <td>Press</td>\n",
       "      <td>45</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>434</td>\n",
       "      <td>9930</td>\n",
       "      <td>2014-09-19 00:00:00</td>\n",
       "      <td>52</td>\n",
       "      <td>Balanced</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>56</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Organised</td>\n",
       "      <td>54</td>\n",
       "      <td>Normal</td>\n",
       "      <td>63</td>\n",
       "      <td>Normal</td>\n",
       "      <td>64</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Organised</td>\n",
       "      <td>47</td>\n",
       "      <td>Medium</td>\n",
       "      <td>44</td>\n",
       "      <td>Press</td>\n",
       "      <td>54</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>434</td>\n",
       "      <td>9930</td>\n",
       "      <td>2015-09-10 00:00:00</td>\n",
       "      <td>47</td>\n",
       "      <td>Balanced</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>54</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Organised</td>\n",
       "      <td>54</td>\n",
       "      <td>Normal</td>\n",
       "      <td>63</td>\n",
       "      <td>Normal</td>\n",
       "      <td>64</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Organised</td>\n",
       "      <td>47</td>\n",
       "      <td>Medium</td>\n",
       "      <td>44</td>\n",
       "      <td>Press</td>\n",
       "      <td>54</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>8485</td>\n",
       "      <td>2010-02-22 00:00:00</td>\n",
       "      <td>70</td>\n",
       "      <td>Fast</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Little</td>\n",
       "      <td>70</td>\n",
       "      <td>Long</td>\n",
       "      <td>Organised</td>\n",
       "      <td>70</td>\n",
       "      <td>Risky</td>\n",
       "      <td>70</td>\n",
       "      <td>Lots</td>\n",
       "      <td>70</td>\n",
       "      <td>Lots</td>\n",
       "      <td>Organised</td>\n",
       "      <td>60</td>\n",
       "      <td>Medium</td>\n",
       "      <td>70</td>\n",
       "      <td>Double</td>\n",
       "      <td>70</td>\n",
       "      <td>Wide</td>\n",
       "      <td>Cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>8485</td>\n",
       "      <td>2011-02-22 00:00:00</td>\n",
       "      <td>47</td>\n",
       "      <td>Balanced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Little</td>\n",
       "      <td>52</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Organised</td>\n",
       "      <td>53</td>\n",
       "      <td>Normal</td>\n",
       "      <td>48</td>\n",
       "      <td>Normal</td>\n",
       "      <td>52</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Organised</td>\n",
       "      <td>47</td>\n",
       "      <td>Medium</td>\n",
       "      <td>47</td>\n",
       "      <td>Press</td>\n",
       "      <td>52</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cover</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  team_fifa_api_id  team_api_id                 date  buildUpPlaySpeed  \\\n",
       "0   1               434         9930  2010-02-22 00:00:00                60   \n",
       "1   2               434         9930  2014-09-19 00:00:00                52   \n",
       "2   3               434         9930  2015-09-10 00:00:00                47   \n",
       "3   4                77         8485  2010-02-22 00:00:00                70   \n",
       "4   5                77         8485  2011-02-22 00:00:00                47   \n",
       "\n",
       "  buildUpPlaySpeedClass  buildUpPlayDribbling buildUpPlayDribblingClass  \\\n",
       "0              Balanced                   NaN                    Little   \n",
       "1              Balanced                  48.0                    Normal   \n",
       "2              Balanced                  41.0                    Normal   \n",
       "3                  Fast                   NaN                    Little   \n",
       "4              Balanced                   NaN                    Little   \n",
       "\n",
       "   buildUpPlayPassing buildUpPlayPassingClass buildUpPlayPositioningClass  \\\n",
       "0                  50                   Mixed                   Organised   \n",
       "1                  56                   Mixed                   Organised   \n",
       "2                  54                   Mixed                   Organised   \n",
       "3                  70                    Long                   Organised   \n",
       "4                  52                   Mixed                   Organised   \n",
       "\n",
       "   chanceCreationPassing chanceCreationPassingClass  chanceCreationCrossing  \\\n",
       "0                     60                     Normal                      65   \n",
       "1                     54                     Normal                      63   \n",
       "2                     54                     Normal                      63   \n",
       "3                     70                      Risky                      70   \n",
       "4                     53                     Normal                      48   \n",
       "\n",
       "  chanceCreationCrossingClass  chanceCreationShooting  \\\n",
       "0                      Normal                      55   \n",
       "1                      Normal                      64   \n",
       "2                      Normal                      64   \n",
       "3                        Lots                      70   \n",
       "4                      Normal                      52   \n",
       "\n",
       "  chanceCreationShootingClass chanceCreationPositioningClass  defencePressure  \\\n",
       "0                      Normal                      Organised               50   \n",
       "1                      Normal                      Organised               47   \n",
       "2                      Normal                      Organised               47   \n",
       "3                        Lots                      Organised               60   \n",
       "4                      Normal                      Organised               47   \n",
       "\n",
       "  defencePressureClass  defenceAggression defenceAggressionClass  \\\n",
       "0               Medium                 55                  Press   \n",
       "1               Medium                 44                  Press   \n",
       "2               Medium                 44                  Press   \n",
       "3               Medium                 70                 Double   \n",
       "4               Medium                 47                  Press   \n",
       "\n",
       "   defenceTeamWidth defenceTeamWidthClass defenceDefenderLineClass  \n",
       "0                45                Normal                    Cover  \n",
       "1                54                Normal                    Cover  \n",
       "2                54                Normal                    Cover  \n",
       "3                70                  Wide                    Cover  \n",
       "4                52                Normal                    Cover  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query(\"SELECT * FROM Team_Attributes LIMIT 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta tabla nos da información acerca de cada equipo de futbol, la cual corresponde a sus características de juego, como la presión defensiva (defencePressure). Cabe destacar que existe una columna *date* la cual probablemente nos indica en qué día exactamente el equipo tiene tales características."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>home_player_X1</th>\n",
       "      <th>home_player_X2</th>\n",
       "      <th>home_player_X3</th>\n",
       "      <th>home_player_X4</th>\n",
       "      <th>home_player_X5</th>\n",
       "      <th>home_player_X6</th>\n",
       "      <th>home_player_X7</th>\n",
       "      <th>home_player_X8</th>\n",
       "      <th>home_player_X9</th>\n",
       "      <th>home_player_X10</th>\n",
       "      <th>home_player_X11</th>\n",
       "      <th>away_player_X1</th>\n",
       "      <th>away_player_X2</th>\n",
       "      <th>away_player_X3</th>\n",
       "      <th>away_player_X4</th>\n",
       "      <th>away_player_X5</th>\n",
       "      <th>away_player_X6</th>\n",
       "      <th>away_player_X7</th>\n",
       "      <th>away_player_X8</th>\n",
       "      <th>away_player_X9</th>\n",
       "      <th>away_player_X10</th>\n",
       "      <th>away_player_X11</th>\n",
       "      <th>home_player_Y1</th>\n",
       "      <th>home_player_Y2</th>\n",
       "      <th>home_player_Y3</th>\n",
       "      <th>home_player_Y4</th>\n",
       "      <th>home_player_Y5</th>\n",
       "      <th>home_player_Y6</th>\n",
       "      <th>home_player_Y7</th>\n",
       "      <th>home_player_Y8</th>\n",
       "      <th>home_player_Y9</th>\n",
       "      <th>home_player_Y10</th>\n",
       "      <th>home_player_Y11</th>\n",
       "      <th>away_player_Y1</th>\n",
       "      <th>away_player_Y2</th>\n",
       "      <th>away_player_Y3</th>\n",
       "      <th>away_player_Y4</th>\n",
       "      <th>away_player_Y5</th>\n",
       "      <th>away_player_Y6</th>\n",
       "      <th>away_player_Y7</th>\n",
       "      <th>away_player_Y8</th>\n",
       "      <th>away_player_Y9</th>\n",
       "      <th>away_player_Y10</th>\n",
       "      <th>away_player_Y11</th>\n",
       "      <th>home_player_1</th>\n",
       "      <th>home_player_2</th>\n",
       "      <th>home_player_3</th>\n",
       "      <th>home_player_4</th>\n",
       "      <th>home_player_5</th>\n",
       "      <th>home_player_6</th>\n",
       "      <th>home_player_7</th>\n",
       "      <th>home_player_8</th>\n",
       "      <th>home_player_9</th>\n",
       "      <th>home_player_10</th>\n",
       "      <th>home_player_11</th>\n",
       "      <th>away_player_1</th>\n",
       "      <th>away_player_2</th>\n",
       "      <th>away_player_3</th>\n",
       "      <th>away_player_4</th>\n",
       "      <th>away_player_5</th>\n",
       "      <th>away_player_6</th>\n",
       "      <th>away_player_7</th>\n",
       "      <th>away_player_8</th>\n",
       "      <th>away_player_9</th>\n",
       "      <th>away_player_10</th>\n",
       "      <th>away_player_11</th>\n",
       "      <th>goal</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotoff</th>\n",
       "      <th>foulcommit</th>\n",
       "      <th>card</th>\n",
       "      <th>cross</th>\n",
       "      <th>corner</th>\n",
       "      <th>possession</th>\n",
       "      <th>B365H</th>\n",
       "      <th>B365D</th>\n",
       "      <th>B365A</th>\n",
       "      <th>BWH</th>\n",
       "      <th>BWD</th>\n",
       "      <th>BWA</th>\n",
       "      <th>IWH</th>\n",
       "      <th>IWD</th>\n",
       "      <th>IWA</th>\n",
       "      <th>LBH</th>\n",
       "      <th>LBD</th>\n",
       "      <th>LBA</th>\n",
       "      <th>PSH</th>\n",
       "      <th>PSD</th>\n",
       "      <th>PSA</th>\n",
       "      <th>WHH</th>\n",
       "      <th>WHD</th>\n",
       "      <th>WHA</th>\n",
       "      <th>SJH</th>\n",
       "      <th>SJD</th>\n",
       "      <th>SJA</th>\n",
       "      <th>VCH</th>\n",
       "      <th>VCD</th>\n",
       "      <th>VCA</th>\n",
       "      <th>GBH</th>\n",
       "      <th>GBD</th>\n",
       "      <th>GBA</th>\n",
       "      <th>BSH</th>\n",
       "      <th>BSD</th>\n",
       "      <th>BSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17 00:00:00</td>\n",
       "      <td>492473</td>\n",
       "      <td>9987</td>\n",
       "      <td>9993</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.73</td>\n",
       "      <td>3.40</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.35</td>\n",
       "      <td>4.20</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.75</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3.30</td>\n",
       "      <td>4.33</td>\n",
       "      <td>1.90</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.40</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.78</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.73</td>\n",
       "      <td>3.40</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492474</td>\n",
       "      <td>10000</td>\n",
       "      <td>9994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.95</td>\n",
       "      <td>1.90</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.90</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.50</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492475</td>\n",
       "      <td>9984</td>\n",
       "      <td>8635</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.38</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.50</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17 00:00:00</td>\n",
       "      <td>492476</td>\n",
       "      <td>9991</td>\n",
       "      <td>9998</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.44</td>\n",
       "      <td>3.75</td>\n",
       "      <td>7.50</td>\n",
       "      <td>1.40</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.80</td>\n",
       "      <td>1.40</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.50</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.44</td>\n",
       "      <td>3.75</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.75</td>\n",
       "      <td>6.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.44</td>\n",
       "      <td>3.75</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492477</td>\n",
       "      <td>7947</td>\n",
       "      <td>9985</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.65</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.60</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.72</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.20</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.70</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.73</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  country_id  league_id     season  stage                 date  \\\n",
       "0   1           1          1  2008/2009      1  2008-08-17 00:00:00   \n",
       "1   2           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "2   3           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "3   4           1          1  2008/2009      1  2008-08-17 00:00:00   \n",
       "4   5           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "\n",
       "   match_api_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "0        492473              9987              9993               1   \n",
       "1        492474             10000              9994               0   \n",
       "2        492475              9984              8635               0   \n",
       "3        492476              9991              9998               5   \n",
       "4        492477              7947              9985               1   \n",
       "\n",
       "   away_team_goal home_player_X1 home_player_X2 home_player_X3 home_player_X4  \\\n",
       "0               1           None           None           None           None   \n",
       "1               0           None           None           None           None   \n",
       "2               3           None           None           None           None   \n",
       "3               0           None           None           None           None   \n",
       "4               3           None           None           None           None   \n",
       "\n",
       "  home_player_X5 home_player_X6 home_player_X7 home_player_X8 home_player_X9  \\\n",
       "0           None           None           None           None           None   \n",
       "1           None           None           None           None           None   \n",
       "2           None           None           None           None           None   \n",
       "3           None           None           None           None           None   \n",
       "4           None           None           None           None           None   \n",
       "\n",
       "  home_player_X10 home_player_X11 away_player_X1 away_player_X2  \\\n",
       "0            None            None           None           None   \n",
       "1            None            None           None           None   \n",
       "2            None            None           None           None   \n",
       "3            None            None           None           None   \n",
       "4            None            None           None           None   \n",
       "\n",
       "  away_player_X3 away_player_X4 away_player_X5 away_player_X6 away_player_X7  \\\n",
       "0           None           None           None           None           None   \n",
       "1           None           None           None           None           None   \n",
       "2           None           None           None           None           None   \n",
       "3           None           None           None           None           None   \n",
       "4           None           None           None           None           None   \n",
       "\n",
       "  away_player_X8 away_player_X9 away_player_X10 away_player_X11  \\\n",
       "0           None           None            None            None   \n",
       "1           None           None            None            None   \n",
       "2           None           None            None            None   \n",
       "3           None           None            None            None   \n",
       "4           None           None            None            None   \n",
       "\n",
       "  home_player_Y1 home_player_Y2 home_player_Y3 home_player_Y4 home_player_Y5  \\\n",
       "0           None           None           None           None           None   \n",
       "1           None           None           None           None           None   \n",
       "2           None           None           None           None           None   \n",
       "3           None           None           None           None           None   \n",
       "4           None           None           None           None           None   \n",
       "\n",
       "  home_player_Y6 home_player_Y7 home_player_Y8 home_player_Y9 home_player_Y10  \\\n",
       "0           None           None           None           None            None   \n",
       "1           None           None           None           None            None   \n",
       "2           None           None           None           None            None   \n",
       "3           None           None           None           None            None   \n",
       "4           None           None           None           None            None   \n",
       "\n",
       "  home_player_Y11 away_player_Y1 away_player_Y2 away_player_Y3 away_player_Y4  \\\n",
       "0            None           None           None           None           None   \n",
       "1            None           None           None           None           None   \n",
       "2            None           None           None           None           None   \n",
       "3            None           None           None           None           None   \n",
       "4            None           None           None           None           None   \n",
       "\n",
       "  away_player_Y5 away_player_Y6 away_player_Y7 away_player_Y8 away_player_Y9  \\\n",
       "0           None           None           None           None           None   \n",
       "1           None           None           None           None           None   \n",
       "2           None           None           None           None           None   \n",
       "3           None           None           None           None           None   \n",
       "4           None           None           None           None           None   \n",
       "\n",
       "  away_player_Y10 away_player_Y11 home_player_1 home_player_2 home_player_3  \\\n",
       "0            None            None          None          None          None   \n",
       "1            None            None          None          None          None   \n",
       "2            None            None          None          None          None   \n",
       "3            None            None          None          None          None   \n",
       "4            None            None          None          None          None   \n",
       "\n",
       "  home_player_4 home_player_5 home_player_6 home_player_7 home_player_8  \\\n",
       "0          None          None          None          None          None   \n",
       "1          None          None          None          None          None   \n",
       "2          None          None          None          None          None   \n",
       "3          None          None          None          None          None   \n",
       "4          None          None          None          None          None   \n",
       "\n",
       "  home_player_9 home_player_10 home_player_11 away_player_1 away_player_2  \\\n",
       "0          None           None           None          None          None   \n",
       "1          None           None           None          None          None   \n",
       "2          None           None           None          None          None   \n",
       "3          None           None           None          None          None   \n",
       "4          None           None           None          None          None   \n",
       "\n",
       "  away_player_3 away_player_4 away_player_5 away_player_6 away_player_7  \\\n",
       "0          None          None          None          None          None   \n",
       "1          None          None          None          None          None   \n",
       "2          None          None          None          None          None   \n",
       "3          None          None          None          None          None   \n",
       "4          None          None          None          None          None   \n",
       "\n",
       "  away_player_8 away_player_9 away_player_10 away_player_11  goal shoton  \\\n",
       "0          None          None           None           None  None   None   \n",
       "1          None          None           None           None  None   None   \n",
       "2          None          None           None           None  None   None   \n",
       "3          None          None           None           None  None   None   \n",
       "4          None          None           None           None  None   None   \n",
       "\n",
       "  shotoff foulcommit  card cross corner possession  B365H  B365D  B365A   BWH  \\\n",
       "0    None       None  None  None   None       None   1.73   3.40   5.00  1.75   \n",
       "1    None       None  None  None   None       None   1.95   3.20   3.60  1.80   \n",
       "2    None       None  None  None   None       None   2.38   3.30   2.75  2.40   \n",
       "3    None       None  None  None   None       None   1.44   3.75   7.50  1.40   \n",
       "4    None       None  None  None   None       None   5.00   3.50   1.65  5.00   \n",
       "\n",
       "    BWD   BWA   IWH  IWD  IWA   LBH  LBD   LBA   PSH   PSD   PSA   WHH   WHD  \\\n",
       "0  3.35  4.20  1.85  3.2  3.5  1.80  3.3  3.75  None  None  None  1.70  3.30   \n",
       "1  3.30  3.95  1.90  3.2  3.5  1.90  3.2  3.50  None  None  None  1.83  3.30   \n",
       "2  3.30  2.55  2.60  3.1  2.3  2.50  3.2  2.50  None  None  None  2.50  3.25   \n",
       "3  4.00  6.80  1.40  3.9  6.0  1.44  3.6  6.50  None  None  None  1.44  3.75   \n",
       "4  3.50  1.60  4.00  3.3  1.7  4.00  3.4  1.72  None  None  None  4.20  3.40   \n",
       "\n",
       "    WHA   SJH  SJD   SJA   VCH   VCD   VCA   GBH   GBD   GBA   BSH   BSD   BSA  \n",
       "0  4.33  1.90  3.3  4.00  1.65  3.40  4.50  1.78  3.25  4.00  1.73  3.40  4.20  \n",
       "1  3.60  1.95  3.3  3.80  2.00  3.25  3.25  1.85  3.25  3.75  1.91  3.25  3.60  \n",
       "2  2.40  2.63  3.3  2.50  2.35  3.25  2.65  2.50  3.20  2.50  2.30  3.20  2.75  \n",
       "3  6.00  1.44  4.0  7.50  1.45  3.75  6.50  1.50  3.75  5.50  1.44  3.75  6.50  \n",
       "4  1.70  4.50  3.5  1.73  4.50  3.40  1.65  4.50  3.50  1.65  4.75  3.30  1.67  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query(\"SELECT * FROM Match LIMIT 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por ultimo, está la tabla Match, la cual da información acerca de los partidos jugados, como la cantidad de goles del local y visitante, así como también los jugadores que disputaron el partido."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diagrama relacional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con esta información en mano, es posible construir el **diagrama relacional** de la base de datos, *PK* representa a la llave primaria, y *FK* a las llaves foráneas. No se escriben el resto de las columnas de las tablas debido a la cantidad de ellas, sin embargo se puede hacer una idea por el nombre de las tablas correspondientes.\n",
    "\n",
    "![alt text](SoccerChallengeDiagram.drawio.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pregunta 2\n",
    "*Las tablas de Match, League, Country y Team tiene toda la información para describir un partido\n",
    "detalladamente. Crea una query SQL para obtener la información detallada por partido uniendo las tablas\n",
    "Match, League, Country y Team.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para lograr lo anterior, se utiliza la función *query* definida en la pregunta anterior, y se utiliza además el diagrama relacional, el cual nos sirve para poder unir las tablas.\n",
    "\n",
    "Se utilizan todas las columnas de la tabla Match, y de las tablas League, Country y Team, se obtiene solamente el nombre de la liga, el país y el equipo respectivamente, pues el resto de columnas o bien es redundante (Abreviatura del nombre del equipo por ejemplo) o ya está disponible en la tabla Match (los ids).\n",
    "\n",
    "Se utiliza también el operador INNER JOIN, con el objetivo de tener toda la información disponible.\n",
    "\n",
    "Se guarda la consulta como una pandas dataframe llamado **Match**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>home_player_X1</th>\n",
       "      <th>home_player_X2</th>\n",
       "      <th>home_player_X3</th>\n",
       "      <th>home_player_X4</th>\n",
       "      <th>home_player_X5</th>\n",
       "      <th>home_player_X6</th>\n",
       "      <th>home_player_X7</th>\n",
       "      <th>home_player_X8</th>\n",
       "      <th>home_player_X9</th>\n",
       "      <th>home_player_X10</th>\n",
       "      <th>home_player_X11</th>\n",
       "      <th>away_player_X1</th>\n",
       "      <th>away_player_X2</th>\n",
       "      <th>away_player_X3</th>\n",
       "      <th>away_player_X4</th>\n",
       "      <th>away_player_X5</th>\n",
       "      <th>away_player_X6</th>\n",
       "      <th>away_player_X7</th>\n",
       "      <th>away_player_X8</th>\n",
       "      <th>away_player_X9</th>\n",
       "      <th>away_player_X10</th>\n",
       "      <th>away_player_X11</th>\n",
       "      <th>home_player_Y1</th>\n",
       "      <th>home_player_Y2</th>\n",
       "      <th>home_player_Y3</th>\n",
       "      <th>home_player_Y4</th>\n",
       "      <th>home_player_Y5</th>\n",
       "      <th>home_player_Y6</th>\n",
       "      <th>home_player_Y7</th>\n",
       "      <th>home_player_Y8</th>\n",
       "      <th>home_player_Y9</th>\n",
       "      <th>home_player_Y10</th>\n",
       "      <th>home_player_Y11</th>\n",
       "      <th>away_player_Y1</th>\n",
       "      <th>away_player_Y2</th>\n",
       "      <th>away_player_Y3</th>\n",
       "      <th>away_player_Y4</th>\n",
       "      <th>away_player_Y5</th>\n",
       "      <th>away_player_Y6</th>\n",
       "      <th>away_player_Y7</th>\n",
       "      <th>away_player_Y8</th>\n",
       "      <th>away_player_Y9</th>\n",
       "      <th>away_player_Y10</th>\n",
       "      <th>away_player_Y11</th>\n",
       "      <th>home_player_1</th>\n",
       "      <th>home_player_2</th>\n",
       "      <th>home_player_3</th>\n",
       "      <th>home_player_4</th>\n",
       "      <th>home_player_5</th>\n",
       "      <th>home_player_6</th>\n",
       "      <th>home_player_7</th>\n",
       "      <th>home_player_8</th>\n",
       "      <th>home_player_9</th>\n",
       "      <th>home_player_10</th>\n",
       "      <th>home_player_11</th>\n",
       "      <th>away_player_1</th>\n",
       "      <th>away_player_2</th>\n",
       "      <th>away_player_3</th>\n",
       "      <th>away_player_4</th>\n",
       "      <th>away_player_5</th>\n",
       "      <th>away_player_6</th>\n",
       "      <th>away_player_7</th>\n",
       "      <th>away_player_8</th>\n",
       "      <th>away_player_9</th>\n",
       "      <th>away_player_10</th>\n",
       "      <th>away_player_11</th>\n",
       "      <th>goal</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotoff</th>\n",
       "      <th>foulcommit</th>\n",
       "      <th>card</th>\n",
       "      <th>cross</th>\n",
       "      <th>corner</th>\n",
       "      <th>possession</th>\n",
       "      <th>league_name</th>\n",
       "      <th>country_name</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17 00:00:00</td>\n",
       "      <td>492473</td>\n",
       "      <td>9987</td>\n",
       "      <td>9993</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>KRC Genk</td>\n",
       "      <td>Beerschot AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492474</td>\n",
       "      <td>10000</td>\n",
       "      <td>9994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>SV Zulte-Waregem</td>\n",
       "      <td>Sporting Lokeren</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492475</td>\n",
       "      <td>9984</td>\n",
       "      <td>8635</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>KSV Cercle Brugge</td>\n",
       "      <td>RSC Anderlecht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17 00:00:00</td>\n",
       "      <td>492476</td>\n",
       "      <td>9991</td>\n",
       "      <td>9998</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>KAA Gent</td>\n",
       "      <td>RAEC Mons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16 00:00:00</td>\n",
       "      <td>492477</td>\n",
       "      <td>7947</td>\n",
       "      <td>9985</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>FCV Dender EH</td>\n",
       "      <td>Standard de Liège</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  country_id  league_id     season  stage                 date  \\\n",
       "0   1           1          1  2008/2009      1  2008-08-17 00:00:00   \n",
       "1   2           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "2   3           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "3   4           1          1  2008/2009      1  2008-08-17 00:00:00   \n",
       "4   5           1          1  2008/2009      1  2008-08-16 00:00:00   \n",
       "\n",
       "   match_api_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "0        492473              9987              9993               1   \n",
       "1        492474             10000              9994               0   \n",
       "2        492475              9984              8635               0   \n",
       "3        492476              9991              9998               5   \n",
       "4        492477              7947              9985               1   \n",
       "\n",
       "   away_team_goal  home_player_X1  home_player_X2  home_player_X3  \\\n",
       "0               1             NaN             NaN             NaN   \n",
       "1               0             NaN             NaN             NaN   \n",
       "2               3             NaN             NaN             NaN   \n",
       "3               0             NaN             NaN             NaN   \n",
       "4               3             NaN             NaN             NaN   \n",
       "\n",
       "   home_player_X4  home_player_X5  home_player_X6  home_player_X7  \\\n",
       "0             NaN             NaN             NaN             NaN   \n",
       "1             NaN             NaN             NaN             NaN   \n",
       "2             NaN             NaN             NaN             NaN   \n",
       "3             NaN             NaN             NaN             NaN   \n",
       "4             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   home_player_X8  home_player_X9  home_player_X10  home_player_X11  \\\n",
       "0             NaN             NaN              NaN              NaN   \n",
       "1             NaN             NaN              NaN              NaN   \n",
       "2             NaN             NaN              NaN              NaN   \n",
       "3             NaN             NaN              NaN              NaN   \n",
       "4             NaN             NaN              NaN              NaN   \n",
       "\n",
       "   away_player_X1  away_player_X2  away_player_X3  away_player_X4  \\\n",
       "0             NaN             NaN             NaN             NaN   \n",
       "1             NaN             NaN             NaN             NaN   \n",
       "2             NaN             NaN             NaN             NaN   \n",
       "3             NaN             NaN             NaN             NaN   \n",
       "4             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   away_player_X5  away_player_X6  away_player_X7  away_player_X8  \\\n",
       "0             NaN             NaN             NaN             NaN   \n",
       "1             NaN             NaN             NaN             NaN   \n",
       "2             NaN             NaN             NaN             NaN   \n",
       "3             NaN             NaN             NaN             NaN   \n",
       "4             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   away_player_X9  away_player_X10  away_player_X11  home_player_Y1  \\\n",
       "0             NaN              NaN              NaN             NaN   \n",
       "1             NaN              NaN              NaN             NaN   \n",
       "2             NaN              NaN              NaN             NaN   \n",
       "3             NaN              NaN              NaN             NaN   \n",
       "4             NaN              NaN              NaN             NaN   \n",
       "\n",
       "   home_player_Y2  home_player_Y3  home_player_Y4  home_player_Y5  \\\n",
       "0             NaN             NaN             NaN             NaN   \n",
       "1             NaN             NaN             NaN             NaN   \n",
       "2             NaN             NaN             NaN             NaN   \n",
       "3             NaN             NaN             NaN             NaN   \n",
       "4             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   home_player_Y6  home_player_Y7  home_player_Y8  home_player_Y9  \\\n",
       "0             NaN             NaN             NaN             NaN   \n",
       "1             NaN             NaN             NaN             NaN   \n",
       "2             NaN             NaN             NaN             NaN   \n",
       "3             NaN             NaN             NaN             NaN   \n",
       "4             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   home_player_Y10  home_player_Y11  away_player_Y1  away_player_Y2  \\\n",
       "0              NaN              NaN             NaN             NaN   \n",
       "1              NaN              NaN             NaN             NaN   \n",
       "2              NaN              NaN             NaN             NaN   \n",
       "3              NaN              NaN             NaN             NaN   \n",
       "4              NaN              NaN             NaN             NaN   \n",
       "\n",
       "   away_player_Y3  away_player_Y4  away_player_Y5  away_player_Y6  \\\n",
       "0             NaN             NaN             NaN             NaN   \n",
       "1             NaN             NaN             NaN             NaN   \n",
       "2             NaN             NaN             NaN             NaN   \n",
       "3             NaN             NaN             NaN             NaN   \n",
       "4             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   away_player_Y7  away_player_Y8  away_player_Y9  away_player_Y10  \\\n",
       "0             NaN             NaN             NaN              NaN   \n",
       "1             NaN             NaN             NaN              NaN   \n",
       "2             NaN             NaN             NaN              NaN   \n",
       "3             NaN             NaN             NaN              NaN   \n",
       "4             NaN             NaN             NaN              NaN   \n",
       "\n",
       "   away_player_Y11  home_player_1  home_player_2  home_player_3  \\\n",
       "0              NaN            NaN            NaN            NaN   \n",
       "1              NaN            NaN            NaN            NaN   \n",
       "2              NaN            NaN            NaN            NaN   \n",
       "3              NaN            NaN            NaN            NaN   \n",
       "4              NaN            NaN            NaN            NaN   \n",
       "\n",
       "   home_player_4  home_player_5  home_player_6  home_player_7  home_player_8  \\\n",
       "0            NaN            NaN            NaN            NaN            NaN   \n",
       "1            NaN            NaN            NaN            NaN            NaN   \n",
       "2            NaN            NaN            NaN            NaN            NaN   \n",
       "3            NaN            NaN            NaN            NaN            NaN   \n",
       "4            NaN            NaN            NaN            NaN            NaN   \n",
       "\n",
       "   home_player_9  home_player_10  home_player_11  away_player_1  \\\n",
       "0            NaN             NaN             NaN            NaN   \n",
       "1            NaN             NaN             NaN            NaN   \n",
       "2            NaN             NaN             NaN            NaN   \n",
       "3            NaN             NaN             NaN            NaN   \n",
       "4            NaN             NaN             NaN            NaN   \n",
       "\n",
       "   away_player_2  away_player_3  away_player_4  away_player_5  away_player_6  \\\n",
       "0            NaN            NaN            NaN            NaN            NaN   \n",
       "1            NaN            NaN            NaN            NaN            NaN   \n",
       "2            NaN            NaN            NaN            NaN            NaN   \n",
       "3            NaN            NaN            NaN            NaN            NaN   \n",
       "4            NaN            NaN            NaN            NaN            NaN   \n",
       "\n",
       "   away_player_7  away_player_8  away_player_9  away_player_10  \\\n",
       "0            NaN            NaN            NaN             NaN   \n",
       "1            NaN            NaN            NaN             NaN   \n",
       "2            NaN            NaN            NaN             NaN   \n",
       "3            NaN            NaN            NaN             NaN   \n",
       "4            NaN            NaN            NaN             NaN   \n",
       "\n",
       "   away_player_11  goal shoton shotoff foulcommit  card cross corner  \\\n",
       "0             NaN  None   None    None       None  None  None   None   \n",
       "1             NaN  None   None    None       None  None  None   None   \n",
       "2             NaN  None   None    None       None  None  None   None   \n",
       "3             NaN  None   None    None       None  None  None   None   \n",
       "4             NaN  None   None    None       None  None  None   None   \n",
       "\n",
       "  possession             league_name country_name     home_team_name  \\\n",
       "0       None  Belgium Jupiler League      Belgium           KRC Genk   \n",
       "1       None  Belgium Jupiler League      Belgium   SV Zulte-Waregem   \n",
       "2       None  Belgium Jupiler League      Belgium  KSV Cercle Brugge   \n",
       "3       None  Belgium Jupiler League      Belgium           KAA Gent   \n",
       "4       None  Belgium Jupiler League      Belgium      FCV Dender EH   \n",
       "\n",
       "      away_team_name  \n",
       "0       Beerschot AC  \n",
       "1   Sporting Lokeren  \n",
       "2     RSC Anderlecht  \n",
       "3          RAEC Mons  \n",
       "4  Standard de Liège  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Match = query(\"\"\"\n",
    "      SELECT Match.*, League.name as league_name, Country.name as country_name, Home_Team.team_long_name as home_team_name,\n",
    "      Away_Team.team_long_name as away_team_name\n",
    "      \n",
    "      FROM Match\n",
    "      INNER JOIN League on Match.league_id=League.id\n",
    "      INNER JOIN Country on Match.country_id = Country.id\n",
    "      INNER JOIN Team Home_Team on Match.home_team_api_id = Home_Team.team_api_id\n",
    "      INNER JOIN Team Away_Team on Match.away_team_api_id = Away_Team.team_api_id\n",
    "      \"\"\")\n",
    "\n",
    "# Columnas a considerar, se eliminan las que no se logró reconocer qué columnas eran\n",
    "cols = [col for col in Match.columns if not col in \"B365H\tB365D\tB365A\tBWH\tBWD\tBWA\tIWH\tIWD\tIWA\tLBH\tLBD\tLBA\tPSH\tPSD\tPSA\tWHH\tWHD\tWHA\tSJH\tSJD\tSJA\tVCH\tVCD\tVCA\tGBH\tGBD\tGBA\tBSH\tBSD\tBSA\"]\n",
    "Match = Match[cols]\n",
    "Match.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pregunta 3\n",
    "*¿Qué insights ves en la información detallada del partido?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para la realización de esta pregunta se listarán las columnas que sean relevantes con respecto al partido , con su respectiva descripción, en caso de que el nombre de la columna no sea lo suficientemente claro, se ocuparán los datos para deducir qué significa. Y finalmente se dará una conclusión acerca de esta pregunta.\n",
    "\n",
    "* *date*: Corresponde a la fecha en la cual se jugó el partido. Esta es una feature importante, pues si nos interesa saber la cantidad de goles que recibió el arquero X, es necesario tener tal columna.\n",
    "* *{home,away}_team_api_id*: Corresponde a los identificadores del equipo local y visitante respectivamente.\n",
    "* *{home, away}_team_goal*: Cantidad de goles que realizó el equipo local y visitante respectivamente.\n",
    "* *{home, away}_player{Xi}*: La posición horizontal del jugador del equipo local/visitante con $i\\in \\{1,\\dotsc,11\\}$- \n",
    "* *{home, away}_player{Yi}*: La posición vertical del jugador del equipo local/visitante con $i\\in \\{1,\\dotsc,11\\}$.\n",
    "* *{home, away}_player{i}*: El id del jugador (player_api_id en la tabla Player) local y visitante respectivamente con $i\\in \\{1,\\dotsc,11\\}$.\n",
    "* *goal*: Información de los goles en formato XML.\n",
    "* *shoton*: Información de los tiros (shot) al arco (on) también en formato XML.\n",
    "* *shotoff*: Información de los tiros (shot) fuera del arco (on) también en formato XML.\n",
    "* *foulcommit*: Información de las faltas cometidas (XML).\n",
    "* *card*: Tarjetas amarillas y rojas (XML).\n",
    "* *cross*: Cruces (pases largos) (XML).\n",
    "* *corner*: Tiros de esquina (XML).\n",
    "* *possession*: Posesión del balón (XML).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *{home, away}_player{Xi,Yi}:* Posición de los jugadores\n",
    "Para verificar esto, se utilizará la información de algunos equipos conocidos (FC Barcelona, por ejemplo) y se escogerá el último partido disponible, y se realizará un grafico de la posición de los jugadores, junto con su nombre (disponible en la tabla Player)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "get_X_Y_names:\n",
    "Recibe: team_name: Nombre del equipo de futbol (Puede ser una aproximación, no necesariamente el nombre completo)\n",
    "Retorna:\n",
    "- X: Posición en el eje x de los jugadores (list)\n",
    "- Y: Posición en el eje y de los jugadores (list)\n",
    "- names: Nombre de los jugadores (list)\n",
    "\"\"\"\n",
    "def get_X_Y_names(team_name: str):\n",
    "    # Información del último partido de \"team_name\"\n",
    "    df = query(\"\"\"\n",
    "        SELECT * FROM Match\n",
    "        WHERE home_team_api_id IN (SELECT team_api_id FROM Team WHERE team_long_name LIKE \"%{}%\")\n",
    "        ORDER BY season DESC LIMIT 1\n",
    "        \"\"\".format(team_name))\n",
    "    # Columnas de las posiciones horizontales (X) y verticales (Y)\n",
    "    X_positions = [\"home_player_X\"+str(i+1) for i in range(11)]\n",
    "    Y_positions = [\"home_player_Y\"+str(i+1) for i in range(11)]\n",
    "\n",
    "    # id de los jugadores\n",
    "    id_positions = [\"home_player_\"+str(i+1) for i in range(11)]\n",
    "\n",
    "    # id, player_name (para el gráfico)\n",
    "    player_info = query(\"SELECT player_api_id, player_name FROM Player\")\n",
    "    # Agregamos los nombres de los jugadores\n",
    "    names = df[id_positions].melt().merge(player_info, how=\"left\", left_on=\"value\", right_on=\"player_api_id\")[\"player_name\"]\n",
    "\n",
    "    # Gráfico\n",
    "    X = df[X_positions].T.to_numpy()\n",
    "    Y = df[Y_positions].T.to_numpy()\n",
    "    return X, Y, names\n",
    "\n",
    "# Gráfico\n",
    "fig, ax = plt.subplots(2,2 ,figsize=(15,15))\n",
    "ax = ax.flatten()\n",
    "# Equipos a considerar\n",
    "Teams = [\"Barcelona\", \"Real Madrid\", \"Bayern Munich\", \"Manchester United\"]\n",
    "for team_number, team in enumerate(Teams):\n",
    "    X, Y, names = get_X_Y_names(team)\n",
    "    ax[team_number].scatter(X, Y)\n",
    "    for i, txt in enumerate(names):\n",
    "        # Se ajusta la posición de los nombres\n",
    "        if i>=1:\n",
    "                ax[team_number].annotate(txt, (X[i], Y[i]+0.2))\n",
    "        else:\n",
    "                ax[team_number].annotate(txt, (X[i], Y[i]+0.2))\n",
    "        ax[team_number].spines['top'].set_visible(False)\n",
    "        ax[team_number].spines['right'].set_visible(False)\n",
    "        ax[team_number].set_title(team, fontsize=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede notar que las posiciones horizontalmente están invertidas (Dani Alves juega de lateral derecho históricamente) para solucionar lo anterior, simplemente se puede considerar -Posición en el eje X: Para efectos del modelamiento de la solución, no afecta este inconveniente, de igual manera se mostrará el gráfico realizando la transformación (multiplicar por -1 la posición):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2 ,figsize=(15,15))\n",
    "ax = ax.flatten()\n",
    "Teams = [\"Barcelona\", \"Real Madrid\", \"Bayern Munich\", \"Manchester United\"]\n",
    "for team_number, team in enumerate(Teams):\n",
    "    X, Y, names = get_X_Y_names(team)\n",
    "    X = -X\n",
    "    ax[team_number].scatter(X, Y)\n",
    "    for i, txt in enumerate(names):\n",
    "        if i>=1:\n",
    "                ax[team_number].annotate(txt, (X[i], Y[i]+0.2))\n",
    "        else:\n",
    "                ax[team_number].annotate(txt, (X[i], Y[i]+0.2))\n",
    "        ax[team_number].spines['top'].set_visible(False)\n",
    "        ax[team_number].spines['right'].set_visible(False)\n",
    "        ax[team_number].set_title(team, fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acá se puede notar que las posiciones horizontales calzan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *{home_away}_team_goal*: Cantidad de goles que realizó el equipo local y visitante respectivamente:\n",
    "\n",
    "A partir de la siguiente consulta, se ve que el Young Boys (home_team) tiene home_team_goal = 4, y FC Basel (away team) tiene away_team_goal = 3. Es decir, si lo dicho en un principio es cierto, el resultado debería ser 4-3 a favor de Young Boys.\n",
    "\n",
    "Lo cual se valida buscando el resultado del partido en internet: https://el.soccerway.com/matches/2015/09/23/switzerland/super-league/bsc-young-boys-bern/fc-basel/2048205/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>home_player_X1</th>\n",
       "      <th>home_player_X2</th>\n",
       "      <th>home_player_X3</th>\n",
       "      <th>home_player_X4</th>\n",
       "      <th>home_player_X5</th>\n",
       "      <th>home_player_X6</th>\n",
       "      <th>home_player_X7</th>\n",
       "      <th>home_player_X8</th>\n",
       "      <th>home_player_X9</th>\n",
       "      <th>home_player_X10</th>\n",
       "      <th>home_player_X11</th>\n",
       "      <th>away_player_X1</th>\n",
       "      <th>away_player_X2</th>\n",
       "      <th>away_player_X3</th>\n",
       "      <th>away_player_X4</th>\n",
       "      <th>away_player_X5</th>\n",
       "      <th>away_player_X6</th>\n",
       "      <th>away_player_X7</th>\n",
       "      <th>away_player_X8</th>\n",
       "      <th>away_player_X9</th>\n",
       "      <th>away_player_X10</th>\n",
       "      <th>away_player_X11</th>\n",
       "      <th>home_player_Y1</th>\n",
       "      <th>home_player_Y2</th>\n",
       "      <th>home_player_Y3</th>\n",
       "      <th>home_player_Y4</th>\n",
       "      <th>home_player_Y5</th>\n",
       "      <th>home_player_Y6</th>\n",
       "      <th>home_player_Y7</th>\n",
       "      <th>home_player_Y8</th>\n",
       "      <th>home_player_Y9</th>\n",
       "      <th>home_player_Y10</th>\n",
       "      <th>home_player_Y11</th>\n",
       "      <th>away_player_Y1</th>\n",
       "      <th>away_player_Y2</th>\n",
       "      <th>away_player_Y3</th>\n",
       "      <th>away_player_Y4</th>\n",
       "      <th>away_player_Y5</th>\n",
       "      <th>away_player_Y6</th>\n",
       "      <th>away_player_Y7</th>\n",
       "      <th>away_player_Y8</th>\n",
       "      <th>away_player_Y9</th>\n",
       "      <th>away_player_Y10</th>\n",
       "      <th>away_player_Y11</th>\n",
       "      <th>home_player_1</th>\n",
       "      <th>home_player_2</th>\n",
       "      <th>home_player_3</th>\n",
       "      <th>home_player_4</th>\n",
       "      <th>home_player_5</th>\n",
       "      <th>home_player_6</th>\n",
       "      <th>home_player_7</th>\n",
       "      <th>home_player_8</th>\n",
       "      <th>home_player_9</th>\n",
       "      <th>home_player_10</th>\n",
       "      <th>home_player_11</th>\n",
       "      <th>away_player_1</th>\n",
       "      <th>away_player_2</th>\n",
       "      <th>away_player_3</th>\n",
       "      <th>away_player_4</th>\n",
       "      <th>away_player_5</th>\n",
       "      <th>away_player_6</th>\n",
       "      <th>away_player_7</th>\n",
       "      <th>away_player_8</th>\n",
       "      <th>away_player_9</th>\n",
       "      <th>away_player_10</th>\n",
       "      <th>away_player_11</th>\n",
       "      <th>goal</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotoff</th>\n",
       "      <th>foulcommit</th>\n",
       "      <th>card</th>\n",
       "      <th>cross</th>\n",
       "      <th>corner</th>\n",
       "      <th>possession</th>\n",
       "      <th>league_name</th>\n",
       "      <th>country_name</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25978</th>\n",
       "      <td>25979</td>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-09-23 00:00:00</td>\n",
       "      <td>1992095</td>\n",
       "      <td>10192</td>\n",
       "      <td>9931</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>274787.0</td>\n",
       "      <td>492132.0</td>\n",
       "      <td>108451.0</td>\n",
       "      <td>25815.0</td>\n",
       "      <td>94553.0</td>\n",
       "      <td>384376.0</td>\n",
       "      <td>598355.0</td>\n",
       "      <td>36785.0</td>\n",
       "      <td>45174.0</td>\n",
       "      <td>302079.0</td>\n",
       "      <td>71764.0</td>\n",
       "      <td>156175.0</td>\n",
       "      <td>95216.0</td>\n",
       "      <td>172768.0</td>\n",
       "      <td>22834.0</td>\n",
       "      <td>458806.0</td>\n",
       "      <td>207234.0</td>\n",
       "      <td>25772.0</td>\n",
       "      <td>40274.0</td>\n",
       "      <td>34035.0</td>\n",
       "      <td>41726.0</td>\n",
       "      <td>527103.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>BSC Young Boys</td>\n",
       "      <td>FC Basel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  country_id  league_id     season  stage                 date  \\\n",
       "25978  25979       24558      24558  2015/2016      9  2015-09-23 00:00:00   \n",
       "\n",
       "       match_api_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "25978       1992095             10192              9931               4   \n",
       "\n",
       "       away_team_goal  home_player_X1  home_player_X2  home_player_X3  \\\n",
       "25978               3             1.0             2.0             4.0   \n",
       "\n",
       "       home_player_X4  home_player_X5  home_player_X6  home_player_X7  \\\n",
       "25978             6.0             8.0             2.0             4.0   \n",
       "\n",
       "       home_player_X8  home_player_X9  home_player_X10  home_player_X11  \\\n",
       "25978             6.0             8.0              4.0              6.0   \n",
       "\n",
       "       away_player_X1  away_player_X2  away_player_X3  away_player_X4  \\\n",
       "25978             1.0             2.0             4.0             6.0   \n",
       "\n",
       "       away_player_X5  away_player_X6  away_player_X7  away_player_X8  \\\n",
       "25978             8.0             4.0             6.0             3.0   \n",
       "\n",
       "       away_player_X9  away_player_X10  away_player_X11  home_player_Y1  \\\n",
       "25978             5.0              7.0              5.0             1.0   \n",
       "\n",
       "       home_player_Y2  home_player_Y3  home_player_Y4  home_player_Y5  \\\n",
       "25978             3.0             3.0             3.0             3.0   \n",
       "\n",
       "       home_player_Y6  home_player_Y7  home_player_Y8  home_player_Y9  \\\n",
       "25978             7.0             7.0             7.0             7.0   \n",
       "\n",
       "       home_player_Y10  home_player_Y11  away_player_Y1  away_player_Y2  \\\n",
       "25978             10.0             10.0             1.0             3.0   \n",
       "\n",
       "       away_player_Y3  away_player_Y4  away_player_Y5  away_player_Y6  \\\n",
       "25978             3.0             3.0             3.0             6.0   \n",
       "\n",
       "       away_player_Y7  away_player_Y8  away_player_Y9  away_player_Y10  \\\n",
       "25978             6.0             8.0             8.0              8.0   \n",
       "\n",
       "       away_player_Y11  home_player_1  home_player_2  home_player_3  \\\n",
       "25978             11.0       274787.0       492132.0       108451.0   \n",
       "\n",
       "       home_player_4  home_player_5  home_player_6  home_player_7  \\\n",
       "25978        25815.0        94553.0       384376.0       598355.0   \n",
       "\n",
       "       home_player_8  home_player_9  home_player_10  home_player_11  \\\n",
       "25978        36785.0        45174.0        302079.0         71764.0   \n",
       "\n",
       "       away_player_1  away_player_2  away_player_3  away_player_4  \\\n",
       "25978       156175.0        95216.0       172768.0        22834.0   \n",
       "\n",
       "       away_player_5  away_player_6  away_player_7  away_player_8  \\\n",
       "25978       458806.0       207234.0        25772.0        40274.0   \n",
       "\n",
       "       away_player_9  away_player_10  away_player_11  goal shoton shotoff  \\\n",
       "25978        34035.0         41726.0        527103.0  None   None    None   \n",
       "\n",
       "      foulcommit  card cross corner possession               league_name  \\\n",
       "25978       None  None  None   None       None  Switzerland Super League   \n",
       "\n",
       "      country_name  home_team_name away_team_name  \n",
       "25978  Switzerland  BSC Young Boys       FC Basel  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Match[Match.match_api_id==1992095]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *goal*: Información de los goles.\n",
    "Para poder procesar esta columna, primero notar a través del siguiente ejemplo que corresponde a datos del tipo XML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<goal><value><comment>n</comment><stats><goals>1</goals><shoton>1</shoton></stats><event_incident_typefk>406</event_incident_typefk><elapsed>22</elapsed><player2>38807</player2><subtype>header</subtype><player1>37799</player1><sortorder>5</sortorder><team>10261</team><id>378998</id><n>295</n><type>goal</type><goal_type>n</goal_type></value><value><comment>n</comment><stats><goals>1</goals><shoton>1</shoton></stats><event_incident_typefk>393</event_incident_typefk><elapsed>24</elapsed><player2>24154</player2><subtype>shot</subtype><player1>24148</player1><sortorder>4</sortorder><team>10260</team><id>379019</id><n>298</n><type>goal</type><goal_type>n</goal_type></value></goal>'"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query(\"select * from match where goal is not null limit 1\")[\"goal\"].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Debido a lo anterior, se define la función:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>id</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>goal_type</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>player2</th>\n",
       "      <th>subtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>o</td>\n",
       "      <td>None</td>\n",
       "      <td>115</td>\n",
       "      <td>None</td>\n",
       "      <td>26</td>\n",
       "      <td>25773</td>\n",
       "      <td>2</td>\n",
       "      <td>8634</td>\n",
       "      <td>5510772</td>\n",
       "      <td>275</td>\n",
       "      <td>goal</td>\n",
       "      <td>o</td>\n",
       "      <td>2030481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n</td>\n",
       "      <td>None</td>\n",
       "      <td>393</td>\n",
       "      <td>None</td>\n",
       "      <td>45</td>\n",
       "      <td>428947</td>\n",
       "      <td>4</td>\n",
       "      <td>10267</td>\n",
       "      <td>5510897</td>\n",
       "      <td>271</td>\n",
       "      <td>goal</td>\n",
       "      <td>n</td>\n",
       "      <td>2030481</td>\n",
       "      <td>1</td>\n",
       "      <td>75307</td>\n",
       "      <td>shot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>n</td>\n",
       "      <td>None</td>\n",
       "      <td>393</td>\n",
       "      <td>None</td>\n",
       "      <td>63</td>\n",
       "      <td>30981</td>\n",
       "      <td>3</td>\n",
       "      <td>8634</td>\n",
       "      <td>5511089</td>\n",
       "      <td>389</td>\n",
       "      <td>goal</td>\n",
       "      <td>n</td>\n",
       "      <td>2030481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150739</td>\n",
       "      <td>shot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  comment stats event_incident_typefk coordinates elapsed player1 sortorder  \\\n",
       "0       o  None                   115        None      26   25773         2   \n",
       "1       n  None                   393        None      45  428947         4   \n",
       "2       n  None                   393        None      63   30981         3   \n",
       "\n",
       "    team       id    n  type goal_type  match_api_id elapsed_plus player2  \\\n",
       "0   8634  5510772  275  goal         o       2030481          NaN     NaN   \n",
       "1  10267  5510897  271  goal         n       2030481            1   75307   \n",
       "2   8634  5511089  389  goal         n       2030481          NaN  150739   \n",
       "\n",
       "  subtype  \n",
       "0     NaN  \n",
       "1    shot  \n",
       "2    shot  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "xml_to_df\n",
    "\n",
    "Recibe: xml_column: Una columna de un pandas dataframe, la cual sus elementos son un string del tipo XML\n",
    "Retorna: Un pandas dataframe, conteniendo la información de xml_column\n",
    "Se exhibe un ejemplo con el útlimo partido del FC Barcelona\n",
    "\"\"\"\n",
    "def xml_to_df(xml_column):\n",
    "    data = []\n",
    "\n",
    "    # Se itera sobre la columna\n",
    "    for i, xml_data in enumerate(xml_column):\n",
    "        # Raiz\n",
    "        root = etree.fromstring(xml_data)\n",
    "\n",
    "        # Se itera sobre cada valor\n",
    "        for value in root:\n",
    "            # Se crea un diccionario, que representa una fila\n",
    "            row = {}\n",
    "            for child in value:\n",
    "                row[child.tag] = child.text\n",
    "            row[\"match_api_id\"] = df.match_api_id.iloc[i]\n",
    "            # Se agrega a la lista de datos\n",
    "            data.append(row)\n",
    "\n",
    "    # Se crea el pandas dataframe\n",
    "    data = pd.DataFrame(data)\n",
    "    return data\n",
    "df = query(\"\"\"select * from match where goal is not null and home_team_api_id in (select team_api_id from team where team_long_name LIKE \"%Barcelona%\")\n",
    "           and home_team_goal>0 and away_team_goal>0\n",
    "order by date desc limit 1\"\"\")\n",
    "xml_to_df(df[\"goal\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede notar que existe varia información acerca de los goles. Para descubrir qué significa, se seguirán los siguientes pasos:\n",
    "\n",
    "* Se utilizará el ejemplo anterior para encontrar qué significa player2\n",
    "* Se extraerá toda la información de los goles, utilizando la función *xml_to_pdf*\n",
    "* Se buscará cuáles son los diferentes tipos de gol\n",
    "* Se buscará el partido con más tipos de goles diferentes, y se buscará en internet lo sucedido en tal partido.\n",
    "* Se realizará algo similar a lo anterior para deducir los tipos de goles restantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>home_player_X1</th>\n",
       "      <th>home_player_X2</th>\n",
       "      <th>home_player_X3</th>\n",
       "      <th>home_player_X4</th>\n",
       "      <th>home_player_X5</th>\n",
       "      <th>home_player_X6</th>\n",
       "      <th>home_player_X7</th>\n",
       "      <th>home_player_X8</th>\n",
       "      <th>home_player_X9</th>\n",
       "      <th>home_player_X10</th>\n",
       "      <th>home_player_X11</th>\n",
       "      <th>away_player_X1</th>\n",
       "      <th>away_player_X2</th>\n",
       "      <th>away_player_X3</th>\n",
       "      <th>away_player_X4</th>\n",
       "      <th>away_player_X5</th>\n",
       "      <th>away_player_X6</th>\n",
       "      <th>away_player_X7</th>\n",
       "      <th>away_player_X8</th>\n",
       "      <th>away_player_X9</th>\n",
       "      <th>away_player_X10</th>\n",
       "      <th>away_player_X11</th>\n",
       "      <th>home_player_Y1</th>\n",
       "      <th>home_player_Y2</th>\n",
       "      <th>home_player_Y3</th>\n",
       "      <th>home_player_Y4</th>\n",
       "      <th>home_player_Y5</th>\n",
       "      <th>home_player_Y6</th>\n",
       "      <th>home_player_Y7</th>\n",
       "      <th>home_player_Y8</th>\n",
       "      <th>home_player_Y9</th>\n",
       "      <th>home_player_Y10</th>\n",
       "      <th>home_player_Y11</th>\n",
       "      <th>away_player_Y1</th>\n",
       "      <th>away_player_Y2</th>\n",
       "      <th>away_player_Y3</th>\n",
       "      <th>away_player_Y4</th>\n",
       "      <th>away_player_Y5</th>\n",
       "      <th>away_player_Y6</th>\n",
       "      <th>away_player_Y7</th>\n",
       "      <th>away_player_Y8</th>\n",
       "      <th>away_player_Y9</th>\n",
       "      <th>away_player_Y10</th>\n",
       "      <th>away_player_Y11</th>\n",
       "      <th>home_player_1</th>\n",
       "      <th>home_player_2</th>\n",
       "      <th>home_player_3</th>\n",
       "      <th>home_player_4</th>\n",
       "      <th>home_player_5</th>\n",
       "      <th>home_player_6</th>\n",
       "      <th>home_player_7</th>\n",
       "      <th>home_player_8</th>\n",
       "      <th>home_player_9</th>\n",
       "      <th>home_player_10</th>\n",
       "      <th>home_player_11</th>\n",
       "      <th>away_player_1</th>\n",
       "      <th>away_player_2</th>\n",
       "      <th>away_player_3</th>\n",
       "      <th>away_player_4</th>\n",
       "      <th>away_player_5</th>\n",
       "      <th>away_player_6</th>\n",
       "      <th>away_player_7</th>\n",
       "      <th>away_player_8</th>\n",
       "      <th>away_player_9</th>\n",
       "      <th>away_player_10</th>\n",
       "      <th>away_player_11</th>\n",
       "      <th>goal</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotoff</th>\n",
       "      <th>foulcommit</th>\n",
       "      <th>card</th>\n",
       "      <th>cross</th>\n",
       "      <th>corner</th>\n",
       "      <th>possession</th>\n",
       "      <th>league_name</th>\n",
       "      <th>country_name</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24440</th>\n",
       "      <td>24441</td>\n",
       "      <td>21518</td>\n",
       "      <td>21518</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>33</td>\n",
       "      <td>2016-04-17 00:00:00</td>\n",
       "      <td>2030481</td>\n",
       "      <td>8634</td>\n",
       "      <td>10267</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>37421.0</td>\n",
       "      <td>212472.0</td>\n",
       "      <td>37482.0</td>\n",
       "      <td>38818.0</td>\n",
       "      <td>150739.0</td>\n",
       "      <td>25773.0</td>\n",
       "      <td>154257.0</td>\n",
       "      <td>30955.0</td>\n",
       "      <td>30981.0</td>\n",
       "      <td>40636.0</td>\n",
       "      <td>19533.0</td>\n",
       "      <td>96613.0</td>\n",
       "      <td>35324.0</td>\n",
       "      <td>184999.0</td>\n",
       "      <td>192770.0</td>\n",
       "      <td>40139.0</td>\n",
       "      <td>75307.0</td>\n",
       "      <td>74747.0</td>\n",
       "      <td>40148.0</td>\n",
       "      <td>428947.0</td>\n",
       "      <td>361770.0</td>\n",
       "      <td>193869.0</td>\n",
       "      <td>&lt;goal&gt;&lt;value&gt;&lt;comment&gt;o&lt;/comment&gt;&lt;stats&gt;&lt;owngo...</td>\n",
       "      <td>&lt;shoton&gt;&lt;value&gt;&lt;stats&gt;&lt;blocked&gt;1&lt;/blocked&gt;&lt;/st...</td>\n",
       "      <td>&lt;shotoff&gt;&lt;value&gt;&lt;stats&gt;&lt;shotoff&gt;1&lt;/shotoff&gt;&lt;/s...</td>\n",
       "      <td>&lt;foulcommit&gt;&lt;value&gt;&lt;stats&gt;&lt;foulscommitted&gt;1&lt;/f...</td>\n",
       "      <td>&lt;card&gt;&lt;value&gt;&lt;comment&gt;y&lt;/comment&gt;&lt;stats&gt;&lt;ycard...</td>\n",
       "      <td>&lt;cross&gt;&lt;value&gt;&lt;stats&gt;&lt;crosses&gt;1&lt;/crosses&gt;&lt;/sta...</td>\n",
       "      <td>&lt;corner&gt;&lt;value&gt;&lt;stats&gt;&lt;corners&gt;1&lt;/corners&gt;&lt;/st...</td>\n",
       "      <td>&lt;possession&gt;&lt;value&gt;&lt;comment&gt;73&lt;/comment&gt;&lt;stats...</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>Spain</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>Valencia CF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  country_id  league_id     season  stage                 date  \\\n",
       "24440  24441       21518      21518  2015/2016     33  2016-04-17 00:00:00   \n",
       "\n",
       "       match_api_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "24440       2030481              8634             10267               1   \n",
       "\n",
       "       away_team_goal  home_player_X1  home_player_X2  home_player_X3  \\\n",
       "24440               2             1.0             2.0             4.0   \n",
       "\n",
       "       home_player_X4  home_player_X5  home_player_X6  home_player_X7  \\\n",
       "24440             6.0             8.0             3.0             5.0   \n",
       "\n",
       "       home_player_X8  home_player_X9  home_player_X10  home_player_X11  \\\n",
       "24440             7.0             3.0              5.0              7.0   \n",
       "\n",
       "       away_player_X1  away_player_X2  away_player_X3  away_player_X4  \\\n",
       "24440             1.0             2.0             4.0             6.0   \n",
       "\n",
       "       away_player_X5  away_player_X6  away_player_X7  away_player_X8  \\\n",
       "24440             8.0             3.0             5.0             7.0   \n",
       "\n",
       "       away_player_X9  away_player_X10  away_player_X11  home_player_Y1  \\\n",
       "24440             4.0              6.0              5.0             1.0   \n",
       "\n",
       "       home_player_Y2  home_player_Y3  home_player_Y4  home_player_Y5  \\\n",
       "24440             3.0             3.0             3.0             3.0   \n",
       "\n",
       "       home_player_Y6  home_player_Y7  home_player_Y8  home_player_Y9  \\\n",
       "24440             7.0             7.0             7.0            10.0   \n",
       "\n",
       "       home_player_Y10  home_player_Y11  away_player_Y1  away_player_Y2  \\\n",
       "24440             10.0             10.0             1.0             3.0   \n",
       "\n",
       "       away_player_Y3  away_player_Y4  away_player_Y5  away_player_Y6  \\\n",
       "24440             3.0             3.0             3.0             6.0   \n",
       "\n",
       "       away_player_Y7  away_player_Y8  away_player_Y9  away_player_Y10  \\\n",
       "24440             6.0             6.0             8.0              8.0   \n",
       "\n",
       "       away_player_Y11  home_player_1  home_player_2  home_player_3  \\\n",
       "24440             11.0        37421.0       212472.0        37482.0   \n",
       "\n",
       "       home_player_4  home_player_5  home_player_6  home_player_7  \\\n",
       "24440        38818.0       150739.0        25773.0       154257.0   \n",
       "\n",
       "       home_player_8  home_player_9  home_player_10  home_player_11  \\\n",
       "24440        30955.0        30981.0         40636.0         19533.0   \n",
       "\n",
       "       away_player_1  away_player_2  away_player_3  away_player_4  \\\n",
       "24440        96613.0        35324.0       184999.0       192770.0   \n",
       "\n",
       "       away_player_5  away_player_6  away_player_7  away_player_8  \\\n",
       "24440        40139.0        75307.0        74747.0        40148.0   \n",
       "\n",
       "       away_player_9  away_player_10  away_player_11  \\\n",
       "24440       428947.0        361770.0        193869.0   \n",
       "\n",
       "                                                    goal  \\\n",
       "24440  <goal><value><comment>o</comment><stats><owngo...   \n",
       "\n",
       "                                                  shoton  \\\n",
       "24440  <shoton><value><stats><blocked>1</blocked></st...   \n",
       "\n",
       "                                                 shotoff  \\\n",
       "24440  <shotoff><value><stats><shotoff>1</shotoff></s...   \n",
       "\n",
       "                                              foulcommit  \\\n",
       "24440  <foulcommit><value><stats><foulscommitted>1</f...   \n",
       "\n",
       "                                                    card  \\\n",
       "24440  <card><value><comment>y</comment><stats><ycard...   \n",
       "\n",
       "                                                   cross  \\\n",
       "24440  <cross><value><stats><crosses>1</crosses></sta...   \n",
       "\n",
       "                                                  corner  \\\n",
       "24440  <corner><value><stats><corners>1</corners></st...   \n",
       "\n",
       "                                              possession      league_name  \\\n",
       "24440  <possession><value><comment>73</comment><stats...  Spain LIGA BBVA   \n",
       "\n",
       "      country_name home_team_name away_team_name  \n",
       "24440        Spain   FC Barcelona    Valencia CF  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partido del FC Barcelona\n",
    "# match_api_id obtenido de la consulta anterior\n",
    "Match[Match.match_api_id==2030481]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>birthday</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2158</td>\n",
       "      <td>75307</td>\n",
       "      <td>Daniel Parejo</td>\n",
       "      <td>189513</td>\n",
       "      <td>1989-04-16 00:00:00</td>\n",
       "      <td>180.34</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9494</td>\n",
       "      <td>428947</td>\n",
       "      <td>Santi Mina</td>\n",
       "      <td>212623</td>\n",
       "      <td>1995-12-07 00:00:00</td>\n",
       "      <td>177.80</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  player_api_id    player_name  player_fifa_api_id  \\\n",
       "0  2158          75307  Daniel Parejo              189513   \n",
       "1  9494         428947     Santi Mina              212623   \n",
       "\n",
       "              birthday  height  weight  \n",
       "0  1989-04-16 00:00:00  180.34     165  \n",
       "1  1995-12-07 00:00:00  177.80     157  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Player1 y player2 del segundo gol del partido\n",
    "query(\"select * from player where player_api_id in (75307,428947)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La información del partido se encuentra en el siguiente enlace: https://espndeportes.espn.com/futbol/comentario?juegoId=433655\n",
    "Al final del analisis se mostrará qué significa cada columna\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>player2</th>\n",
       "      <th>subtype</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>id</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>goal_type</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>del</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>n</td>\n",
       "      <td>None</td>\n",
       "      <td>406</td>\n",
       "      <td>22</td>\n",
       "      <td>38807</td>\n",
       "      <td>header</td>\n",
       "      <td>37799</td>\n",
       "      <td>5</td>\n",
       "      <td>10261</td>\n",
       "      <td>378998</td>\n",
       "      <td>295</td>\n",
       "      <td>goal</td>\n",
       "      <td>n</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n</td>\n",
       "      <td>None</td>\n",
       "      <td>393</td>\n",
       "      <td>24</td>\n",
       "      <td>24154</td>\n",
       "      <td>shot</td>\n",
       "      <td>24148</td>\n",
       "      <td>4</td>\n",
       "      <td>10260</td>\n",
       "      <td>379019</td>\n",
       "      <td>298</td>\n",
       "      <td>goal</td>\n",
       "      <td>n</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>n</td>\n",
       "      <td>None</td>\n",
       "      <td>393</td>\n",
       "      <td>4</td>\n",
       "      <td>39297</td>\n",
       "      <td>shot</td>\n",
       "      <td>26181</td>\n",
       "      <td>2</td>\n",
       "      <td>9825</td>\n",
       "      <td>375546</td>\n",
       "      <td>231</td>\n",
       "      <td>goal</td>\n",
       "      <td>n</td>\n",
       "      <td>489043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>n</td>\n",
       "      <td>None</td>\n",
       "      <td>407</td>\n",
       "      <td>83</td>\n",
       "      <td>30889</td>\n",
       "      <td>distance</td>\n",
       "      <td>30853</td>\n",
       "      <td>0</td>\n",
       "      <td>8650</td>\n",
       "      <td>378041</td>\n",
       "      <td>344</td>\n",
       "      <td>goal</td>\n",
       "      <td>n</td>\n",
       "      <td>489044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>n</td>\n",
       "      <td>None</td>\n",
       "      <td>393</td>\n",
       "      <td>4</td>\n",
       "      <td>36394</td>\n",
       "      <td>shot</td>\n",
       "      <td>23139</td>\n",
       "      <td>2</td>\n",
       "      <td>8654</td>\n",
       "      <td>376060</td>\n",
       "      <td>244</td>\n",
       "      <td>goal</td>\n",
       "      <td>n</td>\n",
       "      <td>489045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  comment stats event_incident_typefk elapsed player2   subtype player1  \\\n",
       "0       n  None                   406      22   38807    header   37799   \n",
       "1       n  None                   393      24   24154      shot   24148   \n",
       "2       n  None                   393       4   39297      shot   26181   \n",
       "3       n  None                   407      83   30889  distance   30853   \n",
       "4       n  None                   393       4   36394      shot   23139   \n",
       "\n",
       "  sortorder   team      id    n  type goal_type  match_api_id elapsed_plus  \\\n",
       "0         5  10261  378998  295  goal         n        489042          NaN   \n",
       "1         4  10260  379019  298  goal         n        489042          NaN   \n",
       "2         2   9825  375546  231  goal         n        489043          NaN   \n",
       "3         0   8650  378041  344  goal         n        489044          NaN   \n",
       "4         2   8654  376060  244  goal         n        489045          NaN   \n",
       "\n",
       "   del  coordinates  \n",
       "0  NaN          NaN  \n",
       "1  NaN          NaN  \n",
       "2  NaN          NaN  \n",
       "3  NaN          NaN  \n",
       "4  NaN          NaN  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Acá se obtiene la información de todos los goles y se guarda como la variable *data*\n",
    "df = query(\"\"\"select * from match where goal is not null\"\"\")\n",
    "data = xml_to_df(df[\"goal\"])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "goal_type\n",
       "n      34521\n",
       "p       2975\n",
       "o       1116\n",
       "npm      736\n",
       "dg       580\n",
       "rp        17\n",
       "psm        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de goles\n",
    "data.value_counts(\"goal_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "match_api_id\n",
       "2030158    5\n",
       "1024627    4\n",
       "659139     4\n",
       "1983480    4\n",
       "2060347    4\n",
       "          ..\n",
       "1474126    1\n",
       "1474125    1\n",
       "489042     1\n",
       "1474306    0\n",
       "1712583    0\n",
       "Name: goal_type, Length: 13224, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partidos con más tipos de goles diferentes\n",
    "data.groupby(\"match_api_id\")[\"goal_type\"].nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>player2</th>\n",
       "      <th>subtype</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>id</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>goal_type</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>del</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39601</th>\n",
       "      <td>o</td>\n",
       "      <td>None</td>\n",
       "      <td>115</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213653</td>\n",
       "      <td>1</td>\n",
       "      <td>9864</td>\n",
       "      <td>4916136</td>\n",
       "      <td>133</td>\n",
       "      <td>goal</td>\n",
       "      <td>o</td>\n",
       "      <td>2030158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39602</th>\n",
       "      <td>n</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>volley</td>\n",
       "      <td>361770</td>\n",
       "      <td>3</td>\n",
       "      <td>10267</td>\n",
       "      <td>4916337</td>\n",
       "      <td>215</td>\n",
       "      <td>goal</td>\n",
       "      <td>n</td>\n",
       "      <td>2030158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39603</th>\n",
       "      <td>npm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>348</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>saved_back_into_play</td>\n",
       "      <td>37450</td>\n",
       "      <td>0</td>\n",
       "      <td>9864</td>\n",
       "      <td>4916423</td>\n",
       "      <td>236</td>\n",
       "      <td>goal</td>\n",
       "      <td>npm</td>\n",
       "      <td>2030158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39604</th>\n",
       "      <td>dg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298</td>\n",
       "      <td>75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>shot</td>\n",
       "      <td>241825</td>\n",
       "      <td>1</td>\n",
       "      <td>10267</td>\n",
       "      <td>4916716</td>\n",
       "      <td>461</td>\n",
       "      <td>goal</td>\n",
       "      <td>dg</td>\n",
       "      <td>2030158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39605</th>\n",
       "      <td>p</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75307</td>\n",
       "      <td>0</td>\n",
       "      <td>10267</td>\n",
       "      <td>4916818</td>\n",
       "      <td>531</td>\n",
       "      <td>goal</td>\n",
       "      <td>p</td>\n",
       "      <td>2030158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      comment stats event_incident_typefk elapsed player2  \\\n",
       "39601       o  None                   115      19     NaN   \n",
       "39602       n  None                     6      33     NaN   \n",
       "39603     npm   NaN                   348      39     NaN   \n",
       "39604      dg   NaN                   298      75     NaN   \n",
       "39605       p  None                    20      89     NaN   \n",
       "\n",
       "                    subtype player1 sortorder   team       id    n  type  \\\n",
       "39601                   NaN  213653         1   9864  4916136  133  goal   \n",
       "39602                volley  361770         3  10267  4916337  215  goal   \n",
       "39603  saved_back_into_play   37450         0   9864  4916423  236  goal   \n",
       "39604                  shot  241825         1  10267  4916716  461  goal   \n",
       "39605                   NaN   75307         0  10267  4916818  531  goal   \n",
       "\n",
       "      goal_type  match_api_id elapsed_plus  del  coordinates  \n",
       "39601         o       2030158          NaN  NaN          NaN  \n",
       "39602         n       2030158          NaN  NaN          NaN  \n",
       "39603       npm       2030158          NaN  NaN          NaN  \n",
       "39604        dg       2030158          NaN  NaN          NaN  \n",
       "39605         p       2030158          NaN  NaN          NaN  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Goles en tal partido\n",
    "data[data.match_api_id==2030158]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>home_player_X1</th>\n",
       "      <th>home_player_X2</th>\n",
       "      <th>home_player_X3</th>\n",
       "      <th>home_player_X4</th>\n",
       "      <th>home_player_X5</th>\n",
       "      <th>home_player_X6</th>\n",
       "      <th>home_player_X7</th>\n",
       "      <th>home_player_X8</th>\n",
       "      <th>home_player_X9</th>\n",
       "      <th>home_player_X10</th>\n",
       "      <th>home_player_X11</th>\n",
       "      <th>away_player_X1</th>\n",
       "      <th>away_player_X2</th>\n",
       "      <th>away_player_X3</th>\n",
       "      <th>away_player_X4</th>\n",
       "      <th>away_player_X5</th>\n",
       "      <th>away_player_X6</th>\n",
       "      <th>away_player_X7</th>\n",
       "      <th>away_player_X8</th>\n",
       "      <th>away_player_X9</th>\n",
       "      <th>away_player_X10</th>\n",
       "      <th>away_player_X11</th>\n",
       "      <th>home_player_Y1</th>\n",
       "      <th>home_player_Y2</th>\n",
       "      <th>home_player_Y3</th>\n",
       "      <th>home_player_Y4</th>\n",
       "      <th>home_player_Y5</th>\n",
       "      <th>home_player_Y6</th>\n",
       "      <th>home_player_Y7</th>\n",
       "      <th>home_player_Y8</th>\n",
       "      <th>home_player_Y9</th>\n",
       "      <th>home_player_Y10</th>\n",
       "      <th>home_player_Y11</th>\n",
       "      <th>away_player_Y1</th>\n",
       "      <th>away_player_Y2</th>\n",
       "      <th>away_player_Y3</th>\n",
       "      <th>away_player_Y4</th>\n",
       "      <th>away_player_Y5</th>\n",
       "      <th>away_player_Y6</th>\n",
       "      <th>away_player_Y7</th>\n",
       "      <th>away_player_Y8</th>\n",
       "      <th>away_player_Y9</th>\n",
       "      <th>away_player_Y10</th>\n",
       "      <th>away_player_Y11</th>\n",
       "      <th>home_player_1</th>\n",
       "      <th>home_player_2</th>\n",
       "      <th>home_player_3</th>\n",
       "      <th>home_player_4</th>\n",
       "      <th>home_player_5</th>\n",
       "      <th>home_player_6</th>\n",
       "      <th>home_player_7</th>\n",
       "      <th>home_player_8</th>\n",
       "      <th>home_player_9</th>\n",
       "      <th>home_player_10</th>\n",
       "      <th>home_player_11</th>\n",
       "      <th>away_player_1</th>\n",
       "      <th>away_player_2</th>\n",
       "      <th>away_player_3</th>\n",
       "      <th>away_player_4</th>\n",
       "      <th>away_player_5</th>\n",
       "      <th>away_player_6</th>\n",
       "      <th>away_player_7</th>\n",
       "      <th>away_player_8</th>\n",
       "      <th>away_player_9</th>\n",
       "      <th>away_player_10</th>\n",
       "      <th>away_player_11</th>\n",
       "      <th>goal</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotoff</th>\n",
       "      <th>foulcommit</th>\n",
       "      <th>card</th>\n",
       "      <th>cross</th>\n",
       "      <th>corner</th>\n",
       "      <th>possession</th>\n",
       "      <th>league_name</th>\n",
       "      <th>country_name</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24543</th>\n",
       "      <td>24544</td>\n",
       "      <td>21518</td>\n",
       "      <td>21518</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-10-17 00:00:00</td>\n",
       "      <td>2030158</td>\n",
       "      <td>10267</td>\n",
       "      <td>9864</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>489240.0</td>\n",
       "      <td>361757.0</td>\n",
       "      <td>184999.0</td>\n",
       "      <td>361710.0</td>\n",
       "      <td>391058.0</td>\n",
       "      <td>361770.0</td>\n",
       "      <td>74747.0</td>\n",
       "      <td>428947.0</td>\n",
       "      <td>75307.0</td>\n",
       "      <td>46384.0</td>\n",
       "      <td>241825.0</td>\n",
       "      <td>25563.0</td>\n",
       "      <td>104382.0</td>\n",
       "      <td>18520.0</td>\n",
       "      <td>41707.0</td>\n",
       "      <td>41470.0</td>\n",
       "      <td>683450.0</td>\n",
       "      <td>112758.0</td>\n",
       "      <td>432591.0</td>\n",
       "      <td>37450.0</td>\n",
       "      <td>215927.0</td>\n",
       "      <td>213653.0</td>\n",
       "      <td>&lt;goal&gt;&lt;value&gt;&lt;comment&gt;o&lt;/comment&gt;&lt;stats&gt;&lt;owngo...</td>\n",
       "      <td>&lt;shoton&gt;&lt;value&gt;&lt;stats&gt;&lt;shoton&gt;1&lt;/shoton&gt;&lt;/stat...</td>\n",
       "      <td>&lt;shotoff&gt;&lt;value&gt;&lt;stats&gt;&lt;shotoff&gt;1&lt;/shotoff&gt;&lt;/s...</td>\n",
       "      <td>&lt;foulcommit&gt;&lt;value&gt;&lt;stats&gt;&lt;foulscommitted&gt;1&lt;/f...</td>\n",
       "      <td>&lt;card&gt;&lt;value&gt;&lt;comment&gt;y&lt;/comment&gt;&lt;stats&gt;&lt;ycard...</td>\n",
       "      <td>&lt;cross&gt;&lt;value&gt;&lt;stats&gt;&lt;crosses&gt;1&lt;/crosses&gt;&lt;/sta...</td>\n",
       "      <td>&lt;corner&gt;&lt;value&gt;&lt;stats&gt;&lt;corners&gt;1&lt;/corners&gt;&lt;/st...</td>\n",
       "      <td>&lt;possession&gt;&lt;value&gt;&lt;comment&gt;62&lt;/comment&gt;&lt;stats...</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Valencia CF</td>\n",
       "      <td>Málaga CF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  country_id  league_id     season  stage                 date  \\\n",
       "24543  24544       21518      21518  2015/2016      8  2015-10-17 00:00:00   \n",
       "\n",
       "       match_api_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "24543       2030158             10267              9864               3   \n",
       "\n",
       "       away_team_goal  home_player_X1  home_player_X2  home_player_X3  \\\n",
       "24543               0             1.0             2.0             4.0   \n",
       "\n",
       "       home_player_X4  home_player_X5  home_player_X6  home_player_X7  \\\n",
       "24543             6.0             8.0             4.0             6.0   \n",
       "\n",
       "       home_player_X8  home_player_X9  home_player_X10  home_player_X11  \\\n",
       "24543             3.0             5.0              7.0              5.0   \n",
       "\n",
       "       away_player_X1  away_player_X2  away_player_X3  away_player_X4  \\\n",
       "24543             1.0             2.0             4.0             6.0   \n",
       "\n",
       "       away_player_X5  away_player_X6  away_player_X7  away_player_X8  \\\n",
       "24543             8.0             4.0             6.0             3.0   \n",
       "\n",
       "       away_player_X9  away_player_X10  away_player_X11  home_player_Y1  \\\n",
       "24543             5.0              7.0              5.0             1.0   \n",
       "\n",
       "       home_player_Y2  home_player_Y3  home_player_Y4  home_player_Y5  \\\n",
       "24543             3.0             3.0             3.0             3.0   \n",
       "\n",
       "       home_player_Y6  home_player_Y7  home_player_Y8  home_player_Y9  \\\n",
       "24543             6.0             6.0             8.0             8.0   \n",
       "\n",
       "       home_player_Y10  home_player_Y11  away_player_Y1  away_player_Y2  \\\n",
       "24543              8.0             11.0             1.0             3.0   \n",
       "\n",
       "       away_player_Y3  away_player_Y4  away_player_Y5  away_player_Y6  \\\n",
       "24543             3.0             3.0             3.0             6.0   \n",
       "\n",
       "       away_player_Y7  away_player_Y8  away_player_Y9  away_player_Y10  \\\n",
       "24543             6.0             8.0             8.0              8.0   \n",
       "\n",
       "       away_player_Y11  home_player_1  home_player_2  home_player_3  \\\n",
       "24543             11.0       489240.0       361757.0       184999.0   \n",
       "\n",
       "       home_player_4  home_player_5  home_player_6  home_player_7  \\\n",
       "24543       361710.0       391058.0       361770.0        74747.0   \n",
       "\n",
       "       home_player_8  home_player_9  home_player_10  home_player_11  \\\n",
       "24543       428947.0        75307.0         46384.0        241825.0   \n",
       "\n",
       "       away_player_1  away_player_2  away_player_3  away_player_4  \\\n",
       "24543        25563.0       104382.0        18520.0        41707.0   \n",
       "\n",
       "       away_player_5  away_player_6  away_player_7  away_player_8  \\\n",
       "24543        41470.0       683450.0       112758.0       432591.0   \n",
       "\n",
       "       away_player_9  away_player_10  away_player_11  \\\n",
       "24543        37450.0        215927.0        213653.0   \n",
       "\n",
       "                                                    goal  \\\n",
       "24543  <goal><value><comment>o</comment><stats><owngo...   \n",
       "\n",
       "                                                  shoton  \\\n",
       "24543  <shoton><value><stats><shoton>1</shoton></stat...   \n",
       "\n",
       "                                                 shotoff  \\\n",
       "24543  <shotoff><value><stats><shotoff>1</shotoff></s...   \n",
       "\n",
       "                                              foulcommit  \\\n",
       "24543  <foulcommit><value><stats><foulscommitted>1</f...   \n",
       "\n",
       "                                                    card  \\\n",
       "24543  <card><value><comment>y</comment><stats><ycard...   \n",
       "\n",
       "                                                   cross  \\\n",
       "24543  <cross><value><stats><crosses>1</crosses></sta...   \n",
       "\n",
       "                                                  corner  \\\n",
       "24543  <corner><value><stats><corners>1</corners></st...   \n",
       "\n",
       "                                              possession      league_name  \\\n",
       "24543  <possession><value><comment>62</comment><stats...  Spain LIGA BBVA   \n",
       "\n",
       "      country_name home_team_name away_team_name  \n",
       "24543        Spain    Valencia CF      Málaga CF  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corresponde al partido de valencia vs Malaga el 2015-10\n",
    "Match[Match.match_api_id==2030158]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>player2</th>\n",
       "      <th>subtype</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>id</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>goal_type</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>del</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>rp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>486</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8455</td>\n",
       "      <td>891598</td>\n",
       "      <td>147</td>\n",
       "      <td>goal</td>\n",
       "      <td>rp</td>\n",
       "      <td>658842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     comment stats event_incident_typefk elapsed player2 subtype player1  \\\n",
       "1321      rp   NaN                   486      59     NaN     NaN     NaN   \n",
       "\n",
       "     sortorder  team      id    n  type goal_type  match_api_id elapsed_plus  \\\n",
       "1321         1  8455  891598  147  goal        rp        658842          NaN   \n",
       "\n",
       "      del  coordinates  \n",
       "1321  NaN          NaN  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gol rp en un partido \n",
    "data[(data.goal_type==\"rp\")].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
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       "      <th>home_player_X4</th>\n",
       "      <th>home_player_X5</th>\n",
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       "      <th>away_player_X6</th>\n",
       "      <th>away_player_X7</th>\n",
       "      <th>away_player_X8</th>\n",
       "      <th>away_player_X9</th>\n",
       "      <th>away_player_X10</th>\n",
       "      <th>away_player_X11</th>\n",
       "      <th>home_player_Y1</th>\n",
       "      <th>home_player_Y2</th>\n",
       "      <th>home_player_Y3</th>\n",
       "      <th>home_player_Y4</th>\n",
       "      <th>home_player_Y5</th>\n",
       "      <th>home_player_Y6</th>\n",
       "      <th>home_player_Y7</th>\n",
       "      <th>home_player_Y8</th>\n",
       "      <th>home_player_Y9</th>\n",
       "      <th>home_player_Y10</th>\n",
       "      <th>home_player_Y11</th>\n",
       "      <th>away_player_Y1</th>\n",
       "      <th>away_player_Y2</th>\n",
       "      <th>away_player_Y3</th>\n",
       "      <th>away_player_Y4</th>\n",
       "      <th>away_player_Y5</th>\n",
       "      <th>away_player_Y6</th>\n",
       "      <th>away_player_Y7</th>\n",
       "      <th>away_player_Y8</th>\n",
       "      <th>away_player_Y9</th>\n",
       "      <th>away_player_Y10</th>\n",
       "      <th>away_player_Y11</th>\n",
       "      <th>home_player_1</th>\n",
       "      <th>home_player_2</th>\n",
       "      <th>home_player_3</th>\n",
       "      <th>home_player_4</th>\n",
       "      <th>home_player_5</th>\n",
       "      <th>home_player_6</th>\n",
       "      <th>home_player_7</th>\n",
       "      <th>home_player_8</th>\n",
       "      <th>home_player_9</th>\n",
       "      <th>home_player_10</th>\n",
       "      <th>home_player_11</th>\n",
       "      <th>away_player_1</th>\n",
       "      <th>away_player_2</th>\n",
       "      <th>away_player_3</th>\n",
       "      <th>away_player_4</th>\n",
       "      <th>away_player_5</th>\n",
       "      <th>away_player_6</th>\n",
       "      <th>away_player_7</th>\n",
       "      <th>away_player_8</th>\n",
       "      <th>away_player_9</th>\n",
       "      <th>away_player_10</th>\n",
       "      <th>away_player_11</th>\n",
       "      <th>goal</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotoff</th>\n",
       "      <th>foulcommit</th>\n",
       "      <th>card</th>\n",
       "      <th>cross</th>\n",
       "      <th>corner</th>\n",
       "      <th>possession</th>\n",
       "      <th>league_name</th>\n",
       "      <th>country_name</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>2202</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>18</td>\n",
       "      <td>2009-12-20 00:00:00</td>\n",
       "      <td>658842</td>\n",
       "      <td>8654</td>\n",
       "      <td>8455</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>36374.0</td>\n",
       "      <td>36394.0</td>\n",
       "      <td>24744.0</td>\n",
       "      <td>23818.0</td>\n",
       "      <td>26348.0</td>\n",
       "      <td>37169.0</td>\n",
       "      <td>24223.0</td>\n",
       "      <td>34590.0</td>\n",
       "      <td>109898.0</td>\n",
       "      <td>41475.0</td>\n",
       "      <td>31020.0</td>\n",
       "      <td>30859.0</td>\n",
       "      <td>31306.0</td>\n",
       "      <td>30627.0</td>\n",
       "      <td>30911.0</td>\n",
       "      <td>38834.0</td>\n",
       "      <td>30699.0</td>\n",
       "      <td>30631.0</td>\n",
       "      <td>30679.0</td>\n",
       "      <td>30630.0</td>\n",
       "      <td>30822.0</td>\n",
       "      <td>31906.0</td>\n",
       "      <td>&lt;goal&gt;&lt;value&gt;&lt;comment&gt;p&lt;/comment&gt;&lt;stats&gt;&lt;penal...</td>\n",
       "      <td>&lt;shoton&gt;&lt;value&gt;&lt;stats&gt;&lt;shoton&gt;1&lt;/shoton&gt;&lt;/stat...</td>\n",
       "      <td>&lt;shotoff&gt;&lt;value&gt;&lt;stats&gt;&lt;shotoff&gt;1&lt;/shotoff&gt;&lt;/s...</td>\n",
       "      <td>&lt;foulcommit&gt;&lt;value&gt;&lt;stats&gt;&lt;foulscommitted&gt;1&lt;/f...</td>\n",
       "      <td>&lt;card&gt;&lt;value&gt;&lt;comment&gt;y&lt;/comment&gt;&lt;stats&gt;&lt;ycard...</td>\n",
       "      <td>&lt;cross&gt;&lt;value&gt;&lt;stats&gt;&lt;corners&gt;1&lt;/corners&gt;&lt;/sta...</td>\n",
       "      <td>&lt;corner&gt;&lt;value&gt;&lt;stats&gt;&lt;corners&gt;1&lt;/corners&gt;&lt;/st...</td>\n",
       "      <td>&lt;possession&gt;&lt;value&gt;&lt;comment&gt;42&lt;/comment&gt;&lt;event...</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>West Ham United</td>\n",
       "      <td>Chelsea</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        id  country_id  league_id     season  stage                 date  \\\n",
       "2201  2202        1729       1729  2009/2010     18  2009-12-20 00:00:00   \n",
       "\n",
       "      match_api_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "2201        658842              8654              8455               1   \n",
       "\n",
       "      away_team_goal  home_player_X1  home_player_X2  home_player_X3  \\\n",
       "2201               1             1.0             2.0             4.0   \n",
       "\n",
       "      home_player_X4  home_player_X5  home_player_X6  home_player_X7  \\\n",
       "2201             6.0             8.0             3.0             5.0   \n",
       "\n",
       "      home_player_X8  home_player_X9  home_player_X10  home_player_X11  \\\n",
       "2201             7.0             4.0              6.0              5.0   \n",
       "\n",
       "      away_player_X1  away_player_X2  away_player_X3  away_player_X4  \\\n",
       "2201             1.0             2.0             4.0             6.0   \n",
       "\n",
       "      away_player_X5  away_player_X6  away_player_X7  away_player_X8  \\\n",
       "2201             8.0             5.0             4.0             6.0   \n",
       "\n",
       "      away_player_X9  away_player_X10  away_player_X11  home_player_Y1  \\\n",
       "2201             5.0              4.0              6.0             1.0   \n",
       "\n",
       "      home_player_Y2  home_player_Y3  home_player_Y4  home_player_Y5  \\\n",
       "2201             3.0             3.0             3.0             3.0   \n",
       "\n",
       "      home_player_Y6  home_player_Y7  home_player_Y8  home_player_Y9  \\\n",
       "2201             6.0             6.0             6.0             8.0   \n",
       "\n",
       "      home_player_Y10  home_player_Y11  away_player_Y1  away_player_Y2  \\\n",
       "2201              8.0             11.0             1.0             3.0   \n",
       "\n",
       "      away_player_Y3  away_player_Y4  away_player_Y5  away_player_Y6  \\\n",
       "2201             3.0             3.0             3.0             5.0   \n",
       "\n",
       "      away_player_Y7  away_player_Y8  away_player_Y9  away_player_Y10  \\\n",
       "2201             7.0             7.0             9.0             11.0   \n",
       "\n",
       "      away_player_Y11  home_player_1  home_player_2  home_player_3  \\\n",
       "2201             11.0        36374.0        36394.0        24744.0   \n",
       "\n",
       "      home_player_4  home_player_5  home_player_6  home_player_7  \\\n",
       "2201        23818.0        26348.0        37169.0        24223.0   \n",
       "\n",
       "      home_player_8  home_player_9  home_player_10  home_player_11  \\\n",
       "2201        34590.0       109898.0         41475.0         31020.0   \n",
       "\n",
       "      away_player_1  away_player_2  away_player_3  away_player_4  \\\n",
       "2201        30859.0        31306.0        30627.0        30911.0   \n",
       "\n",
       "      away_player_5  away_player_6  away_player_7  away_player_8  \\\n",
       "2201        38834.0        30699.0        30631.0        30679.0   \n",
       "\n",
       "      away_player_9  away_player_10  away_player_11  \\\n",
       "2201        30630.0         30822.0         31906.0   \n",
       "\n",
       "                                                   goal  \\\n",
       "2201  <goal><value><comment>p</comment><stats><penal...   \n",
       "\n",
       "                                                 shoton  \\\n",
       "2201  <shoton><value><stats><shoton>1</shoton></stat...   \n",
       "\n",
       "                                                shotoff  \\\n",
       "2201  <shotoff><value><stats><shotoff>1</shotoff></s...   \n",
       "\n",
       "                                             foulcommit  \\\n",
       "2201  <foulcommit><value><stats><foulscommitted>1</f...   \n",
       "\n",
       "                                                   card  \\\n",
       "2201  <card><value><comment>y</comment><stats><ycard...   \n",
       "\n",
       "                                                  cross  \\\n",
       "2201  <cross><value><stats><corners>1</corners></sta...   \n",
       "\n",
       "                                                 corner  \\\n",
       "2201  <corner><value><stats><corners>1</corners></st...   \n",
       "\n",
       "                                             possession  \\\n",
       "2201  <possession><value><comment>42</comment><event...   \n",
       "\n",
       "                 league_name country_name   home_team_name away_team_name  \n",
       "2201  England Premier League      England  West Ham United        Chelsea  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# West Ham vs Chelsea\n",
    "Match[Match.match_api_id==658842]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el siguiente enlace https://www.espn.com.co/futbol/comentario?juegoId=433904 aparece la información del partido del Valencia minuto a minuto, y en https://www.espn.com.uy/futbol/comentario?juegoId=270024 el partido del Chelsea, por lo que se puede concluir que:\n",
    "* elapsed: Minuto del gol\n",
    "* player1: id del jugador que realizó el gol.\n",
    "* player2: id del jugador que realizó la asistencia del gol.\n",
    "* \"o\": Auto gol.\n",
    "* \"n\": Gol.\n",
    "* \"npm\": Penal fallado.\n",
    "* \"dg\": Offside.\n",
    "* \"p\": Gol de penal.\n",
    "* \"rp\": Gol de penal (pegando en el palo primero)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    comment stats event_incident_typefk elapsed player2 subtype player1  \\\n",
       "285     psm   NaN                   181      45     NaN     NaN   30348   \n",
       "\n",
       "    sortorder  team      id    n  type goal_type  match_api_id elapsed_plus  \\\n",
       "285         0  8462  538898  116  goal       psm        489229            2   \n",
       "\n",
       "     del  coordinates  \n",
       "285  NaN          NaN  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gol del tipo psm\n",
    "data[(data.goal_type==\"psm\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>home_player_X1</th>\n",
       "      <th>home_player_X2</th>\n",
       "      <th>home_player_X3</th>\n",
       "      <th>home_player_X4</th>\n",
       "      <th>home_player_X5</th>\n",
       "      <th>home_player_X6</th>\n",
       "      <th>home_player_X7</th>\n",
       "      <th>home_player_X8</th>\n",
       "      <th>home_player_X9</th>\n",
       "      <th>home_player_X10</th>\n",
       "      <th>home_player_X11</th>\n",
       "      <th>away_player_X1</th>\n",
       "      <th>away_player_X2</th>\n",
       "      <th>away_player_X3</th>\n",
       "      <th>away_player_X4</th>\n",
       "      <th>away_player_X5</th>\n",
       "      <th>away_player_X6</th>\n",
       "      <th>away_player_X7</th>\n",
       "      <th>away_player_X8</th>\n",
       "      <th>away_player_X9</th>\n",
       "      <th>away_player_X10</th>\n",
       "      <th>away_player_X11</th>\n",
       "      <th>home_player_Y1</th>\n",
       "      <th>home_player_Y2</th>\n",
       "      <th>home_player_Y3</th>\n",
       "      <th>home_player_Y4</th>\n",
       "      <th>home_player_Y5</th>\n",
       "      <th>home_player_Y6</th>\n",
       "      <th>home_player_Y7</th>\n",
       "      <th>home_player_Y8</th>\n",
       "      <th>home_player_Y9</th>\n",
       "      <th>home_player_Y10</th>\n",
       "      <th>home_player_Y11</th>\n",
       "      <th>away_player_Y1</th>\n",
       "      <th>away_player_Y2</th>\n",
       "      <th>away_player_Y3</th>\n",
       "      <th>away_player_Y4</th>\n",
       "      <th>away_player_Y5</th>\n",
       "      <th>away_player_Y6</th>\n",
       "      <th>away_player_Y7</th>\n",
       "      <th>away_player_Y8</th>\n",
       "      <th>away_player_Y9</th>\n",
       "      <th>away_player_Y10</th>\n",
       "      <th>away_player_Y11</th>\n",
       "      <th>home_player_1</th>\n",
       "      <th>home_player_2</th>\n",
       "      <th>home_player_3</th>\n",
       "      <th>home_player_4</th>\n",
       "      <th>home_player_5</th>\n",
       "      <th>home_player_6</th>\n",
       "      <th>home_player_7</th>\n",
       "      <th>home_player_8</th>\n",
       "      <th>home_player_9</th>\n",
       "      <th>home_player_10</th>\n",
       "      <th>home_player_11</th>\n",
       "      <th>away_player_1</th>\n",
       "      <th>away_player_2</th>\n",
       "      <th>away_player_3</th>\n",
       "      <th>away_player_4</th>\n",
       "      <th>away_player_5</th>\n",
       "      <th>away_player_6</th>\n",
       "      <th>away_player_7</th>\n",
       "      <th>away_player_8</th>\n",
       "      <th>away_player_9</th>\n",
       "      <th>away_player_10</th>\n",
       "      <th>away_player_11</th>\n",
       "      <th>goal</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotoff</th>\n",
       "      <th>foulcommit</th>\n",
       "      <th>card</th>\n",
       "      <th>cross</th>\n",
       "      <th>corner</th>\n",
       "      <th>possession</th>\n",
       "      <th>league_name</th>\n",
       "      <th>country_name</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>1835</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>19</td>\n",
       "      <td>2008-12-26 00:00:00</td>\n",
       "      <td>489229</td>\n",
       "      <td>8462</td>\n",
       "      <td>8654</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36286.0</td>\n",
       "      <td>47418.0</td>\n",
       "      <td>34418.0</td>\n",
       "      <td>24216.0</td>\n",
       "      <td>40987.0</td>\n",
       "      <td>23285.0</td>\n",
       "      <td>24653.0</td>\n",
       "      <td>24373.0</td>\n",
       "      <td>38820.0</td>\n",
       "      <td>30830.0</td>\n",
       "      <td>30348.0</td>\n",
       "      <td>36374.0</td>\n",
       "      <td>30687.0</td>\n",
       "      <td>23818.0</td>\n",
       "      <td>37277.0</td>\n",
       "      <td>26348.0</td>\n",
       "      <td>109898.0</td>\n",
       "      <td>37169.0</td>\n",
       "      <td>24223.0</td>\n",
       "      <td>30966.0</td>\n",
       "      <td>33781.0</td>\n",
       "      <td>34543.0</td>\n",
       "      <td>&lt;goal&gt;&lt;value&gt;&lt;comment&gt;n&lt;/comment&gt;&lt;stats&gt;&lt;goals...</td>\n",
       "      <td>&lt;shoton&gt;&lt;value&gt;&lt;stats&gt;&lt;blocked&gt;1&lt;/blocked&gt;&lt;/st...</td>\n",
       "      <td>&lt;shotoff&gt;&lt;value&gt;&lt;stats&gt;&lt;shotoff&gt;1&lt;/shotoff&gt;&lt;/s...</td>\n",
       "      <td>&lt;foulcommit&gt;&lt;value&gt;&lt;stats&gt;&lt;foulscommitted&gt;1&lt;/f...</td>\n",
       "      <td>&lt;card&gt;&lt;value&gt;&lt;comment&gt;y&lt;/comment&gt;&lt;stats&gt;&lt;ycard...</td>\n",
       "      <td>&lt;cross&gt;&lt;value&gt;&lt;stats&gt;&lt;crosses&gt;1&lt;/crosses&gt;&lt;/sta...</td>\n",
       "      <td>&lt;corner&gt;&lt;value&gt;&lt;stats&gt;&lt;corners&gt;1&lt;/corners&gt;&lt;/st...</td>\n",
       "      <td>&lt;possession&gt;&lt;value&gt;&lt;comment&gt;51&lt;/comment&gt;&lt;event...</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>Portsmouth</td>\n",
       "      <td>West Ham United</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  country_id  league_id     season  stage                 date  \\\n",
       "1834  1835        1729       1729  2008/2009     19  2008-12-26 00:00:00   \n",
       "\n",
       "      match_api_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "1834        489229              8462              8654               1   \n",
       "\n",
       "      away_team_goal  home_player_X1  home_player_X2  home_player_X3  \\\n",
       "1834               4             1.0             2.0             4.0   \n",
       "\n",
       "      home_player_X4  home_player_X5  home_player_X6  home_player_X7  \\\n",
       "1834             6.0             8.0             2.0             4.0   \n",
       "\n",
       "      home_player_X8  home_player_X9  home_player_X10  home_player_X11  \\\n",
       "1834             6.0             8.0              4.0              6.0   \n",
       "\n",
       "      away_player_X1  away_player_X2  away_player_X3  away_player_X4  \\\n",
       "1834             1.0             2.0             4.0             6.0   \n",
       "\n",
       "      away_player_X5  away_player_X6  away_player_X7  away_player_X8  \\\n",
       "1834             8.0             2.0             4.0             6.0   \n",
       "\n",
       "      away_player_X9  away_player_X10  away_player_X11  home_player_Y1  \\\n",
       "1834             8.0              4.0              6.0             1.0   \n",
       "\n",
       "      home_player_Y2  home_player_Y3  home_player_Y4  home_player_Y5  \\\n",
       "1834             3.0             3.0             3.0             3.0   \n",
       "\n",
       "      home_player_Y6  home_player_Y7  home_player_Y8  home_player_Y9  \\\n",
       "1834             7.0             7.0             7.0             7.0   \n",
       "\n",
       "      home_player_Y10  home_player_Y11  away_player_Y1  away_player_Y2  \\\n",
       "1834             10.0             10.0             1.0             3.0   \n",
       "\n",
       "      away_player_Y3  away_player_Y4  away_player_Y5  away_player_Y6  \\\n",
       "1834             3.0             3.0             3.0             7.0   \n",
       "\n",
       "      away_player_Y7  away_player_Y8  away_player_Y9  away_player_Y10  \\\n",
       "1834             7.0             7.0             7.0             10.0   \n",
       "\n",
       "      away_player_Y11  home_player_1  home_player_2  home_player_3  \\\n",
       "1834             10.0        36286.0        47418.0        34418.0   \n",
       "\n",
       "      home_player_4  home_player_5  home_player_6  home_player_7  \\\n",
       "1834        24216.0        40987.0        23285.0        24653.0   \n",
       "\n",
       "      home_player_8  home_player_9  home_player_10  home_player_11  \\\n",
       "1834        24373.0        38820.0         30830.0         30348.0   \n",
       "\n",
       "      away_player_1  away_player_2  away_player_3  away_player_4  \\\n",
       "1834        36374.0        30687.0        23818.0        37277.0   \n",
       "\n",
       "      away_player_5  away_player_6  away_player_7  away_player_8  \\\n",
       "1834        26348.0       109898.0        37169.0        24223.0   \n",
       "\n",
       "      away_player_9  away_player_10  away_player_11  \\\n",
       "1834        30966.0         33781.0         34543.0   \n",
       "\n",
       "                                                   goal  \\\n",
       "1834  <goal><value><comment>n</comment><stats><goals...   \n",
       "\n",
       "                                                 shoton  \\\n",
       "1834  <shoton><value><stats><blocked>1</blocked></st...   \n",
       "\n",
       "                                                shotoff  \\\n",
       "1834  <shotoff><value><stats><shotoff>1</shotoff></s...   \n",
       "\n",
       "                                             foulcommit  \\\n",
       "1834  <foulcommit><value><stats><foulscommitted>1</f...   \n",
       "\n",
       "                                                   card  \\\n",
       "1834  <card><value><comment>y</comment><stats><ycard...   \n",
       "\n",
       "                                                  cross  \\\n",
       "1834  <cross><value><stats><crosses>1</crosses></sta...   \n",
       "\n",
       "                                                 corner  \\\n",
       "1834  <corner><value><stats><corners>1</corners></st...   \n",
       "\n",
       "                                             possession  \\\n",
       "1834  <possession><value><comment>51</comment><event...   \n",
       "\n",
       "                 league_name country_name home_team_name   away_team_name  \n",
       "1834  England Premier League      England     Portsmouth  West Ham United  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Match[Match.match_api_id==489229]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con respecto al único partido donde hubo un gol del tipo \"psm\" no fue posible encontrar información, por lo que no se considerará en análisis posteriores, no implica un costo, pues solo existe un registro con ese tipo de gol."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### shoton: Información de los tiros al arco\n",
    "Se utilizará la función definida en la subsección anterior (xml_to_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>subtype</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
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       "      <th>goal_type</th>\n",
       "      <th>del</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>card_type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>blocked_shot</td>\n",
       "      <td>24154</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>154</td>\n",
       "      <td>7</td>\n",
       "      <td>header</td>\n",
       "      <td>24157</td>\n",
       "      <td>2</td>\n",
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       "      <td>258</td>\n",
       "      <td>shoton</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>153</td>\n",
       "      <td>14</td>\n",
       "      <td>shot</td>\n",
       "      <td>30829</td>\n",
       "      <td>1</td>\n",
       "      <td>10260</td>\n",
       "      <td>274</td>\n",
       "      <td>shoton</td>\n",
       "      <td>378922</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>153</td>\n",
       "      <td>14</td>\n",
       "      <td>shot</td>\n",
       "      <td>30373</td>\n",
       "      <td>2</td>\n",
       "      <td>10260</td>\n",
       "      <td>279</td>\n",
       "      <td>shoton</td>\n",
       "      <td>378923</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>137</td>\n",
       "      <td>17</td>\n",
       "      <td>distance</td>\n",
       "      <td>30373</td>\n",
       "      <td>3</td>\n",
       "      <td>10260</td>\n",
       "      <td>272</td>\n",
       "      <td>shoton</td>\n",
       "      <td>378951</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stats event_incident_typefk elapsed       subtype player1 sortorder   team  \\\n",
       "0  None                    61       3  blocked_shot   24154         0  10260   \n",
       "1  None                   154       7        header   24157         2  10260   \n",
       "2  None                   153      14          shot   30829         1  10260   \n",
       "3  None                   153      14          shot   30373         2  10260   \n",
       "4  None                   137      17      distance   30373         3  10260   \n",
       "\n",
       "     n    type      id  match_api_id elapsed_plus goal_type  del  coordinates  \\\n",
       "0  253  shoton  378828        489042          NaN       NaN  NaN          NaN   \n",
       "1  258  shoton  378866        489042          NaN       NaN  NaN          NaN   \n",
       "2  274  shoton  378922        489042          NaN       NaN  NaN          NaN   \n",
       "3  279  shoton  378923        489042          NaN       NaN  NaN          NaN   \n",
       "4  272  shoton  378951        489042          NaN       NaN  NaN          NaN   \n",
       "\n",
       "  card_type  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  "
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Acá se obtiene la información de todos los tiros al arco\n",
    "df = query(\"\"\"select * from match where shoton is not null\"\"\")\n",
    "data = xml_to_df(df[\"shoton\"])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subtype           \n",
       "blocked_shot          31635\n",
       "shot                  23048\n",
       "distance              12961\n",
       "header                 8097\n",
       "big chance shot        5174\n",
       "deflected              4005\n",
       "direct_freekick        3422\n",
       "volley                 1596\n",
       "big chance header      1379\n",
       "blocked_header          962\n",
       "big chance blocked      538\n",
       "big chance volley       299\n",
       "bicycle_kick            296\n",
       "indirect freekick       160\n",
       "lob                     154\n",
       "big chance bicycle       11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de tiro al arco\n",
    "data.value_counts([\"subtype\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso el tipo de tiro se explica por si solo, y siguiendo la misma idea de la columna *goal* se concluye que player1 corresponde al id del jugador que realizó tal tiro."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### shotoff: Información de los tiros fuera del arco\n",
    "Se realiza lo analogo a lo realizado para la columna shoton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>subtype</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>id</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>del</th>\n",
       "      <th>card_type</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>distance</td>\n",
       "      <td>30373</td>\n",
       "      <td>1</td>\n",
       "      <td>10260</td>\n",
       "      <td>264</td>\n",
       "      <td>shotoff</td>\n",
       "      <td>378835</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>37799</td>\n",
       "      <td>2</td>\n",
       "      <td>10261</td>\n",
       "      <td>257</td>\n",
       "      <td>shotoff</td>\n",
       "      <td>378845</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>317</td>\n",
       "      <td>22</td>\n",
       "      <td>deflected</td>\n",
       "      <td>24228</td>\n",
       "      <td>3</td>\n",
       "      <td>10261</td>\n",
       "      <td>269</td>\n",
       "      <td>shotoff</td>\n",
       "      <td>378995</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>distance</td>\n",
       "      <td>38807</td>\n",
       "      <td>0</td>\n",
       "      <td>10261</td>\n",
       "      <td>278</td>\n",
       "      <td>shotoff</td>\n",
       "      <td>379075</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>47</td>\n",
       "      <td>34</td>\n",
       "      <td>header</td>\n",
       "      <td>24154</td>\n",
       "      <td>2</td>\n",
       "      <td>10260</td>\n",
       "      <td>281</td>\n",
       "      <td>shotoff</td>\n",
       "      <td>379111</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stats event_incident_typefk elapsed    subtype player1 sortorder   team  \\\n",
       "0  None                     9       4   distance   30373         1  10260   \n",
       "1  None                     9       5   distance   37799         2  10261   \n",
       "2  None                   317      22  deflected   24228         3  10261   \n",
       "3  None                     9      31   distance   38807         0  10261   \n",
       "4  None                    47      34     header   24154         2  10260   \n",
       "\n",
       "     n     type      id  match_api_id elapsed_plus  del card_type  coordinates  \n",
       "0  264  shotoff  378835        489042          NaN  NaN       NaN          NaN  \n",
       "1  257  shotoff  378845        489042          NaN  NaN       NaN          NaN  \n",
       "2  269  shotoff  378995        489042          NaN  NaN       NaN          NaN  \n",
       "3  278  shotoff  379075        489042          NaN  NaN       NaN          NaN  \n",
       "4  281  shotoff  379111        489042          NaN  NaN       NaN          NaN  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Acá se obtiene la información de todos los tiros fuera arco\n",
    "df = query(\"\"\"select * from match where shotoff is not null\"\"\")\n",
    "data = xml_to_df(df[\"shotoff\"])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subtype            \n",
       "distance               23110\n",
       "shot                   22314\n",
       "header                 15744\n",
       "deflected               8033\n",
       "direct_freekick         4240\n",
       "big chance shot         4234\n",
       "bad shot                4184\n",
       "volley                  4117\n",
       "post                    1946\n",
       "crossbar                1935\n",
       "big chance header       1481\n",
       "miss_kick               1193\n",
       "bicycle_kick             656\n",
       "big chance volley        575\n",
       "big chance post          547\n",
       "big chance crossbar      380\n",
       "lob                      255\n",
       "indirect freekick        146\n",
       "big chance bicycle        24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de tiro fuera del arco\n",
    "data.value_counts([\"subtype\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De igual manera  el tipo de tiro se explica por si solo, y siguiendo la misma idea de la columna *goal* se concluye que player1 corresponde al id del jugador que realizó tal tiro."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### foulcommit: Información de las faltas cometidas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>player2</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>id</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>subtype</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>card_type</th>\n",
       "      <th>del</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>venue</th>\n",
       "      <th>injury_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>32569</td>\n",
       "      <td>25518</td>\n",
       "      <td>1</td>\n",
       "      <td>10261</td>\n",
       "      <td>267</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>378824</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>24157</td>\n",
       "      <td>30929</td>\n",
       "      <td>0</td>\n",
       "      <td>10261</td>\n",
       "      <td>277</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>378826</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>24148</td>\n",
       "      <td>29581</td>\n",
       "      <td>1</td>\n",
       "      <td>10261</td>\n",
       "      <td>254</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>378830</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>40565</td>\n",
       "      <td>30373</td>\n",
       "      <td>0</td>\n",
       "      <td>10260</td>\n",
       "      <td>256</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>378841</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>320</td>\n",
       "      <td>10</td>\n",
       "      <td>30829</td>\n",
       "      <td>29581</td>\n",
       "      <td>0</td>\n",
       "      <td>10261</td>\n",
       "      <td>275</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>378894</td>\n",
       "      <td>489042</td>\n",
       "      <td>pushing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stats event_incident_typefk elapsed player2 player1 sortorder   team    n  \\\n",
       "0  None                    37       1   32569   25518         1  10261  267   \n",
       "1  None                    37       2   24157   30929         0  10261  277   \n",
       "2  None                    37       3   24148   29581         1  10261  254   \n",
       "3  None                    37       5   40565   30373         0  10260  256   \n",
       "4  None                   320      10   30829   29581         0  10261  275   \n",
       "\n",
       "         type      id  match_api_id  subtype elapsed_plus card_type  del  \\\n",
       "0  foulcommit  378824        489042      NaN          NaN       NaN  NaN   \n",
       "1  foulcommit  378826        489042      NaN          NaN       NaN  NaN   \n",
       "2  foulcommit  378830        489042      NaN          NaN       NaN  NaN   \n",
       "3  foulcommit  378841        489042      NaN          NaN       NaN  NaN   \n",
       "4  foulcommit  378894        489042  pushing          NaN       NaN  NaN   \n",
       "\n",
       "   coordinates venue injury_time  \n",
       "0          NaN   NaN         NaN  \n",
       "1          NaN   NaN         NaN  \n",
       "2          NaN   NaN         NaN  \n",
       "3          NaN   NaN         NaN  \n",
       "4          NaN   NaN         NaN  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Acá se obtiene la información de todos los tiros fuera arco\n",
    "df = query(\"\"\"select * from match where foulcommit is not null\"\"\")\n",
    "data = xml_to_df(df[\"foulcommit\"])\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "card_type\n",
       "y            13\n",
       "r             1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de tarjetas\n",
    "data.value_counts([\"card_type\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se buscará in ejemplo de falta cometida con card_type = y, para ver si efectivamente corresponde a una tarjeta amarilla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
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       "      <th>player2</th>\n",
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       "      <th>id</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>subtype</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>card_type</th>\n",
       "      <th>del</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>venue</th>\n",
       "      <th>injury_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6779</th>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>61</td>\n",
       "      <td>22764</td>\n",
       "      <td>23034</td>\n",
       "      <td>0</td>\n",
       "      <td>8667</td>\n",
       "      <td>349</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>657558</td>\n",
       "      <td>489382</td>\n",
       "      <td>serious_foul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25883</th>\n",
       "      <td>None</td>\n",
       "      <td>43</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38994</td>\n",
       "      <td>3</td>\n",
       "      <td>10260</td>\n",
       "      <td>357</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>1202404</td>\n",
       "      <td>839875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26526</th>\n",
       "      <td>None</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>29581</td>\n",
       "      <td>30338</td>\n",
       "      <td>0</td>\n",
       "      <td>9879</td>\n",
       "      <td>235</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>1603837</td>\n",
       "      <td>1024173</td>\n",
       "      <td>from_behind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28085</th>\n",
       "      <td>None</td>\n",
       "      <td>19</td>\n",
       "      <td>39</td>\n",
       "      <td>24011</td>\n",
       "      <td>23782</td>\n",
       "      <td>0</td>\n",
       "      <td>8456</td>\n",
       "      <td>345</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>1845260</td>\n",
       "      <td>1024992</td>\n",
       "      <td>from_behind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36615</th>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24952</td>\n",
       "      <td>2</td>\n",
       "      <td>9798</td>\n",
       "      <td>236</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>2526319</td>\n",
       "      <td>1229303</td>\n",
       "      <td>hands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40090</th>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>62</td>\n",
       "      <td>40565</td>\n",
       "      <td>32728</td>\n",
       "      <td>1</td>\n",
       "      <td>9850</td>\n",
       "      <td>317</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>2712426</td>\n",
       "      <td>1229464</td>\n",
       "      <td>serious_foul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89685</th>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>59</td>\n",
       "      <td>40176</td>\n",
       "      <td>30894</td>\n",
       "      <td>1</td>\n",
       "      <td>9823</td>\n",
       "      <td>353</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>682596</td>\n",
       "      <td>499613</td>\n",
       "      <td>serious_foul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124566</th>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "      <td>25598</td>\n",
       "      <td>30453</td>\n",
       "      <td>1</td>\n",
       "      <td>8686</td>\n",
       "      <td>316</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>639657</td>\n",
       "      <td>539859</td>\n",
       "      <td>serious_foul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127296</th>\n",
       "      <td>None</td>\n",
       "      <td>133</td>\n",
       "      <td>22</td>\n",
       "      <td>18880</td>\n",
       "      <td>30871</td>\n",
       "      <td>0</td>\n",
       "      <td>9885</td>\n",
       "      <td>213</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>890095</td>\n",
       "      <td>704641</td>\n",
       "      <td>penalty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182892</th>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>150250</td>\n",
       "      <td>163236</td>\n",
       "      <td>0</td>\n",
       "      <td>9864</td>\n",
       "      <td>255</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>974588</td>\n",
       "      <td>684989</td>\n",
       "      <td>serious_foul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189262</th>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>103904</td>\n",
       "      <td>33759</td>\n",
       "      <td>2</td>\n",
       "      <td>8558</td>\n",
       "      <td>255</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>1436047</td>\n",
       "      <td>875757</td>\n",
       "      <td>serious_foul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195346</th>\n",
       "      <td>None</td>\n",
       "      <td>210</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30911</td>\n",
       "      <td>0</td>\n",
       "      <td>8633</td>\n",
       "      <td>248</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>1945794</td>\n",
       "      <td>1051870</td>\n",
       "      <td>serious_foul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195937</th>\n",
       "      <td>None</td>\n",
       "      <td>43</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58294</td>\n",
       "      <td>2</td>\n",
       "      <td>8696</td>\n",
       "      <td>216</td>\n",
       "      <td>foulcommit</td>\n",
       "      <td>1992244</td>\n",
       "      <td>1051908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       stats event_incident_typefk elapsed player2 player1 sortorder   team  \\\n",
       "6779    None                     5      61   22764   23034         0   8667   \n",
       "25883   None                    43      90     NaN   38994         3  10260   \n",
       "26526   None                    19      21   29581   30338         0   9879   \n",
       "28085   None                    19      39   24011   23782         0   8456   \n",
       "36615   None                     3      74     NaN   24952         2   9798   \n",
       "40090   None                     5      62   40565   32728         1   9850   \n",
       "89685   None                     5      59   40176   30894         1   9823   \n",
       "124566  None                     5      67   25598   30453         1   8686   \n",
       "127296  None                   133      22   18880   30871         0   9885   \n",
       "182892  None                     5      38  150250  163236         0   9864   \n",
       "189262  None                     5      39  103904   33759         2   8558   \n",
       "195346  None                   210      87     NaN   30911         0   8633   \n",
       "195937  None                    43      88     NaN   58294         2   8696   \n",
       "\n",
       "          n        type       id  match_api_id       subtype elapsed_plus  \\\n",
       "6779    349  foulcommit   657558        489382  serious_foul          NaN   \n",
       "25883   357  foulcommit  1202404        839875           NaN            3   \n",
       "26526   235  foulcommit  1603837       1024173   from_behind          NaN   \n",
       "28085   345  foulcommit  1845260       1024992   from_behind          NaN   \n",
       "36615   236  foulcommit  2526319       1229303         hands          NaN   \n",
       "40090   317  foulcommit  2712426       1229464  serious_foul          NaN   \n",
       "89685   353  foulcommit   682596        499613  serious_foul          NaN   \n",
       "124566  316  foulcommit   639657        539859  serious_foul          NaN   \n",
       "127296  213  foulcommit   890095        704641       penalty          NaN   \n",
       "182892  255  foulcommit   974588        684989  serious_foul          NaN   \n",
       "189262  255  foulcommit  1436047        875757  serious_foul          NaN   \n",
       "195346  248  foulcommit  1945794       1051870  serious_foul          NaN   \n",
       "195937  216  foulcommit  1992244       1051908           NaN          NaN   \n",
       "\n",
       "       card_type  del  coordinates venue injury_time  \n",
       "6779           y  NaN          NaN   NaN         NaN  \n",
       "25883          y  NaN          NaN   NaN         NaN  \n",
       "26526          y  NaN          NaN   NaN         NaN  \n",
       "28085          y  NaN          NaN   NaN         NaN  \n",
       "36615          y  NaN          NaN   NaN         NaN  \n",
       "40090          y  NaN          NaN   NaN         NaN  \n",
       "89685          y  NaN          NaN   NaN         NaN  \n",
       "124566         y  NaN          NaN   NaN         NaN  \n",
       "127296         y  NaN          NaN   NaN         NaN  \n",
       "182892         y  NaN          NaN   NaN         NaN  \n",
       "189262         y  NaN          NaN   NaN         NaN  \n",
       "195346         y  NaN          NaN   NaN         NaN  \n",
       "195937         y  NaN          NaN   NaN         NaN  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.card_type==\"y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>home_player_X1</th>\n",
       "      <th>home_player_X2</th>\n",
       "      <th>home_player_X3</th>\n",
       "      <th>home_player_X4</th>\n",
       "      <th>home_player_X5</th>\n",
       "      <th>home_player_X6</th>\n",
       "      <th>home_player_X7</th>\n",
       "      <th>home_player_X8</th>\n",
       "      <th>home_player_X9</th>\n",
       "      <th>home_player_X10</th>\n",
       "      <th>home_player_X11</th>\n",
       "      <th>away_player_X1</th>\n",
       "      <th>away_player_X2</th>\n",
       "      <th>away_player_X3</th>\n",
       "      <th>away_player_X4</th>\n",
       "      <th>away_player_X5</th>\n",
       "      <th>away_player_X6</th>\n",
       "      <th>away_player_X7</th>\n",
       "      <th>away_player_X8</th>\n",
       "      <th>away_player_X9</th>\n",
       "      <th>away_player_X10</th>\n",
       "      <th>away_player_X11</th>\n",
       "      <th>home_player_Y1</th>\n",
       "      <th>home_player_Y2</th>\n",
       "      <th>home_player_Y3</th>\n",
       "      <th>home_player_Y4</th>\n",
       "      <th>home_player_Y5</th>\n",
       "      <th>home_player_Y6</th>\n",
       "      <th>home_player_Y7</th>\n",
       "      <th>home_player_Y8</th>\n",
       "      <th>home_player_Y9</th>\n",
       "      <th>home_player_Y10</th>\n",
       "      <th>home_player_Y11</th>\n",
       "      <th>away_player_Y1</th>\n",
       "      <th>away_player_Y2</th>\n",
       "      <th>away_player_Y3</th>\n",
       "      <th>away_player_Y4</th>\n",
       "      <th>away_player_Y5</th>\n",
       "      <th>away_player_Y6</th>\n",
       "      <th>away_player_Y7</th>\n",
       "      <th>away_player_Y8</th>\n",
       "      <th>away_player_Y9</th>\n",
       "      <th>away_player_Y10</th>\n",
       "      <th>away_player_Y11</th>\n",
       "      <th>home_player_1</th>\n",
       "      <th>home_player_2</th>\n",
       "      <th>home_player_3</th>\n",
       "      <th>home_player_4</th>\n",
       "      <th>home_player_5</th>\n",
       "      <th>home_player_6</th>\n",
       "      <th>home_player_7</th>\n",
       "      <th>home_player_8</th>\n",
       "      <th>home_player_9</th>\n",
       "      <th>home_player_10</th>\n",
       "      <th>home_player_11</th>\n",
       "      <th>away_player_1</th>\n",
       "      <th>away_player_2</th>\n",
       "      <th>away_player_3</th>\n",
       "      <th>away_player_4</th>\n",
       "      <th>away_player_5</th>\n",
       "      <th>away_player_6</th>\n",
       "      <th>away_player_7</th>\n",
       "      <th>away_player_8</th>\n",
       "      <th>away_player_9</th>\n",
       "      <th>away_player_10</th>\n",
       "      <th>away_player_11</th>\n",
       "      <th>goal</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotoff</th>\n",
       "      <th>foulcommit</th>\n",
       "      <th>card</th>\n",
       "      <th>cross</th>\n",
       "      <th>corner</th>\n",
       "      <th>possession</th>\n",
       "      <th>league_name</th>\n",
       "      <th>country_name</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2008</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>34</td>\n",
       "      <td>2009-04-25 00:00:00</td>\n",
       "      <td>489382</td>\n",
       "      <td>8667</td>\n",
       "      <td>8650</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23021.0</td>\n",
       "      <td>34430.0</td>\n",
       "      <td>34275.0</td>\n",
       "      <td>26066.0</td>\n",
       "      <td>38816.0</td>\n",
       "      <td>23034.0</td>\n",
       "      <td>30595.0</td>\n",
       "      <td>34437.0</td>\n",
       "      <td>23025.0</td>\n",
       "      <td>39073.0</td>\n",
       "      <td>23563.0</td>\n",
       "      <td>30660.0</td>\n",
       "      <td>37442.0</td>\n",
       "      <td>30617.0</td>\n",
       "      <td>22764.0</td>\n",
       "      <td>47547.0</td>\n",
       "      <td>30889.0</td>\n",
       "      <td>38818.0</td>\n",
       "      <td>37139.0</td>\n",
       "      <td>95327.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>30853.0</td>\n",
       "      <td>&lt;goal&gt;&lt;value&gt;&lt;comment&gt;n&lt;/comment&gt;&lt;stats&gt;&lt;goals...</td>\n",
       "      <td>&lt;shoton&gt;&lt;value&gt;&lt;stats&gt;&lt;blocked&gt;1&lt;/blocked&gt;&lt;/st...</td>\n",
       "      <td>&lt;shotoff&gt;&lt;value&gt;&lt;stats&gt;&lt;shotoff&gt;1&lt;/shotoff&gt;&lt;/s...</td>\n",
       "      <td>&lt;foulcommit&gt;&lt;value&gt;&lt;stats&gt;&lt;foulscommitted&gt;1&lt;/f...</td>\n",
       "      <td>&lt;card&gt;&lt;value&gt;&lt;comment&gt;y&lt;/comment&gt;&lt;stats&gt;&lt;ycard...</td>\n",
       "      <td>&lt;cross&gt;&lt;value&gt;&lt;event_incident_typefk&gt;123&lt;/even...</td>\n",
       "      <td>&lt;corner&gt;&lt;value&gt;&lt;stats&gt;&lt;corners&gt;1&lt;/corners&gt;&lt;/st...</td>\n",
       "      <td>&lt;possession&gt;&lt;value&gt;&lt;comment&gt;46&lt;/comment&gt;&lt;event...</td>\n",
       "      <td>England Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>Hull City</td>\n",
       "      <td>Liverpool</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  country_id  league_id     season  stage                 date  \\\n",
       "2007  2008        1729       1729  2008/2009     34  2009-04-25 00:00:00   \n",
       "\n",
       "      match_api_id  home_team_api_id  away_team_api_id  home_team_goal  \\\n",
       "2007        489382              8667              8650               1   \n",
       "\n",
       "      away_team_goal  home_player_X1  home_player_X2  home_player_X3  \\\n",
       "2007               3             1.0             2.0             4.0   \n",
       "\n",
       "      home_player_X4  home_player_X5  home_player_X6  home_player_X7  \\\n",
       "2007             6.0             8.0             2.0             4.0   \n",
       "\n",
       "      home_player_X8  home_player_X9  home_player_X10  home_player_X11  \\\n",
       "2007             6.0             8.0              5.0              5.0   \n",
       "\n",
       "      away_player_X1  away_player_X2  away_player_X3  away_player_X4  \\\n",
       "2007             1.0             2.0             4.0             6.0   \n",
       "\n",
       "      away_player_X5  away_player_X6  away_player_X7  away_player_X8  \\\n",
       "2007             8.0             4.0             6.0             3.0   \n",
       "\n",
       "      away_player_X9  away_player_X10  away_player_X11  home_player_Y1  \\\n",
       "2007             5.0              7.0              5.0             1.0   \n",
       "\n",
       "      home_player_Y2  home_player_Y3  home_player_Y4  home_player_Y5  \\\n",
       "2007             3.0             3.0             3.0             3.0   \n",
       "\n",
       "      home_player_Y6  home_player_Y7  home_player_Y8  home_player_Y9  \\\n",
       "2007             7.0             7.0             7.0             7.0   \n",
       "\n",
       "      home_player_Y10  home_player_Y11  away_player_Y1  away_player_Y2  \\\n",
       "2007              9.0             11.0             1.0             3.0   \n",
       "\n",
       "      away_player_Y3  away_player_Y4  away_player_Y5  away_player_Y6  \\\n",
       "2007             3.0             3.0             3.0             6.0   \n",
       "\n",
       "      away_player_Y7  away_player_Y8  away_player_Y9  away_player_Y10  \\\n",
       "2007             6.0             8.0             8.0              8.0   \n",
       "\n",
       "      away_player_Y11  home_player_1  home_player_2  home_player_3  \\\n",
       "2007             11.0        23021.0        34430.0        34275.0   \n",
       "\n",
       "      home_player_4  home_player_5  home_player_6  home_player_7  \\\n",
       "2007        26066.0        38816.0        23034.0        30595.0   \n",
       "\n",
       "      home_player_8  home_player_9  home_player_10  home_player_11  \\\n",
       "2007        34437.0        23025.0         39073.0         23563.0   \n",
       "\n",
       "      away_player_1  away_player_2  away_player_3  away_player_4  \\\n",
       "2007        30660.0        37442.0        30617.0        22764.0   \n",
       "\n",
       "      away_player_5  away_player_6  away_player_7  away_player_8  \\\n",
       "2007        47547.0        30889.0        38818.0        37139.0   \n",
       "\n",
       "      away_player_9  away_player_10  away_player_11  \\\n",
       "2007        95327.0         24800.0         30853.0   \n",
       "\n",
       "                                                   goal  \\\n",
       "2007  <goal><value><comment>n</comment><stats><goals...   \n",
       "\n",
       "                                                 shoton  \\\n",
       "2007  <shoton><value><stats><blocked>1</blocked></st...   \n",
       "\n",
       "                                                shotoff  \\\n",
       "2007  <shotoff><value><stats><shotoff>1</shotoff></s...   \n",
       "\n",
       "                                             foulcommit  \\\n",
       "2007  <foulcommit><value><stats><foulscommitted>1</f...   \n",
       "\n",
       "                                                   card  \\\n",
       "2007  <card><value><comment>y</comment><stats><ycard...   \n",
       "\n",
       "                                                  cross  \\\n",
       "2007  <cross><value><event_incident_typefk>123</even...   \n",
       "\n",
       "                                                 corner  \\\n",
       "2007  <corner><value><stats><corners>1</corners></st...   \n",
       "\n",
       "                                             possession  \\\n",
       "2007  <possession><value><comment>46</comment><event...   \n",
       "\n",
       "                 league_name country_name home_team_name away_team_name  \n",
       "2007  England Premier League      England      Hull City      Liverpool  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partido\n",
    "Match[Match.match_api_id==489382]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>birthday</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1937</td>\n",
       "      <td>23034</td>\n",
       "      <td>Craig Fagan</td>\n",
       "      <td>152677</td>\n",
       "      <td>1982-12-11 00:00:00</td>\n",
       "      <td>180.34</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  player_api_id  player_name  player_fifa_api_id             birthday  \\\n",
       "0  1937          23034  Craig Fagan              152677  1982-12-11 00:00:00   \n",
       "\n",
       "   height  weight  \n",
       "0  180.34     165  "
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Player1 \n",
    "query(\"select * from Player where player_api_id=23034\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de la informacion del partido (https://int.soccerway.com/matches/2009/04/25/england/premier-league/hull-city-afc/liverpool-fc/637259/commentary/) se concluye que:\n",
    "\n",
    "* player1: id del jugador que recibió la tarjeta\n",
    "* player2: jugador que recibió la falta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Card: Información de las tarjetas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Acá se obtiene la información de todos los tiros fuera arco\n",
    "df = query(\"\"\"select * from match where card is not null\"\"\")\n",
    "data = xml_to_df(df[\"card\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>card_type</th>\n",
       "      <th>subtype</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>id</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>del</th>\n",
       "      <th>goal_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>y</td>\n",
       "      <td>None</td>\n",
       "      <td>73</td>\n",
       "      <td>78</td>\n",
       "      <td>y</td>\n",
       "      <td>serious_fouls</td>\n",
       "      <td>24157</td>\n",
       "      <td>1</td>\n",
       "      <td>10260</td>\n",
       "      <td>342</td>\n",
       "      <td>card</td>\n",
       "      <td>379481</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>y</td>\n",
       "      <td>None</td>\n",
       "      <td>73</td>\n",
       "      <td>82</td>\n",
       "      <td>y</td>\n",
       "      <td>serious_fouls</td>\n",
       "      <td>30362</td>\n",
       "      <td>1</td>\n",
       "      <td>10260</td>\n",
       "      <td>346</td>\n",
       "      <td>card</td>\n",
       "      <td>379503</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>y</td>\n",
       "      <td>None</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30829</td>\n",
       "      <td>1</td>\n",
       "      <td>10260</td>\n",
       "      <td>353</td>\n",
       "      <td>card</td>\n",
       "      <td>379547</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>y</td>\n",
       "      <td>None</td>\n",
       "      <td>73</td>\n",
       "      <td>56</td>\n",
       "      <td>y</td>\n",
       "      <td>serious_fouls</td>\n",
       "      <td>37442</td>\n",
       "      <td>5</td>\n",
       "      <td>8650</td>\n",
       "      <td>327</td>\n",
       "      <td>card</td>\n",
       "      <td>377978</td>\n",
       "      <td>489044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>y</td>\n",
       "      <td>None</td>\n",
       "      <td>25</td>\n",
       "      <td>90</td>\n",
       "      <td>y</td>\n",
       "      <td>stall_time</td>\n",
       "      <td>46621</td>\n",
       "      <td>3</td>\n",
       "      <td>8650</td>\n",
       "      <td>353</td>\n",
       "      <td>card</td>\n",
       "      <td>378060</td>\n",
       "      <td>489044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  comment stats event_incident_typefk elapsed card_type        subtype  \\\n",
       "0       y  None                    73      78         y  serious_fouls   \n",
       "1       y  None                    73      82         y  serious_fouls   \n",
       "2       y  None                    70      90         y            NaN   \n",
       "3       y  None                    73      56         y  serious_fouls   \n",
       "4       y  None                    25      90         y     stall_time   \n",
       "\n",
       "  player1 sortorder   team    n  type      id  match_api_id elapsed_plus  del  \\\n",
       "0   24157         1  10260  342  card  379481        489042          NaN  NaN   \n",
       "1   30362         1  10260  346  card  379503        489042          NaN  NaN   \n",
       "2   30829         1  10260  353  card  379547        489042          NaN  NaN   \n",
       "3   37442         5   8650  327  card  377978        489044          NaN  NaN   \n",
       "4   46621         3   8650  353  card  378060        489044          NaN  NaN   \n",
       "\n",
       "  goal_type  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subtype             \n",
       "serious_fouls           7680\n",
       "Unsportsmanlike Cond    3643\n",
       "verbal_abuse            2003\n",
       "emergency_brake         1313\n",
       "shirt_pull               750\n",
       "stall_time               642\n",
       "pushing                  545\n",
       "hands                    531\n",
       "advantage                469\n",
       "diving                   451\n",
       "violence                 396\n",
       "kicked_ball_away         263\n",
       "Removing Shirt           178\n",
       "dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de tarjetas\n",
    "data.value_counts([\"subtype\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta columna da simplemente más información acerca de la tarjeta, como por ejemplo el tipo de falta."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Corner: Información de los tiros de esquina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Acá se obtiene la información de todos los tiros fuera arco\n",
    "df = query(\"\"\"select * from match where corner is not null\"\"\")\n",
    "data = xml_to_df(df[\"corner\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>subtype</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>id</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>del</th>\n",
       "      <th>spectators</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>329</td>\n",
       "      <td>19</td>\n",
       "      <td>cross</td>\n",
       "      <td>38807</td>\n",
       "      <td>0</td>\n",
       "      <td>10261</td>\n",
       "      <td>280</td>\n",
       "      <td>corner</td>\n",
       "      <td>378960</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>330</td>\n",
       "      <td>22</td>\n",
       "      <td>short</td>\n",
       "      <td>40565</td>\n",
       "      <td>0</td>\n",
       "      <td>10261</td>\n",
       "      <td>263</td>\n",
       "      <td>corner</td>\n",
       "      <td>378992</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>329</td>\n",
       "      <td>22</td>\n",
       "      <td>cross</td>\n",
       "      <td>38807</td>\n",
       "      <td>4</td>\n",
       "      <td>10261</td>\n",
       "      <td>293</td>\n",
       "      <td>corner</td>\n",
       "      <td>378996</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>329</td>\n",
       "      <td>48</td>\n",
       "      <td>cross</td>\n",
       "      <td>24154</td>\n",
       "      <td>2</td>\n",
       "      <td>10260</td>\n",
       "      <td>300</td>\n",
       "      <td>corner</td>\n",
       "      <td>379352</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>329</td>\n",
       "      <td>51</td>\n",
       "      <td>cross</td>\n",
       "      <td>38807</td>\n",
       "      <td>0</td>\n",
       "      <td>10261</td>\n",
       "      <td>301</td>\n",
       "      <td>corner</td>\n",
       "      <td>379366</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stats event_incident_typefk elapsed subtype player1 sortorder   team    n  \\\n",
       "0  None                   329      19   cross   38807         0  10261  280   \n",
       "1  None                   330      22   short   40565         0  10261  263   \n",
       "2  None                   329      22   cross   38807         4  10261  293   \n",
       "3  None                   329      48   cross   24154         2  10260  300   \n",
       "4  None                   329      51   cross   38807         0  10261  301   \n",
       "\n",
       "     type      id  match_api_id elapsed_plus  del spectators  coordinates  \n",
       "0  corner  378960        489042          NaN  NaN        NaN          NaN  \n",
       "1  corner  378992        489042          NaN  NaN        NaN          NaN  \n",
       "2  corner  378996        489042          NaN  NaN        NaN          NaN  \n",
       "3  corner  379352        489042          NaN  NaN        NaN          NaN  \n",
       "4  corner  379366        489042          NaN  NaN        NaN          NaN  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subtype    \n",
       "cross          42251\n",
       "cross_right    17422\n",
       "cross_left     15746\n",
       "short           6526\n",
       "short_left      3079\n",
       "short_right     2652\n",
       "dtype: int64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de tarjetas\n",
    "data.value_counts([\"subtype\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta columna da simplemente más información acerca de los tiros de esquina"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cross: Información acerca de los pases largos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Acá se obtiene la información de todos los tiros fuera arco\n",
    "df = query(\"\"\"select * from match where cross is not null\"\"\")\n",
    "data = xml_to_df(df[\"cross\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stats</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>subtype</th>\n",
       "      <th>player1</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>team</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>id</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>del</th>\n",
       "      <th>spectators</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>goal_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>cross</td>\n",
       "      <td>30829</td>\n",
       "      <td>1</td>\n",
       "      <td>10260</td>\n",
       "      <td>265</td>\n",
       "      <td>cross</td>\n",
       "      <td>378863</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>cross</td>\n",
       "      <td>24148</td>\n",
       "      <td>0</td>\n",
       "      <td>10260</td>\n",
       "      <td>255</td>\n",
       "      <td>cross</td>\n",
       "      <td>378921</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>329</td>\n",
       "      <td>19</td>\n",
       "      <td>cross</td>\n",
       "      <td>38807</td>\n",
       "      <td>0</td>\n",
       "      <td>10261</td>\n",
       "      <td>280</td>\n",
       "      <td>corner</td>\n",
       "      <td>378960</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>cross</td>\n",
       "      <td>30929</td>\n",
       "      <td>5</td>\n",
       "      <td>10261</td>\n",
       "      <td>292</td>\n",
       "      <td>cross</td>\n",
       "      <td>378970</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>cross</td>\n",
       "      <td>24148</td>\n",
       "      <td>0</td>\n",
       "      <td>10260</td>\n",
       "      <td>259</td>\n",
       "      <td>cross</td>\n",
       "      <td>378978</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stats event_incident_typefk elapsed subtype player1 sortorder   team    n  \\\n",
       "0  None                     7       7   cross   30829         1  10260  265   \n",
       "1  None                     7      14   cross   24148         0  10260  255   \n",
       "2  None                   329      19   cross   38807         0  10261  280   \n",
       "3  None                     7      19   cross   30929         5  10261  292   \n",
       "4  None                     7      20   cross   24148         0  10260  259   \n",
       "\n",
       "     type      id  match_api_id elapsed_plus  del spectators  coordinates  \\\n",
       "0   cross  378863        489042          NaN  NaN        NaN          NaN   \n",
       "1   cross  378921        489042          NaN  NaN        NaN          NaN   \n",
       "2  corner  378960        489042          NaN  NaN        NaN          NaN   \n",
       "3   cross  378970        489042          NaN  NaN        NaN          NaN   \n",
       "4   cross  378978        489042          NaN  NaN        NaN          NaN   \n",
       "\n",
       "  goal_type  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subtype\n",
       "cross      284775\n",
       "dtype: int64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de tarjetas\n",
    "data.value_counts([\"subtype\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta columna da simplemente más información acerca de los pases largos, como el minuto (elapsed) y el jugador que los ejecutó (player1), esto no se verificó en internet, pues se deduce a partir de la sintaxis y de lo que se ha estudiado hasta ahora."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Possession: Información acerca de la posesión del balón"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Acá se obtiene la información de todos los tiros fuera arco\n",
    "df = query(\"\"\"select * from match where possession is not null\"\"\")\n",
    "data = xml_to_df(df[\"possession\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>event_incident_typefk</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>subtype</th>\n",
       "      <th>sortorder</th>\n",
       "      <th>awaypos</th>\n",
       "      <th>homepos</th>\n",
       "      <th>n</th>\n",
       "      <th>type</th>\n",
       "      <th>id</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>elapsed_plus</th>\n",
       "      <th>goal_type</th>\n",
       "      <th>card_type</th>\n",
       "      <th>injury_time</th>\n",
       "      <th>del</th>\n",
       "      <th>stats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>352</td>\n",
       "      <td>25</td>\n",
       "      <td>possession</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>68</td>\n",
       "      <td>special</td>\n",
       "      <td>379029</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>352</td>\n",
       "      <td>45</td>\n",
       "      <td>possession</td>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "      <td>54</td>\n",
       "      <td>117</td>\n",
       "      <td>special</td>\n",
       "      <td>379251</td>\n",
       "      <td>489042</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>352</td>\n",
       "      <td>70</td>\n",
       "      <td>possession</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>54</td>\n",
       "      <td>190</td>\n",
       "      <td>special</td>\n",
       "      <td>379443</td>\n",
       "      <td>489042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>352</td>\n",
       "      <td>90</td>\n",
       "      <td>possession</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "      <td>252</td>\n",
       "      <td>special</td>\n",
       "      <td>379575</td>\n",
       "      <td>489042</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>352</td>\n",
       "      <td>27</td>\n",
       "      <td>possession</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>special</td>\n",
       "      <td>375608</td>\n",
       "      <td>489043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  comment event_incident_typefk elapsed     subtype sortorder awaypos homepos  \\\n",
       "0      56                   352      25  possession         1      44      56   \n",
       "1      54                   352      45  possession         4      46      54   \n",
       "2      54                   352      70  possession         0      46      54   \n",
       "3      55                   352      90  possession         1      45      55   \n",
       "4      65                   352      27  possession         0      35      65   \n",
       "\n",
       "     n     type      id  match_api_id elapsed_plus goal_type card_type  \\\n",
       "0   68  special  379029        489042          NaN       NaN       NaN   \n",
       "1  117  special  379251        489042            1       NaN       NaN   \n",
       "2  190  special  379443        489042          NaN       NaN       NaN   \n",
       "3  252  special  379575        489042            5       NaN       NaN   \n",
       "4   67  special  375608        489043          NaN       NaN       NaN   \n",
       "\n",
       "  injury_time  del  stats  \n",
       "0         NaN  NaN    NaN  \n",
       "1         NaN  NaN    NaN  \n",
       "2         NaN  NaN    NaN  \n",
       "3         NaN  NaN    NaN  \n",
       "4         NaN  NaN    NaN  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subtype   \n",
       "possession    34815\n",
       "dtype: int64"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipos de tarjetas\n",
    "data.value_counts([\"subtype\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Información acerca de la posesión del balon, (awaypos y homepos) en determinados momentos del partido (elapsed), nuevamente esta información se deduce a partir del sintaxis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusión\n",
    "En general se puede decir que a partir de la información contenida en la tabla Match, se puede describir de buena manera un partido, pues es posible:\n",
    "\n",
    "* Identificar el equipo local, visitante, y los respectivos jugadores.\n",
    "* Identificar la posición de cada jugador.\n",
    "* Identificar quién anotó los goles, y quien asistió, así como también información acerca de las faltas cometidas, pases largos y posesión del balón.\n",
    "\n",
    "Si bien existe bastante información, esta se enfoca más en el aspecto ofensivo del juego (goles, asistencia, centros, tiros de esquina), y no en el aspecto defensivo, por ejemplo:\n",
    "\n",
    "* Intercepciones.\n",
    "* Despejes.\n",
    "* Tiros de esquina ganados.\n",
    "\n",
    "**Es decir, que a priori los jugadores ofensivos son más facilmente identificables a partir de esta información que los defensivos.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pregunta 4 y 5\n",
    "*Dado que queremos armar un equipo maravilloso nos interesa tener un perfil por cada jugador. Ocupa SQL\n",
    "y/o Python para crear un Dataframe que tenga un jugador por fila con toda la información que creas\n",
    "relevante*\n",
    "\n",
    "*¿Qué insights ves en el perfil por jugador? ¿Qué data crees que es relevante para elegir a los mejores\n",
    "jugadores?*\n",
    "\n",
    "Esta pregunta decidí responderla en conjunto, pues prefiero primeramente mostrar qué tipos de datos son relevantes para elegir a los mejores jugadores, y posteriormente construir el perfil de cada jugador."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Qué data crees que es relevante para elegir a los mejores\n",
    "jugadores?*\n",
    "* En primer lugar, la posición del jugador, pues por ejemplo, no es posible evaluar de igual manera a un arquero que a un delantero, pues sus tareas son opuestas. Para lograr lo anterior, se utilizará la información de la posición de cada jugador disponible en la tabla Match (*home/away_player_X/Y*). Como un jugador puede jugar en diferentes posiciones dependiendo del partido, simplemente se utilizará la media de la posición por cada jugador, es decir la posición en que más veces ha jugado. Dentro de las posiciones y con el objetivo de simplificar el análisis se diferenciarán simplemente 4 posiciones:\n",
    "\n",
    "* Arquero (ARQ).\n",
    "* Defensa (DF).\n",
    "* Mediocampista (MC).\n",
    "* Delantero (DEL).\n",
    "\n",
    "Esto es claramente mejorable, pues la labor de un delantero centro, es diferente a las de un delantero extremo, asi como también un lateral/defensa central, o un volante creador/defensivo.\n",
    "\n",
    "Posteriormente se explicará cómo identificar cada posición.\n",
    "\n",
    "Las métricas **que se crearán** para evaluar a cada jugador serán las siguientes, para la posición de arquero:\n",
    "\n",
    "* goalsR_per_match $=\\frac{goals}{matches} $ (Cantidad de goles recibidos dividido por el número de partidos).\n",
    "* blocks_per_match $=\\frac{blocks}{matches}$ (Cantidad de tiros bloqueados dividido por el número de partidos).\n",
    "\n",
    "Para la posición de defensa:\n",
    "* fouls_per_match $=\\frac{fouls}{matches}$(Número de faltas cometidas dividido por la cantidad de partidos)\n",
    "* goalsR_per_match\n",
    "\n",
    "Para la posición de mediocampista:\n",
    "* asst_per_match: $=\\frac{assists}{matches}$ (Número de asistencias dividido por la cantidad de partidos)\n",
    "* goalsR_per_match\n",
    "* goals_per_match\n",
    "* cross_per_match: $=\\frac{crosses}{matches}$ (Número de pases largos dividido por la cantidad de partidos)\n",
    "* foulsR_per_match: $\\frac{fouls \\; recibidos}{match}$ (Número de faltas recibidas dividido por la cantidad de partidos)\n",
    "\n",
    "Para la posición de delantero:\n",
    "* asst_per_match\n",
    "* goals_per_match: Cantidad de goles realizados dividido por el numero de partidos\n",
    "* precision: $=\\frac{goals}{shoton}$ (Número de goles dividido por el numero de tiros, es decir, el porcentaje de tiros que son goles)\n",
    "* foulsR_per_match\n",
    "\n",
    "Para todos los jugadores se medirá la experiencia de acuerdo a la siguiente expresión:\n",
    "$$log(matches)$$\n",
    "con matches la cantidad de partidos disputados, esto básicamente, porque creo que llega un punto en que jugar más partidos ya no se adquiere experiencia.\n",
    "\n",
    "Creo que este tipo de datos son relevantes para poder identificar qué tan bueno es un jugador, pues están alineados con las labores respectivas (tapar para un arquero), o bien hacer goles para un delantero. Además se cree relevante la experiencia que tiene un jugador. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para la construcción de todas estas características se crea la tabla **player** (pandas dataframe) que consiste simplemente en la tabla Player de la base de datos.\n",
    "\n",
    "Simplemente se considerarán las columnas *player_api_id* y *player_name*. La edad, el peso y la estatura no se considerarán esta vez, pero puede ser una oportunidad de mejor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "player = query(\"select player_api_id, player_name from Player\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Posición:\n",
    "Para poder estudiar las posiciones de los jugadores, se realizará un mapa de calor con todas las posiciones registradas hasta la fecha."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def myplot(x, y, s, bins=1000):\n",
    "    heatmap, xedges, yedges = np.histogram2d(x, y, bins=bins, range = [[0, 10], [0, 12]])\n",
    "    heatmap = gaussian_filter(heatmap, sigma=s)\n",
    "\n",
    "    extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n",
    "    return heatmap.T, extent\n",
    "fig, ax = plt.subplots(1, figsize=(10,10))\n",
    "\n",
    "s = 20\n",
    "a = np.array([])\n",
    "b = np.array([])\n",
    "for i in range(11):\n",
    "    # Local\n",
    "    a1 = Match[[\"home_player_X\"+str(i+1)]].dropna().to_numpy().flatten()\n",
    "    b1 = Match[[\"home_player_Y\"+str(i+1)]].dropna().to_numpy().flatten()   \n",
    "    #Visita\n",
    "    a2 = Match[[\"away_player_X\"+str(i+1)]].dropna().to_numpy().flatten()\n",
    "    b2 = Match[[\"away_player_Y\"+str(i+1)]].dropna().to_numpy().flatten()\n",
    "    \n",
    "    a = np.append(a, a1)\n",
    "    b = np.append(b,b1)\n",
    "    \n",
    "    a = np.append(a, a2)\n",
    "    b = np.append(b,b2)\n",
    "    \n",
    "img, extent = myplot(a, b, s)\n",
    "ax.imshow(img, extent=extent, origin='lower', cmap=cm.jet)\n",
    "ax.set_title(\"Mapa de calor de las posiciones\")\n",
    "ax.grid(alpha = 1)\n",
    "ax.set_yticks([0,2,4.5,8.5,12]);\n",
    "ax.set_xticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de la componente vertical es posible identificar las zonas de cada posición:\n",
    "\n",
    "* [0,2): Zona del arquero\n",
    "* [2,4.5): Zona de los defensas\n",
    "* [4.5,8.5): Medio Campo\n",
    "* [8,5, 12): Delanteros\n",
    "\n",
    "Por lo tanto, para identificar la posición de cada jugador, se buscará la posición vertical del último partido disputado por tal jugador, y se escogerá su posición de acuerdo a la partición anterior. \n",
    "\n",
    "Para realizarlo, se agrupan los datos según el identificador de cada jugador, y se escoge la media de las posiciones verticales (pues no necesariamente jugó siempre en la misma posición), requiere un poco más de desarrollo, pues existen 22 posiciones (11 posiciones para cada equipo, local o visita).\n",
    "\n",
    "Se crea finalmente la tabla **y_position** (pandas dataframe) que contiene las columnas:\n",
    "\n",
    "* player_api_id\n",
    "* Y: posición vertical del jugador\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_position = pd.DataFrame([])\n",
    "\n",
    "# Se itera sobre todas las posiciones diferentes\n",
    "for i in range(11):\n",
    "    # Se obtiene la posición los partidos de local\n",
    "    y_i = Match.melt(\"home_player_\"+str(i+1),value_vars=\"home_player_Y\"+str(i+1))\n",
    "    y_i.drop(\"variable\", axis = 1, inplace=True)\n",
    "    y_i.columns = [\"player_api_id\", \"y_position\"]\n",
    "    y_position = pd.concat((y_position, y_i), axis = 0)\n",
    "    # Se obtiene la posición los partidos de visita\n",
    "    y_i = Match.melt(\"away_player_\"+str(i+1),value_vars=\"away_player_Y\"+str(i+1))\n",
    "    y_i.drop(\"variable\", axis = 1, inplace=True)\n",
    "    y_i.columns = [\"player_api_id\", \"y_position\"]\n",
    "    y_position = pd.concat((y_position, y_i), axis = 0)\n",
    "    \n",
    "y_position.dropna(inplace=True)\n",
    "# Se escoge la última posición en la que jugó cada jugador\n",
    "# y_position = y_position.groupby(\"player_api_id\")[\"y_position\"].apply(lambda x: x.mode()[0])\n",
    "y_position_mean = y_position.groupby(\"player_api_id\")[\"y_position\"].mean()\n",
    "y_position_mean = y_position_mean.reset_index(name=\"y_position\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se utiliza la sintaxis definida al comienzo de la pregunta\n",
    "def f(x):\n",
    "    if x<2:\n",
    "        return \"ARQ\"\n",
    "    elif x<4.5:\n",
    "        return \"DF\"\n",
    "    elif x<8.5:\n",
    "        return \"MC\"\n",
    "    elif x<=12:\n",
    "        return \"DEL\"\n",
    "y_position_mean.y_position = y_position_mean.y_position.apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se une a la tabla player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>505942</td>\n",
       "      <td>Aaron Appindangoye</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>155782</td>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162549</td>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30572</td>\n",
       "      <td>Aaron Galindo</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23780</td>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11055</th>\n",
       "      <td>26357</td>\n",
       "      <td>Zoumana Camara</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11056</th>\n",
       "      <td>111182</td>\n",
       "      <td>Zsolt Laczko</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11057</th>\n",
       "      <td>36491</td>\n",
       "      <td>Zsolt Low</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11058</th>\n",
       "      <td>35506</td>\n",
       "      <td>Zurab Khizanishvili</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11059</th>\n",
       "      <td>39902</td>\n",
       "      <td>Zvjezdan Misimovic</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11060 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       player_api_id          player_name y_position\n",
       "0             505942   Aaron Appindangoye         DF\n",
       "1             155782      Aaron Cresswell         DF\n",
       "2             162549          Aaron Doran         MC\n",
       "3              30572        Aaron Galindo         DF\n",
       "4              23780         Aaron Hughes         DF\n",
       "...              ...                  ...        ...\n",
       "11055          26357       Zoumana Camara         DF\n",
       "11056         111182         Zsolt Laczko         MC\n",
       "11057          36491            Zsolt Low         DF\n",
       "11058          35506  Zurab Khizanishvili         DF\n",
       "11059          39902   Zvjezdan Misimovic        DEL\n",
       "\n",
       "[11060 rows x 3 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player = player.merge(y_position_mean, how = \"inner\", on=\"player_api_id\")\n",
    "player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Goles:\n",
    "Primeramente se estudia la cantidad de información que existen acerca de los goles, y además se aprovecha de ver el del resto de columnas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Porcentaje de nulos por columna\n",
    "plt.figure(figsize=(5,20))\n",
    "(Match.isna().sum().sort_values()/Match.shape[0]).plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spain LIGA BBVA               0.032895\n",
       "Italy Serie A                 0.099437\n",
       "France Ligue 1               33.355263\n",
       "Netherlands Eredivisie       78.349673\n",
       "Switzerland Super League     92.967651\n",
       "Scotland Premier League      99.287281\n",
       "Poland Ekstraklasa           99.635417\n",
       "Belgium Jupiler League      100.000000\n",
       "Portugal Liga ZON Sagres    100.000000\n",
       "England Premier League             NaN\n",
       "Germany 1. Bundesliga              NaN\n",
       "Name: league_name, dtype: float64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Porcentaje de partidos donde no existe información de los goles, desagregado por liga\n",
    "(Match[Match.goal.isna()][\"league_name\"].value_counts()/Match[\"league_name\"].value_counts()*100).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notar que las ligas de Holando, Suiza, Escocia, Polonia, Belgica y Portugal, presentan un alto porcentaje (mayor al 78%) de partidos sin información de los goles. Como estas ligas, no están dentro de las 5 mejores de europa, y el objetivo es encontrar los mejores jugadores, simplemente se filtrarán tales partidos:\n",
    "\n",
    "Top 5 ligas europas: https://www.statista.com/study/64713/leading-football-leagues-in-europe/#:~:text=The%20Big%20Five%20consists%20of,and%20Ligue%201%20in%20France.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "Match = Match.dropna(subset=[\"goal\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Se realiza el mismo grafico\n",
    "plt.figure(figsize=(5,20))\n",
    "(Match.isna().sum().sort_values()/Match.shape[0]).plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede notar que se eliminó la mayoría de los nulos a través del sencillo analisis, sin perder la oportunidad de detectar a los mejores jugadores. \n",
    "\n",
    "Habiendo realizado tal limpieza, se calculará *goals_per_match*, para ello:\n",
    "\n",
    "* Se calculará la cantidad de partidos\n",
    "* Se calculará la cantidad de goles\n",
    "* Se creará la caracteristica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2625.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2752.0</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2768.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2770.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2790.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11055</th>\n",
       "      <td>744907.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11056</th>\n",
       "      <td>746419.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11057</th>\n",
       "      <td>748432.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11058</th>\n",
       "      <td>750435.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11059</th>\n",
       "      <td>750584.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11060 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       player_api_id  matches\n",
       "0             2625.0       18\n",
       "1             2752.0       81\n",
       "2             2768.0       71\n",
       "3             2770.0       16\n",
       "4             2790.0       50\n",
       "...              ...      ...\n",
       "11055       744907.0        1\n",
       "11056       746419.0        2\n",
       "11057       748432.0        2\n",
       "11058       750435.0        1\n",
       "11059       750584.0        1\n",
       "\n",
       "[11060 rows x 2 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cantidad de partidos\n",
    "# Simplemente se utiliza la tabla y_position, y se cuenta las apariciones de cada id\n",
    "matches = y_position.groupby(\"player_api_id\").size().reset_index(name=\"matches\")\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goles realizados\n",
    "goals = xml_to_df(Match[\"goal\"])\n",
    "# Goles validos\n",
    "goals = goals[goals.goal_type.isin([\"n\", \"p\", \"rp\"])]\n",
    "goals = goals.groupby(\"player1\").size().reset_index(name = \"goals\")\n",
    "goals.columns = [\"player_api_id\",\"goals\"]\n",
    "goals.player_api_id = goals.player_api_id.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goles recibidos (goalsR)\n",
    "goalsR = pd.DataFrame([])\n",
    "\n",
    "# Se itera sobre todas las posiciones diferentes\n",
    "for i in range(11):\n",
    "    # Se obtiene los goles recibidos de local para cada jugador\n",
    "    y_i = Match.melt(\"home_player_\"+str(i+1),value_vars=\"away_team_goal\")\n",
    "    y_i.drop(\"variable\", axis = 1, inplace=True)\n",
    "    y_i.columns = [\"player_api_id\", \"goalsR\"]\n",
    "    goalsR = pd.concat((goalsR, y_i), axis = 0)\n",
    "    # Se obtiene los goles recibidos de visita para cada jugador\n",
    "    y_i = Match.melt(\"away_player_\"+str(i+1),value_vars=\"home_team_goal\")\n",
    "    y_i.drop(\"variable\", axis = 1, inplace=True)\n",
    "    y_i.columns = [\"player_api_id\", \"goalsR\"]\n",
    "    goalsR = pd.concat((goalsR, y_i), axis = 0)\n",
    "goalsR = goalsR.groupby(\"player_api_id\")[[\"goalsR\"]].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se cruza la información\n",
    "player = player.merge(matches, how = \"left\", on = \"player_api_id\")\n",
    "player = player.merge(goals, how = \"left\", on = \"player_api_id\")\n",
    "player = player.merge(goalsR, how = \"left\", on = \"player_api_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Faltas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>fouls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100033</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101042</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101070</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101103</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101192</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_api_id  fouls\n",
       "0         100033      9\n",
       "1         101042     43\n",
       "2         101070     42\n",
       "3         101103     51\n",
       "4         101192    234"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Faltas realizadas\n",
    "fouls = xml_to_df(Match[\"foulcommit\"])\n",
    "fouls = fouls[fouls.player1!=\"Unknown player\"]\n",
    "fouls = fouls.groupby(\"player1\").size().reset_index(name = \"fouls\")\n",
    "fouls.columns = [\"player_api_id\",\"fouls\"]\n",
    "\n",
    "fouls.player_api_id = fouls.player_api_id.astype(\"int\")\n",
    "fouls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>foulsR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100033</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101042</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101070</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101103</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101192</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_api_id  foulsR\n",
       "0         100033       1\n",
       "1         101042      37\n",
       "2         101070      20\n",
       "3         101103      23\n",
       "4         101192     196"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Faltas recibidas\n",
    "foulsR = xml_to_df(Match[\"foulcommit\"])\n",
    "foulsR = foulsR[foulsR.player2!=\"Unknown player\"]\n",
    "foulsR = foulsR.groupby(\"player2\").size().reset_index(name = \"foulsR\")\n",
    "foulsR.columns = [\"player_api_id\",\"foulsR\"]\n",
    "\n",
    "foulsR.player_api_id = foulsR.player_api_id.astype(\"int\")\n",
    "foulsR.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se cruza la información\n",
    "player = player.merge(fouls, how = \"left\", on = \"player_api_id\")\n",
    "player = player.merge(foulsR, how = \"left\", on = \"player_api_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Asistencias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Asistencias realizados\n",
    "assists = xml_to_df(Match[\"goal\"])\n",
    "# Asistencias validas (fue gol finalmente) (se descartan los penales)\n",
    "assists = assists[assists.goal_type.isin([\"n\"])]\n",
    "assists = assists.groupby(\"player2\").size().reset_index(name = \"assists\")\n",
    "assists.columns = [\"player_api_id\",\"assists\"]\n",
    "assists.player_api_id = assists.player_api_id.astype(\"int\")\n",
    "\n",
    "# Se cruza la información\n",
    "player = player.merge(assists, how = \"left\", on = \"player_api_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>matches</th>\n",
       "      <th>goals</th>\n",
       "      <th>goalsR</th>\n",
       "      <th>fouls</th>\n",
       "      <th>foulsR</th>\n",
       "      <th>assists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6169</th>\n",
       "      <td>30981</td>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>DEL</td>\n",
       "      <td>249</td>\n",
       "      <td>282.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      player_api_id   player_name y_position  matches  goals  goalsR  fouls  \\\n",
       "6169          30981  Lionel Messi        DEL      249  282.0   193.0  129.0   \n",
       "\n",
       "      foulsR  assists  \n",
       "6169   419.0     89.0  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notar que Messi tiene pocas asistencias en comparación a sus goles\n",
    "player[player.player_name==\"Lionel Messi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5056921873642131"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Esto es debido a que existen una gran cantidad de nulos en la columna que indica la asistencia del gol, al rededor del 50%:\n",
    "# Asistencias realizados\n",
    "assists = xml_to_df(Match[\"goal\"])\n",
    "# Asistencias validas (fue gol finalmente)\n",
    "assists = assists[assists.goal_type.isin([\"n\"])]\n",
    "(assists.isna().sum()[\"player2\"])/assists.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Pases largos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Faltas realizadas\n",
    "cross = xml_to_df(Match[\"cross\"])\n",
    "cross = cross.groupby(\"player1\").size().reset_index(name = \"cross\")\n",
    "cross.columns = [\"player_api_id\",\"cross\"]\n",
    "\n",
    "cross.player_api_id = cross.player_api_id.astype(\"int\")\n",
    "\n",
    "# Se cruza la información\n",
    "player = player.merge(cross, how = \"left\", on = \"player_api_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tiros al arco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tiros al arco\n",
    "shoton = xml_to_df(Match[\"shoton\"])\n",
    "shoton = shoton.groupby(\"player1\").size().reset_index(name = \"shoton\")\n",
    "shoton.columns = [\"player_api_id\",\"shoton\"]\n",
    "\n",
    "shoton.player_api_id = shoton.player_api_id.astype(\"int\")\n",
    "# Se cruza la información\n",
    "player = player.merge(shoton, how = \"left\", on = \"player_api_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>matches</th>\n",
       "      <th>goals</th>\n",
       "      <th>goalsR</th>\n",
       "      <th>fouls</th>\n",
       "      <th>foulsR</th>\n",
       "      <th>assists</th>\n",
       "      <th>cross</th>\n",
       "      <th>shoton</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2889</th>\n",
       "      <td>43283</td>\n",
       "      <td>Eduardo</td>\n",
       "      <td>ARQ</td>\n",
       "      <td>116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      player_api_id player_name y_position  matches  goals  goalsR  fouls  \\\n",
       "2889          43283     Eduardo        ARQ      116    NaN    45.0    NaN   \n",
       "\n",
       "      foulsR  assists  cross  shoton  \n",
       "2889     NaN      NaN    2.0     NaN  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player[player.player_api_id==43283]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tiros recibidos\n",
    "shotR = xml_to_df(Match[\"shoton\"])\n",
    "# Cruzamos con la información del arquero\n",
    "shotR = shotR.merge(Match[[\"match_api_id\",\"home_team_api_id\",\"away_team_api_id\",\"home_player_1\",\"away_player_1\"]], how = \"inner\", on = \"match_api_id\")\n",
    "\n",
    "# Tiros recibidos de local (shotR)\n",
    "shotRL = shotR[shotR.team!=\"home_team_api_id\"].groupby(\"home_player_1\").size().reset_index(name=\"shotR\")\n",
    "shotRL.columns = [\"player_api_id\",\"shotR\"]\n",
    "# idem caso visitante\n",
    "shotRV = shotR[shotR.team!=\"away_team_api_id\"].groupby(\"away_player_1\").size().reset_index(name=\"shotR\")\n",
    "shotRV.columns = [\"player_api_id\",\"shotR\"]\n",
    "\n",
    "shotR = shotRL.merge(shotRV, how=\"inner\", on = \"player_api_id\")\n",
    "shotR[\"shotR\"] = shotR[\"shotR_x\"]+shotR[\"shotR_y\"]\n",
    "shotR = shotR[[\"player_api_id\",\"shotR\"]]\n",
    "\n",
    "# Se cruza la información\n",
    "player = player.merge(shotR, how = \"left\", on = \"player_api_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>matches</th>\n",
       "      <th>goals</th>\n",
       "      <th>goalsR</th>\n",
       "      <th>fouls</th>\n",
       "      <th>foulsR</th>\n",
       "      <th>assists</th>\n",
       "      <th>cross</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6169</th>\n",
       "      <td>30981</td>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>DEL</td>\n",
       "      <td>249</td>\n",
       "      <td>282.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      player_api_id   player_name y_position  matches  goals  goalsR  fouls  \\\n",
       "6169          30981  Lionel Messi        DEL      249  282.0   193.0  129.0   \n",
       "\n",
       "      foulsR  assists  cross  shoton  shotR  \n",
       "6169   419.0     89.0  475.0   439.0    NaN  "
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player[player.player_name == \"Lionel Messi\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, notar que existen columnas con valores nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "player.isna().sum().sort_values().plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esto es debido a que utilizando el left join, en caso de no encontrar el indice en la tabla derecha, se reemplaza con un nan, pero esto se debe netamente a que por ejemplo, Lionel Messi no puede recibir tiros al arco, debido a que no es arquero, por lo que se reemplazan los nans con cero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "player = player.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con esta información se crea la tabla **player_features** que contiene las caracteristicas de los jugadores que permitirán evaluarlos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>matches</th>\n",
       "      <th>goals</th>\n",
       "      <th>goalsR</th>\n",
       "      <th>fouls</th>\n",
       "      <th>foulsR</th>\n",
       "      <th>assists</th>\n",
       "      <th>cross</th>\n",
       "      <th>shoton</th>\n",
       "      <th>shotR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>505942</td>\n",
       "      <td>Aaron Appindangoye</td>\n",
       "      <td>DF</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>155782</td>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>DF</td>\n",
       "      <td>75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162549</td>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>MC</td>\n",
       "      <td>104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30572</td>\n",
       "      <td>Aaron Galindo</td>\n",
       "      <td>DF</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23780</td>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>DF</td>\n",
       "      <td>162</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_api_id         player_name y_position  matches  goals  goalsR  \\\n",
       "0         505942  Aaron Appindangoye         DF        8    0.0     0.0   \n",
       "1         155782     Aaron Cresswell         DF       75    4.0    97.0   \n",
       "2         162549         Aaron Doran         MC      104    0.0     0.0   \n",
       "3          30572       Aaron Galindo         DF       10    0.0    19.0   \n",
       "4          23780        Aaron Hughes         DF      162    1.0   200.0   \n",
       "\n",
       "   fouls  foulsR  assists  cross  shoton  shotR  \n",
       "0    0.0     0.0      0.0    0.0     0.0    0.0  \n",
       "1   40.0    72.0      8.0  225.0    22.0    0.0  \n",
       "2    2.0     5.0      0.0    1.0     0.0    0.0  \n",
       "3    0.0     0.0      0.0    0.0     0.0    0.0  \n",
       "4   37.0    71.0      0.0   30.0    12.0    0.0  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se estudiarán solo los jugadores con más de 20 partidos disputados, esto con el objetivo de evitar tener por ejemplo un jugador con 3 goles por partido, siendo que solo ha jugado un partido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>goalsR_per_match</th>\n",
       "      <th>blocks_per_match</th>\n",
       "      <th>fouls_per_match</th>\n",
       "      <th>asst_per_match</th>\n",
       "      <th>cross_per_match</th>\n",
       "      <th>foulsR_per_match</th>\n",
       "      <th>precision</th>\n",
       "      <th>goals_per_match</th>\n",
       "      <th>log_matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>155782</td>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>DF</td>\n",
       "      <td>1.293333</td>\n",
       "      <td>-1.293333</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>4.317488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162549</td>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.644391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23780</td>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>DF</td>\n",
       "      <td>1.234568</td>\n",
       "      <td>-1.234568</td>\n",
       "      <td>0.228395</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.438272</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.006173</td>\n",
       "      <td>5.087596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27316</td>\n",
       "      <td>Aaron Hunt</td>\n",
       "      <td>MC</td>\n",
       "      <td>1.626582</td>\n",
       "      <td>-1.626582</td>\n",
       "      <td>0.379747</td>\n",
       "      <td>0.069620</td>\n",
       "      <td>0.069620</td>\n",
       "      <td>0.708861</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.240506</td>\n",
       "      <td>5.062595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30895</td>\n",
       "      <td>Aaron Lennon</td>\n",
       "      <td>MC</td>\n",
       "      <td>1.226519</td>\n",
       "      <td>-1.226519</td>\n",
       "      <td>0.541436</td>\n",
       "      <td>0.193370</td>\n",
       "      <td>0.193370</td>\n",
       "      <td>1.287293</td>\n",
       "      <td>0.189781</td>\n",
       "      <td>0.143646</td>\n",
       "      <td>5.198497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_api_id      player_name y_position  goalsR_per_match  \\\n",
       "1         155782  Aaron Cresswell         DF          1.293333   \n",
       "2         162549      Aaron Doran         MC          0.000000   \n",
       "4          23780     Aaron Hughes         DF          1.234568   \n",
       "5          27316       Aaron Hunt         MC          1.626582   \n",
       "7          30895     Aaron Lennon         MC          1.226519   \n",
       "\n",
       "   blocks_per_match  fouls_per_match  asst_per_match  cross_per_match  \\\n",
       "1         -1.293333         0.533333        0.106667         0.106667   \n",
       "2          0.000000         0.019231        0.000000         0.000000   \n",
       "4         -1.234568         0.228395        0.000000         0.000000   \n",
       "5         -1.626582         0.379747        0.069620         0.069620   \n",
       "7         -1.226519         0.541436        0.193370         0.193370   \n",
       "\n",
       "   foulsR_per_match  precision  goals_per_match  log_matches  \n",
       "1          0.960000   0.153846         0.053333     4.317488  \n",
       "2          0.048077        NaN         0.000000     4.644391  \n",
       "4          0.438272   0.076923         0.006173     5.087596  \n",
       "5          0.708861   0.447059         0.240506     5.062595  \n",
       "7          1.287293   0.189781         0.143646     5.198497  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se crean las features según lo comentado anteriormente siguiendo la misma sintaxis\n",
    "player_features = player[player.matches>20][[\"player_api_id\",\"player_name\", \"y_position\"]]\n",
    "player_features[\"goalsR_per_match\"] = player[\"goalsR\"]/player[\"matches\"]\n",
    "player_features[\"blocks_per_match\"] = (player[\"shotR\"] - player[\"goalsR\"])/player[\"matches\"] #Bloqueos = Tiros recibidos - goles recibidos\n",
    "player_features[\"fouls_per_match\"] = player[\"fouls\"]/player[\"matches\"]\n",
    "player_features[\"asst_per_match\"] = player[\"assists\"]/player[\"matches\"]\n",
    "player_features[\"cross_per_match\"] = player[\"assists\"]/player[\"matches\"]\n",
    "player_features[\"foulsR_per_match\"] = player[\"foulsR\"]/player[\"matches\"]\n",
    "player_features[\"precision\"] = player[\"goals\"]/(player[\"shoton\"]+player[\"goals\"]) # Goles también son tiros al arco\n",
    "player_features[\"goals_per_match\"] = player[\"goals\"]/player[\"matches\"]\n",
    "player_features[\"log_matches\"] = np.log(player[\"matches\"])\n",
    "\n",
    "player_features.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "blocks_per_match puede ser negativa, basicamente porque los jugadores que no son arqueros tienen la feature goalsR (goles recibido) igual a 0.\n",
    "\n",
    "La feature precision toma el valor nan en caso de que el jugador no tenga tiros al arco y tampoco goles, por lo que nuevamente se reemplazarán los nans por 0.\n",
    "\n",
    "En caso de que solo tenga los tiros al arco igual a 0, significa que la precision toma valor infinito, por lo que se reemplazará ese valor por 0 simplemente.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_features = player_features.fillna(0)\n",
    "player_features = player_features.replace([float('inf')], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>goalsR_per_match</th>\n",
       "      <th>blocks_per_match</th>\n",
       "      <th>fouls_per_match</th>\n",
       "      <th>asst_per_match</th>\n",
       "      <th>cross_per_match</th>\n",
       "      <th>foulsR_per_match</th>\n",
       "      <th>precision</th>\n",
       "      <th>goals_per_match</th>\n",
       "      <th>log_matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "      <td>6601.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>119417.753977</td>\n",
       "      <td>0.756202</td>\n",
       "      <td>-0.469281</td>\n",
       "      <td>0.378746</td>\n",
       "      <td>0.028448</td>\n",
       "      <td>0.028448</td>\n",
       "      <td>0.337754</td>\n",
       "      <td>0.195351</td>\n",
       "      <td>0.061829</td>\n",
       "      <td>4.103431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>123614.800418</td>\n",
       "      <td>0.671500</td>\n",
       "      <td>1.556237</td>\n",
       "      <td>0.500568</td>\n",
       "      <td>0.052355</td>\n",
       "      <td>0.052355</td>\n",
       "      <td>0.477987</td>\n",
       "      <td>0.274097</td>\n",
       "      <td>0.112846</td>\n",
       "      <td>0.644772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2752.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.480000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.044522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32931.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.335570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.555348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46836.000000</td>\n",
       "      <td>0.803828</td>\n",
       "      <td>-0.620000</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>178753.000000</td>\n",
       "      <td>1.370370</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>4.605170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>698273.000000</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>12.409091</td>\n",
       "      <td>3.730769</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.132530</td>\n",
       "      <td>5.703782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       player_api_id  goalsR_per_match  blocks_per_match  fouls_per_match  \\\n",
       "count    6601.000000       6601.000000       6601.000000      6601.000000   \n",
       "mean   119417.753977          0.756202         -0.469281         0.378746   \n",
       "std    123614.800418          0.671500          1.556237         0.500568   \n",
       "min      2752.000000          0.000000         -2.480000         0.000000   \n",
       "25%     32931.000000          0.000000         -1.335570         0.000000   \n",
       "50%     46836.000000          0.803828         -0.620000         0.130435   \n",
       "75%    178753.000000          1.370370          0.000000         0.640000   \n",
       "max    698273.000000          2.480000         12.409091         3.730769   \n",
       "\n",
       "       asst_per_match  cross_per_match  foulsR_per_match    precision  \\\n",
       "count     6601.000000      6601.000000       6601.000000  6601.000000   \n",
       "mean         0.028448         0.028448          0.337754     0.195351   \n",
       "std          0.052355         0.052355          0.477987     0.274097   \n",
       "min          0.000000         0.000000          0.000000     0.000000   \n",
       "25%          0.000000         0.000000          0.000000     0.000000   \n",
       "50%          0.000000         0.000000          0.111111     0.000000   \n",
       "75%          0.037037         0.037037          0.524590     0.312500   \n",
       "max          0.565217         0.565217          3.833333     1.000000   \n",
       "\n",
       "       goals_per_match  log_matches  \n",
       "count      6601.000000  6601.000000  \n",
       "mean          0.061829     4.103431  \n",
       "std           0.112846     0.644772  \n",
       "min           0.000000     3.044522  \n",
       "25%           0.000000     3.555348  \n",
       "50%           0.000000     4.077537  \n",
       "75%           0.068966     4.605170  \n",
       "max           1.132530     5.703782  "
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estadísticas de los datos para poder identificar outliers\n",
    "player_features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zIFN41rOexUUXXcSiRYt6HZokST3jrLmSJI2yRYsWsWjRIuacdRm3jtEZx6WdlWQJsARg9uzZPY5G0nhjj6gkSZJ2WlUtr6r5VTV/1qxZvQ5H0jhjISpJkiRJ6ioLUUmSJG0nySrg68CvJbkryeJexyRpYhlRIZrk+CTfSbI+yVmDbH9Dkg1Jbmxe/7PzoUqSJKkbqmpRVR1UVdOq6pCqWtHrmCRNLMNOVpRkKvAB4GXAXcB1SVZX1e0Dml5cVW8ahRglSZIkSRPISHpEjwbWV9UdVfUY8HHgpNENS5IkSZI0UY2kED0Y+FHb57uadQO9OsnNST6Z5NCORCdJkiRJmnA6NVnRfwBzquo5wJXAhYM1SrIkybok6zZs2NChQ0tSdw03bl6SJEk7NpJC9G6gvYfzkGbd46rqJ1W1qfn4YeD5g+3I501JGu/axs2fABwJLEpyZG+jkiRJGl9GUoheBxyR5PAkewKnAKvbGyQ5qO3jiUBf50KUpDHFcfOSJEm7adhZc6tqS5I3AZ8HpgIrq+q2JGcD66pqNfDmJCcCW4D7gTeMYsyS1EuDjZt/QY9ikSRptz333V/goZ9v7ug+55x1WUf2s+/e07jpncd1ZF8aW4YtRAGq6nLg8gHr3tG2/Hbg7Z0NTZLGpyRLgCUAs2fP7nE02h2enEmaDB76+WbuPOcVvQ5jUJ3KmRp7RlSISpIeN5Jx88uB5QDz58+v7oWmTvPkTJKk0dGpWXMlabIYdty8JEmSdsweUUnaCUONm+9xWJIkSeOKhagk7aTBxs1LkiRp5CxEJUkawsy5Z/HsC8/qdRiDmjkXYGyOX5UkaTgWopIkDeHhvnOcrEiSpFHgZEWSJEmSpK6yEJUkSZIkdZWFqCRJkiSpqxwjKknSDozVsZj77j2t1yFIkrTLLEQlSRpCpycqmnPWZWN28iNJkrrJW3MlSZIkSV1lISpJkiRJ6ioLUUmSJElSV41ojGiS44F/BKYCH66qcwZs3wv4CPB84CfAyVV1Z2dDlSRJUrcMd/6niWPm3LN49oVn9TqMQc2cC+DY+olo2EI0yVTgA8DLgLuA65Ksrqrb25otBh6oqqcnOQU4Fzh5NAKWJEnS6Brh+Z8miIf7zhmzE6mN1ZnLtftGcmvu0cD6qrqjqh4DPg6cNKDNScCFzfIngd9Oks6FKUmSpC4ayfmfJO2ykRSiBwM/avt8V7Nu0DZVtQV4CHhqJwKUJElS143k/E+SdllXnyOaZAmwBGD27NndPLRGwUhulfjBua/s6DEPe9tnh23jQ94lddvO3ASUc4dvU1W7EY3UHZ7XTSy9OK+D4c/tPK+buEZSiN4NHNr2+ZBm3WBt7kqyB7AvrUmLtlNVy4HlAPPnz/f/suPYiMcRnON/ZkkTn4WjJqBhz/88r5s4PK9TL4zk1tzrgCOSHJ5kT+AUYPWANquB1zfLrwGuKv+vLEmSNF6N5PxPknbZsD2iVbUlyZuAz9OavntlVd2W5GxgXVWtBlYA/5ZkPXA/rWQlSZKkcWio878ehyVpAhnRGNGquhy4fMC6d7QtPwr8bmdDkyRJUq8Mdv4nSZ0ykltzJUmSJEnqmPRqKGeSDcAPenJwjTUHAvf1OgiNGYdV1axeB9Ep5joNYL5TP3OdJjJznfoNmet6VohK/ZKsq6r5vY5Dkkab+U7SZGCu00h4a64kSZIkqassRCVJkiRJXWUhqrFgea8DkKQuMd9JmgzMdRqWY0QlSZIkSV1lj6gkSZIkqassRCVJkiRJXWUhqkEleVeSt+5g+xuSbEhyY5JvJ/nzbsbXCUmOSvLyEbTb2I14JO28JHOS3DrI+quT7PSjA4bLfeNR82f0+yNod2eSA7sRk6Tdk+TNSfqSXLQL350Q5zXNuegvj6DN+7sVk3aOhah2x8VVdRRwDLA0yaGjcZAke4zGfoGjgGELUUnqhrSMxv+X5wDDFqKSxpX/Bbysql7b60CGM4rncW8AdliIamyzEJ2AkvyfJN9JsjbJqiRvbXr/rk1yc5LPJNm/afvHSa5LclOSTyV5yiD7e3OS25vvfnzg9qr6CbAeOGgHMV2Q5Pwk65J8N8krm/VTk/xtE8PNSf6kWX9skq8kWQ3cPsQ+5zS9sRc0+7woyUuTfDXJ95Ic3bQ7OsnXk3wrydeS/FqSPYGzgZObXt2Tk+yT5F+T3NLE8uq2Yy1r/oyuTfK0nfjPIWn07dH8++9L8smBeSzJoubf9a1Jzm1bf3ySG5p/218auNMmP16RZO/h8mDbd96V5N+anPO9JH/ctu0v23Ldu5t1c5p8/RHgVmDQC3pJNja58rYkX2zy2tVJ7khyYtu+vtL8phuS/Lfm6+cAv9Xkuj9v8u7fNX8eNyc5ve1QpzffvSXJM4f9k5fUdUnOB34FuCLJW5Jc2vxbvjbJc5o2293d0fx7nzNgPwcluabJDbcm+a0dHHNjkv/X5KAvJZnVrP/VJJ9Lcn2Tf57ZrO8/7/sG8N4h9vmuJBc23/tBkv8vyXub/PO5JNOadu9ocuetSZan5TXAfOCiJv69k/xGc553U5JvJpnZHOqXm/19L8mgsahHqsrXBHoBvwHcCEwHZgLfA94K3Ay8uGlzNvAPzfJT2777N8DpzfK7gLc2yz8G9mqW92ve3wC8v1me3X/MHcR1AfA5Whc/jgDuamJcAvzvps1ewDrgcOBY4BHg8B3scw6wBXh2s9/rgZVAgJOAS5t2vwDs0Sy/FPjUwN/QfD63/8+l+bx/817A7zTL7+2P15cvX71/NXmggGOazyubnHc1rZOUXwZ+CMwC9gCuAl7VfP5Rf44BDmje39V8/03Av7flviflwSHieRdwE7A3cGBzjF8GjqP1OIM0+eqzwIua+LcBLxzmdxZwQrP8GeALwDTgucCNzfqn9OfhJs+ua5aPBT7btq8/BT7Zlhf7f/udPPH/gP8FfLjX/319+fI1+Kv593ogcB7wzmbdS9rywbtozuOaz7cCc5rljc37W4ClzfJUYOYOjlfAa5vld/DEOeCXgCOa5RcAVzXLFzR5buoO9vkuYG1bLvvZgDz3qmb5gLbv/BtPnJNdDcxvlvcE7gB+o/n8C7Ry/hua9fvSOu/8AXBor//7+Wq9RqurXL1zDPDvVfUo8GiS/wBm0Dpx+nLT5kLgkmZ5XpK/AfYD9gE+P8g+b6Z1xelS4NK29ScneRHwTOBNzTF35BNVtQ34XpI7mu8dBzynubIFrURxBPAY8M2q+v4w+/x+Vd0CkOQ24EtVVUluoXWC17/PC5McQSuRThtiXy8FTun/UFUPNIuP0Uqm0Cp2XzZMTJK660dV9dVm+aPAm9u2/QZwdVVtAEhrPNWLgK3ANf05pqrub/vO62gVkK+qqs3NuqHy4GD+vap+Dvw8yRrgaGABrXz3rabNPrRy3Q+BH1TVtcPs8zFaF/MAbgE2VdXmAbluGvD+JEc1v+8ZQ+zrpcD5VbUFnvTbP928Xw/8f8PEJKn3FgCvBqiqq5I8NckvjPC71wErm57HS6vqxh203QZc3Cx/FPh0kn2A/wZckqS/3V5t37mkqrYOE8MVbblsKtvnuTnN8sIkZ9K62HYAcBvwHwP282vAPVV1HUBV/RSgietLVfVQ8/l24DBaOV495q25uoBWEfls4N20rhYN9ArgA8DzgOvyxL3+F1fVc2gloXOS/NIwxxr40Nqi1TtwelUd1bwOr6ovNNsfGUH8m9qWt7V93gaPX2j5a2BNVc0DfofBf+OObK6q/ti3tu1X0tgwWG7ZHf0nQIe0rRsqD440ngD/ty3XPb2qVjTbR5Lr2vPQ47muubjXH8ufA/9Fq2dhPq0egp3Vn0PNddL4toXtz/OfdO5TVdfQujB3N3BBktftxP6r2f+DbXntqKqa29ZmxOdxTS4bmOf2SDId+GfgNc256r8M9ltGcoyGuW0MsRCdeL4K/E6S6c2VqlfSSgQPtN37/4dAf+/oTOCe5mrYkwa8pzVxxqFVtQZ4G63exX3a21TVOlq3SpwxTGy/m2RKkl+lNbbhO7R6YP+0bRzAM5LM2NkfPYx9aSVZaN2i0e9hWr+/35XAG/s/pBlHK2nMm53kN5vl36d1q1e/bwIvTnJgkqnAIlr571rgRUkOB0hyQNt3vgX8CbA6yS+PJA8OcFKTg59K67bY62jlulObvEySg5P84m796ifbl1aPwDZaeX5qs36wXPcn/cX0gN8uaXz5Cs35W5Jjgfua3sA7aV04I8nzaA172k6Sw4D/qqp/AT7c334IU4D+u9d+H1jbHOf7SX632V+SPHf3f9J2+ovO+5r8+Zq2be257TvAQUl+o4ll5jAXDDUGWIhOMM0tCatp3UZ2Ba0r+w8Brwf+NsnNtGaLPbv5yv8BvkGrgP32ILucCny0uWXiW8A/VdWDg7Q7F/ijtoHhg/khrZPCK4DTmlt5P0xrMqIb0noEw4fo/JWq9wL/N8m3Bux7DXBkM8j9ZFpjZPdvBsPfBCzscBySRsd3gDcm6QP2Bz7Yv6Gq7gHOovXv/Sbg+qr69+ZW3SW0bi+7iSduOev/3lpaY0UvA57KyPJgv5ub410L/HVV/bi50+NjwNeb/XyS7YvDTvhn4PXN73kmT/RG3AxsbSbw+HNaefeHwM1NW2fUlcavdwHPb87vzqF1vgfwKeCAZtjSm4DvDvLdY4GbmvOjk4F/3MFxHgGObs7VXsIT55GvBRY3ueQ2WnN0dEyTa/+F1hjXz9O6sNfvAuD8JDfSOl89GTivieVKdr7nVF2WJ3rANVEk2aeqNqY1c+Q1wJKquqHHMV1Aa7KMT/YyDkkaTUneRWsikL/rdSyS1ClJNlbVju4EkXaaXdYT0/IkR9K6EnRhr4tQSZIkSWpnj6g6KslS4HcHrL6kqpbtxj6fSmt68IF+u1rPMJWkrkryRzx5XPxXq+qNg7Xfif1+g+1nnQT4w/7ZwSVpNI1GDhqtfKnxz0JUkiRJktRVTlYkSZIkSeoqC1FJkiRJUldZiEqSJGlQSaYm+VaSz/Y6FkkTi4WoJEmShnIG0NfrICRNPBaikiRJepIkhwCvAD7c61gkTTwWopIkSRrMPwBnAtt6HIekCWiPXh34wAMPrDlz5vTq8JLGqOuvv/6+qprV6zg6xVwnaTBjPdcleSVwb1Vdn+TYIdosAZYAzJgx4/nPfOYzuxegpHFhR7muZ4XonDlzWLduXa8OL2mMSvKDXsfQSeY6SYMZB7nuGODEJC8HpgO/kOSjVfUH/Q2qajmwHGD+/PllrpM00I5ynbfmqmdWrVrFvHnzmDp1KvPmzWPVqlW9DkmSRoX5TuNNVb29qg6pqjnAKcBV7UWoNBhznXZGz3pENbmtWrWKpUuXsmLFChYsWMDatWtZvHgxAIsWLepxdJLUOeY7SZPBqlWrOOOMM5gxYwYAjzzyCGeccQZgrtPg7BFVTyxbtowVK1awcOFCpk2bxsKFC1mxYgXLli3rdWiS1FHmO413VXV1Vb2y13FobDvzzDPZY489WLlyJY8++igrV65kjz324Mwzz+x1aBqjUlU9ObBjCSa3qVOn8uijjzJt2rTH123evJnp06ezdevWHkamXktyfVXN73UcnWKuk/lOgzHXaaJJwhe+8AVe9rKXPb7uyiuv5LjjjqNX9YZ6b0e5zh5R9cTcuXNZu3btduvWrl3L3LlzexSRJI0O850kSU9mIaqeWLp0KYsXL2bNmjVs3ryZNWvWsHjxYpYuXdrr0CSpo8x3kiaDQw45hNe97nXb5brXve51HHLIIb0OTWOUkxWpJ/oHrZ9++un09fUxd+5cli1b5mB2SROO+U7SZPDe976XM844g1NPPZUf/OAHHHbYYWzdupX3ve99vQ5NY5SFqHpm0aJFnohJmhTMd5Imuv4ct2zZMpIwY8YM3vOe95j7NCQLUUmSJEm7zYtu2hmOEZUkSZIkdZWFqCRJkiSpqyxEJUmSJO22VatWMW/ePKZOncq8efNYtWpVr0PSGOYYUUmSJEm7ZdWqVSxdupQVK1awYMEC1q5dy+LFiwEcN6pB2SMqSZIkabcsW7aMFStWsHDhQqZNm8bChQtZsWIFy5Yt63VoGqMsRCVJkiTtlr6+PhYsWLDdugULFtDX19ejiDTWWYhKkiRJ2i1z585l7dq1261bu3Ytc+fO7VFEGuscIypJkiRptyxdupSTTz6ZGTNm8IMf/IDDDjuMRx55hH/8x3/sdWgao+wRlSRJktQxSXodgsYBC1H1jFN8S5oszHeSJrply5axZMkSZsyYAcCMGTNYsmSJkxVpSN6aq55wim9Jk4X5TtJkcPvtt3PvvfcyY8YMqopHHnmE5cuXc9999/U6NI1R9oiqJ5ziW9JkYb6TNBlMnTqVrVu3snLlSjZt2sTKlSvZunUrU6dO7XVoGqMsRNUTTvEtabIw30maDLZs2cK0adO2Wzdt2jS2bNnSo4g01lmIqiec4lvSZGG+kzRZvOAFL+CEE05gzz335IQTTuAFL3hBr0PSGOYYUfVE+xTfP/zhD5k9e7ZTfEuakMx3kiaDAw44gMsuu4z3vve9nHbaaZx//vmceeaZHHDAAb0OTWOUPaLquarqdQiS1BXmO0kT1VOe8hT22WcfzjvvPGbOnMl5553HPvvsw1Oe8pReh6YxykJUPbFs2TIuvvhivv/977Nt2za+//3vc/HFFzt5h6QJx3yn8SjJ9CTfTHJTktuSvLvXMWls+/GPf8x555233eNbzjvvPH784x/3ODKNVd6aq55w8g5Jk4X5TuPUJuAlVbUxyTRgbZIrquraXgemsWnu3Ll8+tOfZv369Wzbto3169fz6U9/2vHwGpI9ouoJJ++QNFmY7zQeVcvG5uO05uW95RrSwQcfzKWXXsqpp57Kgw8+yKmnnsqll17KwQcf3OvQNEZZiKonli5dyuLFi1mzZg2bN29mzZo1LF68mKVLl/Y6NEnqKPOdxqskU5PcCNwLXFlV3+hxSBrDvvzlL/Pa176Wa665hgMOOIBrrrmG1772tXz5y1/udWgao7w1Vz2xaNEiAE4//XT6+vqYO3cuy5Yte3y9JE0U5juNV1W1FTgqyX7AZ5LMq6pb+7cnWQIsAZg9e3ZvgtSYsWnTJpYvX77d5EQ/+9nPuOiii3oYlcYye0TVM1/72te2G0fwta99rdchSdKoMN9pPKuqB4E1wPED1i+vqvlVNX/WrFk9iU1jx1577cWSJUuYN28eU6dOZd68eSxZsoS99tqr16FpjLIQVU+cfvrpnH/++bznPe/hkUce4T3veQ/nn38+p59+eq9Dk6SOMt9pPEoyq+kJJcnewMuAb/c0KI1pL37xi7nooovo6+tj27Zt9PX1cdFFF/HiF7+416FpjPLWXPXEv/zLv3DuuefyF3/xFwCPv//VX/0V5513Xi9Dk6SOMt9pnDoIuDDJVFodF5+oqs/2OCaNYevWrQNg27Zt2733r5cGGlGPaJLjk3wnyfokZ+2g3auTVJL5nQtRE9GmTZs47bTTtlt32mmnsWnTph5FJJnrNDrMdxqPqurmqvr1qnpOVc2rqrN7HZPGtvvvv5/999+fq666iscee4yrrrqK/fffn/vvv7/XoWmMGrYQba6EfQA4ATgSWJTkyEHazQTOAJxRTcNyHIHGGnOdRov5TtJkceaZZ7Jw4UKmTZvGwoULOfPMM3sdksawkfSIHg2sr6o7quox4OPASYO0+2vgXODRDsanCap/HMGLXvQi7r//fl70ohc5jkC9Zq7TqDDfSZosli5dSpLHXz6mSjsykkL0YOBHbZ/vatY9LsnzgEOr6rIOxqYJ7O677+ZVr3oVK1euZL/99mPlypW86lWv4u677+51aJq8zHUaFeY7SZPBlClTHh8X2m/btm1MmeLcqBrcbk9WlGQK8D7gDSNo6/OmBEBfXx/f+ta3mDZt2uPrNm/ezPTp03sYlTQ0c512lflO0mTQX4T2F6T97wOLU6nfSC5R3A0c2vb5kGZdv5nAPODqJHcCLwRWDzaJh8+bUr+5c+eydu3a7datXbuWuXPn9igiyVyn0WG+kzRZHHTQQVQVAFXFQQcd1OOINJaNpBC9DjgiyeFJ9gROAVb3b6yqh6rqwKqaU1VzgGuBE6vKuZo1pKVLl3LyySdz+OGHM2XKFA4//HBOPvlkxxKol8x1GhXmO0mTxX/+53/yd3/3dzzyyCP83d/9Hf/5n//Z65A0hg1biFbVFuBNwOeBPlrPkbotydlJThztADXxJel1CJK5Tl1hvpM0kVUVb3nLW5gxYwZvectbHu8dlQaTXv0FmT9/fvmA28lr3rx5nHfeeSxcuPDxdWvWrOH000/n1ltv7WFk6rUk11fVhHk+p7lO5jsNxlyniWZHF9osSCevHeU6p7FST/T19bFgwYLt1i1YsIC+vr4eRSRJo8N8J0nSk1mIqifmzp3Lu9/97u0e8P7ud7/byTskTTjmO0mSnsxCVD2xcOFCzj33XE499VQefvhhTj31VM4999ztbl2TpInAfCdJ0pNZiKon1qxZw9ve9jZWrlzJzJkzWblyJW9729tYs2ZNr0OTpI4y30mS9GR79DoATU59fX0cdNBB3H777VQVt99+OwcddJBjpiRNOOY7SZKezB5R9cTee+/NF7/4RU477TQefPBBTjvtNL74xS+y99579zo0Seoo850kSU/m41vUE0mYNm0aAJs3b95u2Sm+JzcfaaCJxnynwZjrNNH4+BYNZke5zltz1TObN29m6tSpAGzbto2tW7f2OCJJGh3mO0mStuetueqZqVOncuihhzJlyhQOPfTQx0/SJGmiMd9JkrQ9C1H1zNatW7nvvvuoKu677z57CCRNWOY7SZK256256qmNGzdu9y5JE5X5TpKkJ9gjqp7qH9i+owHukjQRmO8kSXqChah6qn8WNWdTkzTRme8kSXqChagkSZK2k+TQJGuS3J7ktiRn9DomSROLY0TVU0moqsffJWmiMt9pnNkCvKWqbkgyE7g+yZVVdXuvA5M0Mdgjqp7yVjVJk4X5TuNJVd1TVTc0yw8DfcDBvY1K0kRiISpJkqQhJZkD/DrwjR6HImkCsRCVJEnSoJLsA3wK+LOq+umAbUuSrEuybsOGDb0JUNK4ZSEqSZKkJ0kyjVYRelFVfXrg9qpaXlXzq2r+rFmzuh+gpHHNQlSSJEnbSeuBtyuAvqp6X6/jkTTxWIhKkiRpoGOAPwRekuTG5vXyXgclaeLw8S2SJEnaTlWtBdLrOCRNXPaISpIkSZK6ykJUkiRJktRVFqKSJEmSpK6yEJUkSZIkdZWFqCRJkiSpqyxEJUmSJEldZSEqSZIkSeoqC1FJkiRJUldZiEqSJEmSuspCVJIkSZLUVRaikiRJkqSuGlEhmuT4JN9Jsj7JWYNs/4sktye5OcmXkhzW+VAlaXSZ6yRJkrpj2EI0yVTgA8AJwJHAoiRHDmj2LWB+VT0H+CTw3k4HKkmjyVwnSZLUPSPpET0aWF9Vd1TVY8DHgZPaG1TVmqr6WfPxWuCQzoYpSaPOXCdJktQlIylEDwZ+1Pb5rmbdUBYDVwy2IcmSJOuSrNuwYcPIo5Sk0WeukyRJ6pKOTlaU5A+A+cDfDra9qpZX1fyqmj9r1qxOHlqSusZcJ0mStHv2GEGbu4FD2z4f0qzbTpKXAkuBF1fVps6EJ0ldY66TJGkHknT8e1W1q+FonBtJj+h1wBFJDk+yJ3AKsLq9QZJfBz4EnFhV93Y+TEkadeY6SZJ2oKqGfI3G9zSxDVuIVtUW4E3A54E+4BNVdVuSs5Oc2DT7W2Af4JIkNyZZPcTuJGlMMtdJkrTr9tprr51aL43k1lyq6nLg8gHr3tG2/NIOxyVJXWeukyRp1zz66KNMnz6dTZueGLWy11578eijj/YwKo1lHZ2sSJIkSdLk9Oijj1JVHPa2z1JVFqHaIQtRSZIkbSfJyiT3Jrm117FImpgsRCVJkjTQBcDxvQ5C0sRlISpJkqTtVNU1wP29jkPSxGUhKkmSJEnqKgtRSZIk7bQkS5KsS7Juw4YNvQ5H0jhjISpJkqSdVlXLq2p+Vc2fNWtWr8ORNM5YiEqSJEmSuspCVJIkSdtJsgr4OvBrSe5KsrjXMUmaWPbodQCSJEkaW6pqUa9jkDSx2SMqSZIkSeoqC1FJkiRJUldZiEqSJEmSuspCVJIkSZLUVRaikiRJkqSushCVJEmSJHWVhagkSZIkqat8jqhGVZKOfqeqdiccSRoVu5LrdvQ9c50kaaKzENWoGupkymJT0kSyo7xlvpMk6cm8NVc9MdTJlydlkiYa850kSU9mj6h6pv8kbM5Zl3HnOa/ocTSSNHrMd5LGsue++ws89PPNHd3nnLMu68h+9t17Gje987iO7Etji4WoJEmSNIk99PPNY/YiWacKWo093porSZIkSeoqC1FJkiRJUldZiEqSJEmSuspCVJIkSZLUVRaikiRJkqSushCVJEmSJHWVj2/RLun086Y6OTW3z5uS1Ck+W0+SpNFhIapd4vOmJE0G5jpJkkaHt+ZKkiRJkrrKQlSSJEmS1FUjujU3yfHAPwJTgQ9X1TkDtu8FfAR4PvAT4OSqurOzoUrS6DLXSdIThsuJmjhmzj2LZ194Vq/DGNTMuQBjc4iEds+whWiSqcAHgJcBdwHXJVldVbe3NVsMPFBVT09yCnAucPJoBCxJo8FcJ0lPGGFO1ATxcN85jodX143k1tyjgfVVdUdVPQZ8HDhpQJuTgAub5U8Cv50knQtTkkaduU6SnjCSnChJu2wkhejBwI/aPt/VrBu0TVVtAR4CntqJACWpS8x1kvSEkeRESdplXX18S5IlwBKA2bNnd/PQ6jDHEkhDM9dNHOY6aWjmuollJLfA/uDcV3b8uIe97bM73L7v3tM6fkyNDSMpRO8GDm37fEizbrA2dyXZA9iX1kQe26mq5cBygPnz59euBKyx4ZbX39LrEKROM9fpScx1msSGzYnmuoljxONDz/E/szpnJLfmXgcckeTwJHsCpwCrB7RZDby+WX4NcFVV+TdV0nhirpOkJ4wkJ0rSLhu2R7SqtiR5E/B5WtN3r6yq25KcDayrqtXACuDfkqwH7qeVrCRp3DDXSdIThsqJPQ5L0gQyojGiVXU5cPmAde9oW34U+N3OhiZJ3WWuk6QnDJYTJalT0qu7ypJsAH7Qk4NrrDkQuK/XQWjMOKyqZvU6iE4x12kA8536mes0kZnr1G/IXNezQlTql2RdVc3vdRySNNrMd5ImA3OdRmIkkxVJkiRJktQxFqKSJEmSpK6yENVYsLzXAUhSl5jvJE0G5joNyzGikiRJkqSuskdUkiRJktRVFqKSJEmSpK6yENWIJTk2yX/rdRw7K8lRSV4+gnYbuxGPJI22JH81gjYXJHlNN+KRtHuSvDlJX5KLduG7Ozy/SXJnkluS3Jzky0kO2/VIeyPJnyV5yjBt3pXkrd2KScOzENXOOBYYtUI0yR6jtOujgGELUUkT1yjml92SZOoo7XrYQlTSuPK/gJdV1WtHaf8Lq+o5wNXA/x6lY4xmLv4zYIeFqMYeC9FJJsmlSa5PcluSJUmmNlfFb22uhv150+7NSW5vro59PMkc4DTgz5PcmOS3htj/BUnOT7IuyXeTvLJZPzXJ3ya5rtnnnzTrj03ylSSrgduH2OecJN9u9v3dJBcleWmSryb5XpKjm3ZHJ/l6km8l+VqSX0uyJ3A2cHIT98lJ9knyr21X/17ddqxlSW5Kcm2Sp3XuT17SaEvyuubf9E1J/q0tH30DeG9zd8S1TZvPJNm/+d52+a5Z9+ImZ9zY5JSZQxzz2CTXJLksyXea401pth3X5KQbklySZJ9m/Z1Jzk1yA/C7Q+z36iT/r8mlfUl+I8mnm5z3N23ttsvpzbpzgL2b2C8a7M+m7VAvavLlHbF3VBqTkpwP/ApwRZK3NP/ub27y2XOaNtv19jXndXMG7OegJl/d2Gwf7Fzu68DBO4il/5zsoiY3fTJNT2SS56fVo3p9ks8nOahZf3WSf0iyDjhjiP1ekOSDzW+6o8mtK5tjXNDW7oNNXrwtybubdW8GfhlYk2RNs+74JvfelORLbYc6sonnjuZ76qWq8jWJXsABzfvewK3A84Er27bv17z/GNhrwLp3AW8dZv8XAJ+jdZHjCOAuYDqwBPjfTZu9gHXA4bR6WR8BDt/BPucAW4BnN/u9HlgJBDgJuLRp9wvAHs3yS4FPNctvAN7ftr9zgX9o+7x/817A7zTL7+2P15cvX2P/BTwL+C5wYPP5gCYffRaY2qy7GXhxs3x2fx4YIt/9B3BMs7xPf24Z5LjHAo/SOkmcClwJvAY4ELgGmNG0exvwjmb5TuDMYX7P1cC5zfIZTYwHNfnzLuCp/b+zee/P6f3rN+7oz6Z5vwC4pMmrRwLre/3f0ZcvX4O/mrxxIHAe8M5m3UuAG5vld9F2jtbkgznN8sbm/S3A0mZ5KjCzfd/N8j8AS3YQx5zmfKk/P64E3gpMA74GzGrWnwysbJavBv55mN93AfBxnji3+ynbn/cd1bQ7oC3+q4HnDPIbZgE/ojm3bPvOu5oY92r+LH8CTOv1f9vJ/BqTtyppVL05yf9olg8F9gR+Jcl5wGXAF5ptNwMXJbkUuHQnj/GJqtoGfC/JHcAzgeOA57Rdcd+XVqH6GPDNqvr+MPv8flXdApDkNuBLVVVJbqGVFPv3eWGSI2glyWlD7OulwCn9H6rqgWbxMVonrdBKei8byY+VNCa8BLikqu4DqKr7k9Cs25pkX1pF5peb9hfSKsJg8Hz3VeB9TY/ip6vqrh0c+5tVdQdAklXAAlrF6ZHAV5s49qTV09Dv4hH8ptXN+y3AbVV1T3OMO2jl75/w5Jx+RLO+3ZP+bNq2Xdrk69vjXSDSeLAAeDVAVV2V5KlJfmGE370OWJlkGq1/+ze2bVuT5ABgI/B/htnPj6rqq83yR4E30+qEmAdc2eS8qcA9bd8ZSc77j7Zzu/8acN43B7gR+L3m7o89aF2cO5JWDm/3QuCa/nPLATnvsqraBGxKci/wNFoX99QD3po7iSQ5llYR9ptV9VzgW7SuCj2X1lWl04APN81fAXwAeB5wXXbunv6BD6ctWle4Tq+qo5rX4VXVX/Q+MoJ9bmpb3tb2eRs8fkHlr4E1VTUP+B1aPbE7Y3NV9ce+tW2/ksavkeSXJ+W7qjoH+J+0ehq/muSZO/j+UDnvyracd2RVLd7JuNrz3MAcuMcQOX1n8177frOT35U0dmxh+/P6J+WCqroGeBFwN3BBkte1bV4IHEar2Hv3MMcaKufd1pbznl1Vx7W16UTOO5xW7+tvV2s862XsXs7zXK/HLEQnl32BB6rqZ81J1Qtp3Zowpao+RWtw+vOa8U2HVtUaWreT7Uvr1rSHgUHHSQ3wu0mmJPlVWrerfQf4PPCnzVU4kjwjyYxR+H13N8tvaFs/MO4rgTf2f0gzTkzSuHYVrdzzVIDmyv7jquoh4IG2MVF/CHx5qHyX5Fer6paqOpdWL8KOCtGjkxze7OtkYC1wLXBMkqc38cxI8oyO/dqWwXJ6v839+ZZh/mwkjStfAV4Lj3cw3FdVP6V1a+rzmvXPozX8aTtpzYb7X1X1L7Q6Hp7Xvr2qttCa9Od1w+SJ2Ul+s1n+fVo57zvArP71SaYledYu/cKh/QKtgvah5g6OE9q2tZ/rXUtr/PvhTSzmvDHKQnRy+RytK0p9wDm0/qEeDFyd5EZat1e8ndbtFB9tbo34FvBPVfUgrTFT/yM7mKyo8UPgm8AVwGlV9SithHc7cEOSW4EP0fmrUO8F/m+Sbw3Y9xpag9NvTHIy8DfA/s1A/ZtoXQWUNI5V1W3AMlrF5U3A+wZp9nrgb5PcTGs27bMZOt/9WZMjbgY208pnQ7kOeD/QB3wf+ExVbaB1QWxVs4+vs+NidlcMltP7LQduTnLRCP9sJI0P7wKe3+SVc2jlNYBPAQc0t7G+ida48IGOBW5qzpNOBv5xYINmCMAq2i7YD+I7wBub3LM/8MGqeozW+PhzmzxzIx1+0kJV3UQrT38b+BitIRT9lgOfS7Kmyb9LgE83sYzktmD1QJ64E1Hafc3MZp+tqk/2OhZJGm1Nj8Rbq+qVPQ5FkkZdWjPxfrYZBiXtFntEJUmSJEldZY+odkmSpTz5+XeXVNWy3djnU4EvDbLpt6tq4CyQktQ1SZ4N/NuA1Zuq6gW7ud8PAMcMWP2PVfWvu7NfSdodo3VONhrnjxq/LEQlSZIkSV3lrbmSJEmSpK6yEJUkSZIkdZWFqCRJkiSpqyxEJUmSJEldZSEqSZIkSeoqC1FJkiRJUlft0asDH3jggTVnzpxeHV7SGHX99dffV1Wzeh1Hp5jrJA3GXCdpMthRrutZITpnzhzWrVvXq8NLGqOS/KDXMXSSuU7SYMx1kiaDHeW6YW/NTbIyyb1Jbh1ie5L8U5L1SW5O8rzdCVaSJEmSNLGNZIzoBcDxO9h+AnBE81oCfHD3w9JkkORJL0maiFatWsW8efOYOnUq8+bNY9WqVb0OSZKknhr21tyquibJnB00OQn4SFUVcG2S/ZIcVFX3dCpITTxDFZ1JaP1VkqSJYdWqVSxdupQVK1awYMEC1q5dy+LFiwFYtGhRj6OTJKk3OjFr7sHAj9o+39Wsk4ZVVY+/JGkiWrZsGStWrGDhwoVMmzaNhQsXsmLFCpYtW9br0CRJ6pmuTlaUZAmt23eZPXt2Nw8tSVJP9PX1sWDBgu3WLViwgL6+vh5FJEm7ZjSGUdkZMXl1okf0buDQts+HNOuepKqWV9X8qpo/a9aEmbFckqQhzZ07l7Vr1263bu3atcydO7dHEUnSrmm/k21Hr8Pe9tkRt9Xk1YlCdDXwumb23BcCDzk+VCPlREWSJrqlS5eyePFi1qxZw+bNm1mzZg2LFy9m6dKlvQ5NkqSeGfbW3CSrgGOBA5PcBbwTmAZQVecDlwMvB9YDPwP+aLSC1cRRVYMWn14ZkzTRLFq0iK997WuccMIJbNq0ib322os//uM/dqIiSdKkNmyPaFUtqqqDqmpaVR1SVSuq6vymCKVa3lhVv1pVz64qn2asYa1atYrDDz+cq666iscee4yrrrqKww8/3EcaSJpwVq1axWWXXcYVV1zBY489xhVXXMFll11mvpMkTWqduDVX2mnOIilpsjDfaaxJcmeSW5LcmORJHQhJjk3yULP9xiTv6EWckiY2C1H1RF9fH5dccgnTp08nCdOnT+eSSy5xFklJE46z5mqMWlhVR1XV/CG2f6XZflRVnd3VyCRNChai6on99tuP888/ny1btgCwZcsWzj//fPbbb7/eBiZJHeasuZIkPZmFqHrigQceoKpYsmQJDz74IEuWLKGqeOCBB3odmiR1lLPmagwq4AtJrm+e8T6Y30xyU5Irkjyrm8FJmhyGnTVXGg1VxSmnnMI111zDAQccwNy5cznllFP4+Mc/3uvQJKmj+mfHPf300+nr62Pu3LksW7bMWXPVSwuq6u4kvwhcmeTbVXVN2/YbgMOqamOSlwOXAkcM3ElTxC4BmD17dhfCljSR2COqnrnzzjtZv34927ZtY/369dx55529DkmTQJKVSe5NcusQ25Pkn5KsT3Jzkue1bXt9ku81r9d3L2qNd4sWLeLWW29l69at3HrrrRah6qmqurt5vxf4DHD0gO0/raqNzfLlwLQkBw6yn+VVNb+q5s+aNasLkUuaSCxE1TPXXnstmzZtAmDTpk1ce+21PY5Ik8QFwPE72H4CrSv/R9C60v9BgCQH0HqO8gtonbS9M8n+oxqpJHVYkhlJZvYvA8cBtw5o80tpHvad5Gha54s/6XaskiY2b81VTyWhqh5/l0ZbVV2TZM4OmpwEfKRafyGvTbJfkoOAY4Erq+p+gCRX0ipofRikpPHkacBnmjpzD+BjVfW5JKcBNM+Jfw3wp0m2AD8HTin/Jy2pwyxE1TP77bcfDz74INAaM9r+Weqhg4EftX2+q1k31PoncdyUpLGqqu4AnjvI+vPblt8PvL+bcUmafLw1Vz3z0EMP8fd///c88sgj/P3f/z0PPfRQr0OSOsJxU5IkSTtmIaqeGXiXj3f9aIy4Gzi07fMhzbqh1kuSJGknWYiqp97ylrcwY8YM3vKWt/Q6FKnfauB1zey5LwQeqqp7gM8DxyXZv5mk6LhmnSRJknaSY0QlTSpJVtGaeOjAJHfRmgl3Gjw+Rupy4OXAeuBnwB812+5P8tfAdc2uzu6fuEiSJEk7x0JUPTVlyhS2bdv2+Ls02qpqhw9wbGaGfOMQ21YCK0cjLkmSpMlkRLfmJjk+yXeaB7yfNcj22UnWJPlW8wD4l3c+VE1EzfTxj79LkiRJmviGLUSTTAU+QOsh70cCi5IcOaDZ/wY+UVW/DpwC/HOnA9XEtHXr1u3eJUmSJE18I+kRPRpYX1V3VNVjwMdpPfC9XQG/0CzvC/y4cyFKkiRJkiaSkYwRHewh7i8Y0OZdwBeSnA7MAF7akegkSZIkSRNOpx7fsgi4oKoOoTXb5L8ledK+kyxJsi7Jug0bNnTo0BrP9t9//+3eJUmSJE18IylER/IQ98XAJwCq6uvAdODAgTuqquVVNb+q5s+aNWvXItaEMWXKFH72s58B8LOf/YwpU3ysrSRJkjQZjOTM/zrgiCSHJ9mT1mREqwe0+SHw2wBJ5tIqRO3y1A5t27aNpzzlKdx888085SlP8fEtkiRJ0iQxbCFaVVuANwGfB/pozY57W5Kzk5zYNHsL8MdJbgJWAW9onsWnSS7JoK9+DzzwAM95znN44IEHhv2Oj3iRNF6tWrWKefPmMXXqVObNm8eqVat6HZIkST01onshq+ryqnpGVf1qVS1r1r2jqlY3y7dX1TFV9dyqOqqqvjCaQWv8qKpBXx/72MeYOXMm06ZNA2DatGnMnDmTj33sY0N+x2sbksajVatWccYZZ/DII48A8Mgjj3DGGWdYjKpnktyZ5JYkNyZZN8j2JPmn5vnxNyd5Xi/ilDSxOShPPbFo0SI+9KEP8YxnPAMyhWc84xl86EMfYtGiRb0OTZI66swzz2SPPfZg5cqVPProo6xcuZI99tiDM888s9ehaXJb2HQezB9k2wnAEc1rCfDBrkYmaVKwEFXPLFq0iFtvvZXDzlzNrbfeahEqaUK66667uPDCC1m4cCHTpk1j4cKFXHjhhdx11129Dk0ayknAR6rlWmC/JAf1OihJE4uFqCRJo+z9738/06dPJwnTp0/n/e9/f69D0uRWtJ7/fn2SJYNsH+wZ8gd3JTJJk4aFqCRJo2jGjBmsXr2aU089lQcffJBTTz2V1atXM2PGjF6HpslrQVU9j9YtuG9M8qJd2YnPh5e0OyxEJUkaRZs2bWKfffbhiiuuYP/99+eKK65gn332YdOmTb0OTZNUVd3dvN8LfAY4ekCTkTxD3ufDS9otFqKSJI2iLVu2cN555zFjxgySMGPGDM477zy2bNnS69A0CSWZkWRm/zJwHHDrgGargdc1s+e+EHioqu7pcqiSJrg9eh2AJEkT2V577cUXv/jF7dZ98YtfZK+99upRRJrkngZ8pnk29x7Ax6rqc0lOA6iq84HLgZcD64GfAX/Uo1glTWD2iEqadJIcn+Q7zTPyzhpk+/9rnq93Y5LvJnmwbdvWtm2ruxq4xqUXv/jFXHTRRbzoRS/i/vvv50UvehEXXXQRL37xi3sdmiahqrqjee77c6vqWW3Phz+/KUJpZst9Y/P8+GdX1ZOeNSpJu8seUUmTSpKpwAeAl9GaCfK6JKur6vb+NlX1523tTwd+vW0XP6+qo7oUriaAu+++m/nz53P++efzwQ9+kCTMnz+fu+9+0pA7SZImDXtEJU02RwPrm16Bx4CP03pm3lAWAau6EpkmpNtvv5377ruPL33pSzz22GN86Utf4r777uP2228f/suSJE1QFqKSJpsRPx8vyWHA4cBVbaunN48ruDbJq0YtSk0Ye+65J8cccwynn34606dP5/TTT+eYY45hzz337HVokiT1jIWoJA3tFOCTVbW1bd1hVTUf+H3gH5L86sAv+Ww9tdu0aRMXX3wxp556Kg8//DCnnnoqF198sY9vkSRNahaikiabET0fr3EKA27LbXv+3h3A1Ww/frS/jc/W0+P22msvfuVXfoW3vvWtzJgxg7e+9a38yq/8irPmSpImNQtRSZPNdcARSQ5PsietYvNJs98meSawP/D1tnX7J9mrWT4QOAZwoJ92aNOmTXz3u99lv/32Iwn77bcf3/3ud+0RlSRNaiMqRId71EHT5veS3J7ktiQf62yYktQZVbUFeBPweaAP+ERV3Zbk7CQntjU9Bfh4VVXburnAuiQ3AWuAc9pn25WGMm3aNDZu3EhVsXHjRqZNm9brkCRJ6qlhH98ykkcdJDkCeDtwTFU9kOQXRytgSdpdVXU5rQe2t697x4DP7xrke18Dnj2qwWlC2rJlC1OmtK79btu2jW3btvU4IkmSemskPaIjedTBHwMfqKoHAKrq3s6GKUnS+FVVbN3amvNq69atbN/RLknS5DOSQnQkjzp4BvCMJF9tHmlwfKcClCRJkiRNLMPemrsT+zkCOJbWDJTXJHl2VT3Y3ijJEmAJwOzZszt0aEmSJEnSeDKSHtGRPOrgLmB1VW2uqu8D36VVmG7HRxpIkiRJkkbSI/r4ow5oFaCn0HqQe7tLgUXAvzaPNHgGcEcH45QkaVxLQlU9/i5JY8Vz3/0FHvr55o7uc85Zl3VkP/vuPY2b3nlcR/alsWXYQrSqtiTpf9TBVGBl/6MOgHVVtbrZdlyS24GtwF9W1U9GM3BJksaT/uLTIlRjQfNUhHXA3VX1ygHb3gD8LU/cAff+qvpwdyNUNz30883cec4reh3GoDpV0GrsGdEY0eEeddA8Z+8vmpckSZLGtjNoPUv5F4bYfnFVvamL8UiaZEYyRlSSJEkTRJJDgFcA9nJK6hkLUUmSpMnlH4AzgW07aPPqJDcn+WSSQwdrkGRJknVJ1m3YsGE04pQ0gVmISpIkTRJJXgncW1XX76DZfwBzquo5wJXAhYM18mkIknaHhagkSdLkcQxwYpI7gY8DL0ny0fYGVfWTqtrUfPww8PzuhihpMrAQlSRJmiSq6u1VdUhVzaH1SL6rquoP2tskOajt44m0JjWSpI4a0ay5kiRJmrgGPJbvzUlOBLYA9wNv6GVskiYmC1FJkqRJqKquBq5ultsfy/d24O29iUrSZOGtuZIkSZKkrrIQlSRJkiR1lYWoJEmSJKmrLEQlTTpJjk/ynSTrk5w1yPY3JNmQ5Mbm9T/btr0+yfea1+u7G7kkSdLE4GRFkiaVJFOBDwAvA+4CrkuyuqpuH9D04qp604DvHgC8E5gPFHB9890HuhC6JEnShGGPqKTJ5mhgfVXdUVWP0Xqg+0kj/O5/B66sqvub4vNK4PhRilOSJGnCshCVNNkcDPyo7fNdzbqBXp3k5iSfTHLoTn5XkiRJO2AhKklP9h/AnKp6Dq1ezwt35stJliRZl2Tdhg0bRiVASZKk8WxEhehwE3u0tXt1kkoyv3MhSlJH3Q0c2vb5kGbd46rqJ1W1qfn4YeD5I/1u8/3lVTW/qubPmjWrY4FLkiRNFMMWom0Te5wAHAksSnLkIO1mAmcA3+h0kJLUQdcBRyQ5PMmewCnA6vYGSQ5q+3gi0Ncsfx44Lsn+SfYHjmvWSZIkaSeMpEd0pBN7/DVwLvBoB+OTpI6qqi3Am2gVkH3AJ6rqtiRnJzmxafbmJLcluQl4M/CG5rv308p11zWvs5t1kiRJ2gkjeXzLYJNzvKC9QZLnAYdW1WVJ/rKD8UlSx1XV5cDlA9a9o2357cDbh/juSmDlqAYoSZI0we32ZEVJpgDvA94ygrZO4CFJktRjSaYm+VaSzw6yba8kFzdzg3wjyZwehChpghtJj+hwk3PMBOYBVycB+CVgdZITq2pd+46qajmwHGD+/Pm1G3FLkiRp151Ba3jCLwyybTHwQFU9PckptIZendzN4NRdM+eexbMvHHI+0p6aORfgFb0OQ6NgJIXo4xN70CpATwF+v39jVT0EHNj/OcnVwFsHFqGSJEnqvSSH0DqzXwb8xSBNTgLe1Sx/Enh/klSVnQgT1MN953DnOWOz2Jtz1mW9DkGjZNhbc0c4sYckSZLGh38AzgS2DbH98flBmvPAh4CndiUySZPGSHpEh53YY8D6Y3c/LEmSJHVaklcC91bV9UmO3c19LQGWAMyePXv3g5M0qez2ZEWSJEkaN44BTkxyJ61H8r0kyUcHtHl8fpAkewD7Aj8ZuKOqWl5V86tq/qxZs0Y3akkTjoWoJEnSJFFVb6+qQ6pqDq15P66qqj8Y0Gw18Ppm+TVNG8eHSuqoEd2aK0mSpIkrydnAuqpaDawA/i3JeuB+WgWrJHWUhagkSdIkVFVXA1c3y+9oW/8o8Lu9iUrSZOGtuZIkSZKkrrJHVLvkue/+Ag/9fHPH9tfJZ0Ttu/c0bnrncR3bnyRJkqTOshDVLnno55t98LEkSZKkXeKtuZIkSZKkrrIQlSRJkiR1lYWoJEmSJKmrLEQlSZIkSV1lISpJkiRJ6ioLUUmTTpLjk3wnyfokZw2y/S+S3J7k5iRfSnJY27atSW5sXqu7G7kkSdLE4ONbJE0qSaYCHwBeBtwFXJdkdVXd3tbsW8D8qvpZkj8F3guc3Gz7eVUd1c2YJUmSJpoR9YjuTu+BJI0xRwPrq+qOqnoM+DhwUnuDqlpTVT9rPl4LHNLlGCVJkia0YQvRtt6DE4AjgUVJjhzQrL/34DnAJ2n1HkjSWHQw8KO2z3c164ayGLii7fP0JOuSXJvkVYN9IcmSps26DRs27HbAkiRJE81IekTtPZA0KSX5A2A+8Ldtqw+rqvnA7wP/kORXB36vqpZX1fyqmj9r1qwuRStJkjR+jKQQ3d3eA0kaS+4GDm37fEizbjtJXgosBU6sqk3966vq7ub9DuBq4NdHM1hJ6qQk05N8M8lNSW5L8u5B2rwhyYa2idn+Zy9ilTSxdXSyorbegxcPsX0JsARg9uzZnTy0JI3UdcARSQ6nVYCeQqt383FJfh34EHB8Vd3btn5/4GdVtSnJgcAxOBRB0viyCXhJVW1MMg1Ym+SKqrp2QLuLq+pNPYhP0iQxkkJ0Z3sPXtzee9CuqpYDywHmz59fOx2tJO2mqtqS5E3A54GpwMqqui3J2cC6qlpN61bcfYBLkgD8sKpOBOYCH0qyjdYdJecMmG1Xksa0qipgY/NxWvPynEzMOeuyXocwqH33ntbrEDRKRlKI7nLvgSSNRVV1OXD5gHXvaFt+6RDf+xrw7NGNTpJGVzMR5fXA04EPVNU3Bmn26iQvAr4L/HlV/WiQNpog7jznFR3d35yzLuv4PjXxDDtGtKq2AP29B33AJ/p7D5Kc2DRr7z3wIe+SJEljVFVtbZ6HfAhwdJJ5A5r8BzCneRrClcCFg+3HGcIl7Y4RjRHd1d4DSZIkjU1V9WCSNcDxwK1t63/S1uzDDDEW3iFXknbHSGbNlSRJO5BkyNeufE8aLUlmJdmvWd4beBnw7QFtDmr7eCKtO+IkqaM6OmuuJEmTUWv+l8HtqLDc0fekUXIQcGEzTnQKrSFXnx0wYdubm+FXW4D7gTf0LFpJE5aFqCRJ0iRRVTczyPOPBwy5ejvw9m7GJWny8dZcSZJG0VC9nvaGSpImMwtRSZJGWVVRVRz2ts8+vixJ0mRmISpJkiRJ6ioLUUmSJElSV1mISpIkSZK6ykJUkiRJktRVFqKSJEmSpK6yEJUkSZIkddUevQ5AkqSx6rnv/gIP/XxzR/c556zLOrKfffeexk3vPK4j+5IkqdssRCVJGsJDP9/Mnee8otdhDKpTBa0kSb3grbmSJEmSpK4aUSGa5Pgk30myPslZg2zfK8nFzfZvJJnT8UglqUN2J6cleXuz/jtJ/ntXA5ckSZoghi1Ek0wFPgCcABwJLEpy5IBmi4EHqurpwP8Dzu10oJLUCbuT05p2pwDPAo4H/rnZnyRJknbCSHpEjwbWV9UdVfUY8HHgpAFtTgIubJY/Cfx2knQuTEnqmN3JaScBH6+qTVX1fWB9sz9JGheSTE/yzSQ3JbktybsHaeOdbpJG3UgK0YOBH7V9vqtZN2ibqtoCPAQ8tRMBSlKH7U5OG8l3JWks2wS8pKqeCxwFHJ/khQPaeKebpFHX1VlzkywBlgDMnj27m4dWh82cexbPvvBJQ+vGhJlzAcbmLJeaHMx1E4e5ThNNVRWwsfk4rXnVgGYnAe9qlj8JvD9Jmu9qEtuZGx4zwssX/rWavEZSiN4NHNr2+ZBm3WBt7kqyB7Av8JOBO6qq5cBygPnz5/u3bhy75fW39DoEaVftTk4byXfNdROIuU4TUTO2/Xrg6cAHquobA5psd1dIkv67Qu7raqAacywa1UkjuTX3OuCIJIcn2ZPWRB2rB7RZDby+WX4NcJVXzSSNUbuT01YDpzTjpw4HjgC+2aW4JakjqmprVR1F62La0Unm7cp+kixJsi7Jug0bNnQ0RkkT37CFaDM+6k3A54E+4BNVdVuSs5Oc2DRbATw1yXrgL4CxeR+TpElvd3JaVd0GfAK4Hfgc8Maq2trt3yBJnVBVDwJraM0C3u7xuz+Gu9OtquZX1fxZs2aNcrSSJpoRjRGtqsuBywese0fb8qPA73Y2NEkaHbuT06pqGbBsVAOUpFGSZBawuaoeTLI38DKePBlR/10hX8c73SSNkq5OViRJkqSeOgi4sBknOoXWXSGfTXI2sK6qVtO6K+TfmrtC7qc1hEGSOiq9usCVZAPwg54cXGPNgTgBgp5wWFVNmHu8zHUawHynfuY6TWTmOvUbMtf1rBCV+iVZV1Xzex2HJI02852kycBcp5EYyay5kiRJkiR1jIWoJEmSJKmrLEQ1FizvdQCS1CXmO0mTgblOw3KMqCRJkiSpq+wRlSRJkiR1lYWoei7J/CT/tIPtv5zkk92MSZIkSdLosRBVxzUPyR6xqlpXVW/ewfYfV9Vrdj8ySdqxJO9K8tZex7G7ksxJ8vsjaHdnkgO7EZOk7kqysdcx9EuyX5L/NYJ2VyfxsS+ThIWodkpzcvPtJBcl6UvyySRPaU5mzk1yA/C7SY5L8vUkNyS5JMk+zfd/I8nXktyU5JtJZiY5Nslnm+0vTnJj8/pWs31Oklub7dOT/GuSW5rtC5v1b0jy6SSfS/K9JO/t2R+SJI1QWkbj/8VzgGELUUnqkv2AYQtRTS4WotoVvwb8c1XNBX7KE4nlJ1X1POCLwP8GXtp8Xgf8RZI9gYuBM6rqucBLgZ8P2PdbgTdW1VHAbw2y/Y1AVdWzgUXAhUmmN9uOAk4Gng2cnOTQDv1eSeNAkv+T5DtJ1iZZleStSY5Kcm2Sm5N8Jsn+Tds/TnJdc1HsU0meMsj+3pzk9ua7H9/Bcd+V5N+ai2/fS/LHbdv+sjnOzUne3ayb08T5EeBWYNBclWRjkr9NcluSLyY5uuktuCPJiW37+kpz0e+GJP+t+fo5wG81F/X+PMnUJH+X5NYmltPbDnV6891bkjxzJ//YJY1xzQWvv23+/d+S5ORm/ZQk/9x0MFyZ5PIkQ96B1nQ6/N8mr6xL8rwkn0/y/0tyWtNmnyRfasspJzVfPwf41ea7f9u0fVvT5qYk57Qd6nebzorvJvmtpu3U5jf059M/adYflOSaZr+39rfX+LBHrwPQuPSjqvpqs/xRoP+22oub9xcCRwJfTQKwJ/B1WgXsPVV1HUBV/RSgadPvq8D7klwEfLqq7hqwfQFwXvP9byf5AfCMZtuXquqhZp+3A4cBP+rED5Y0tiX5DeDVwHOBacANwPXAR4DTq+rLSc4G3gn8Ga388i/Nd/8GWEyTW9qcBRxeVZuS7DdMCM+hlftmAN9KchkwDzgCOBoIsDrJi4AfNutfX1XX7mCfM4Crquovk3wG+BvgZbTy64XAauBe4GVV9WiSI4BVwPwm9rdW1Sub3/intHpJj6qqLUkOaDvOfVX1vLRum3sr8D+H+a2Sxpf/j9bF+ucCBwLXJbkGOIZWXjgS+EWgD1g5zL5+WFVHJfl/wAXNPqbTuqh2PvAo8D+q6qdp3fZ/bZLVtHLSvKajgSQnACcBL6iqnw3ISXtU1dFJXk4rZ7+UVo5+qKp+I8letM4xv9D8ts9X1bK0hoY96aKixi4LUe2Kgc/86f/8SPMe4MqqWtTeKMmzh91x1TnNCdzLaSWZ/04rqY3Eprblrfj3W5pMjgH+vaoeBR5N8h+0Crn9qurLTZsLgUua5XlNAbofsA/w+UH2eTNwUZJLgUuHOf6/V9XPgZ8nWUOr+FwAHAd8q2mzD60C9IfAD4YpQgEeAz7XLN8CbKqqzUluoXXyCK2i+/1JjqKV954xcCeNlwLnV9UWgKq6v23bp5v362md1EmaWBYAq6pqK/BfSb4M/Eaz/pKq2gb8Z5O7hrO6eb8F2KeqHgYeTtJ/we4R4D3NRbdtwMHA0wbZz0uBf62qn8EOc9KcZvk44DltPbb70sqn1wErk0wDLq2qG0fwGzRGeGuudsXsJL/ZLP8+sHbA9muBY5I8HSDJjCTPAL4DHNT0XJDW+M/tisUkv1pVt1TVubSSy8DbxL4CvLZp+wxgdrNfSdoZFwBvam7zfzetK/oDvQL4APA8Wj0IO7q4NdgFugD/t6qOal5Pr6oVzfZHGN7meuJh39toLrY1J439sfw58F+0ejrm07oDZWf1X8TzAp6k4fTni21s3wHQn5deC8wCnt/0fv4Xg+fXkRyjPSeF1t0t/fn08Kr6QlVdA7wIuBu4IMnrdvYHqXcsRLUrvgO8MUkfsD/wwfaNVbUBeAOwKsnNtG7LfWZVPUZrDOd5SW4CruTJyenP+scwAZuBKwZs/2dgStMjcDHwhqrahKTJ7qvA76Q1odk+wCtpFXsPtI0Z+kOgv3d0JnBPcxX9tQN3ltYEQodW1RrgbbSuvu+zg+Of1Bz7qcCxtC6kfR44NU9M1nZwkl/czd850L60hjxso/X7+mctf5jWb+x3JfAn/cX0gNvgJE1sX6E1d8bUJLNoFW7fpJU3X92MFX0ardy1u/YF7m3u3lhIa5gUDJ6T/ijN+PwR5KTPA3/a5GySPKPp6DgM+K9mqMWHaV041DjhlU/tii1V9QcD1s1p/1BVV9G67YMB66+jNY6q3dXNi6o6nSe7k9ZYK5rb7v5okP1eQKuHo//zK3cQv6QJpqqua8Yh3UzrCvwtwEPA64Hzm5OdO3gif/wf4BvAhuZ95oBdTgU+mmRfWlfi/6mqHtxBCDcDa2iNv/rrqvox8OMkc4GvN2PdNwJ/QOsqf6f8M/CpphfgczzR03ozsLW56HcBrfGvzwBuTrIZ+Bfg/R2MQ9LY9RngN4GbaN2tcWZV/WeSTwG/DdxOa06NG2jlzd1xEfAfTYfBOuDbAFX1kyRfTespCFc0Y9+PAtYleQy4HPirHez3w7TONW9IK6FuAF5Fq3j+yyavbQTsER1H8sRdP9LwkswBPltV83odiyS1S7JPVW1sis5rgCVVdUMXjvsuYGNV/d1oH0uSOqktbz6VVi/pMVX1n72OS5ODPaLaKVV1J03vpCSNMcuTHEnrlv8Lu1GEStI499lmkqE9ad3NYRGqrrFHVJKkEUjyR8AZA1Z/tareuJv7/Qaw14DVf1hVt+zOfiVpVzSPizp8wOq3VdVgs4tLu8xCVJIkSZLUVc6aK0mSJEnqKgtRSZIkSVJXWYhKkiRJkrrKQlSSJEmS1FUWopIkSZKkrvr/A2jz4BTtiTY+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1152x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots de los atributos\n",
    "fig, ax = plt.subplots(3,3, figsize = (16,8))\n",
    "ax = ax.flatten()\n",
    "for i, col in enumerate(player_features.columns[3:]):\n",
    "    player_features[col].plot(kind=\"box\", ax = ax[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus\n",
    "¿Cuál es tu sugerencia de jugadores para armar el mejor equipo de la historia? Argumenta tu respuesta.\n",
    "\n",
    "Como **primera aproximación**, y sin ocupar los datos anteriormente creados se propone lo siguiente:\n",
    "\n",
    "Se puede notar en la tabla Player, que existe una columna denominada *overall_ranking* es decir, una métrica que dice qué tan bueno es el jugador, como primera solución, puede ser considerar los mejores jugadores de acuerdo a esta métrica y construir el equipo de acuerdo a tal criterio, para ello:\n",
    "\n",
    "* Se realiza una consulta para identificar cuáles han sido los mejores jugadores\n",
    "* Se plantea una formación, en este caso simplemente se asumirá un 4-3-3 (4 defensas, 3 mediocampistas y 3 delanteros)\n",
    "* Se busca a partir de la consulta realizada los 3 mejores delanteros (escogiendo un delantero izquierdo, uno derecho y un delantero centro) y se realiza lo analogo para los mediocampistas, defensas y arquero.\n",
    "\n",
    "Se pudo notar que además este *overall_score* existe para diferente epocas del año, debido a que se quiere encontrar el mejor equipo de la historia, se escogerá el mejor *overall_score* historico de cada jugador."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>MAX(P.overall_rating)</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30981</td>\n",
       "      <td>2015-12-17 00:00:00</td>\n",
       "      <td>94</td>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30893</td>\n",
       "      <td>2015-10-16 00:00:00</td>\n",
       "      <td>93</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30717</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>93</td>\n",
       "      <td>Gianluigi Buffon</td>\n",
       "      <td>ARQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30829</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>93</td>\n",
       "      <td>Wayne Rooney</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39989</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>92</td>\n",
       "      <td>Gregory Coupet</td>\n",
       "      <td>ARQ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_api_id                 date  MAX(P.overall_rating)  \\\n",
       "0          30981  2015-12-17 00:00:00                     94   \n",
       "1          30893  2015-10-16 00:00:00                     93   \n",
       "2          30717  2007-02-22 00:00:00                     93   \n",
       "3          30829  2007-02-22 00:00:00                     93   \n",
       "4          39989  2007-02-22 00:00:00                     92   \n",
       "\n",
       "         player_name y_position  \n",
       "0       Lionel Messi        DEL  \n",
       "1  Cristiano Ronaldo        DEL  \n",
       "2   Gianluigi Buffon        ARQ  \n",
       "3       Wayne Rooney        DEL  \n",
       "4     Gregory Coupet        ARQ  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_scores = query(\"\"\"\n",
    "      SELECT P.player_api_id, P.date, MAX(P.overall_rating), B.player_name\n",
    "      FROM Player_Attributes P\n",
    "      LEFT JOIN Player B\n",
    "      on P.player_api_id=B.player_api_id\n",
    "      GROUP BY player_name\n",
    "      ORDER BY overall_rating DESC\n",
    "      \"\"\")\n",
    "best_scores = best_scores.merge(y_position_mean, how=\"inner\", on=\"player_api_id\")\n",
    "best_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>MAX(P.overall_rating)</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30717</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>93</td>\n",
       "      <td>Gianluigi Buffon</td>\n",
       "      <td>ARQ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_api_id                 date  MAX(P.overall_rating)  \\\n",
       "2          30717  2007-02-22 00:00:00                     93   \n",
       "\n",
       "        player_name y_position  \n",
       "2  Gianluigi Buffon        ARQ  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mejor arquero\n",
    "best_scores[best_scores.y_position==\"ARQ\"].sort_values(\"MAX(P.overall_rating)\", ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>MAX(P.overall_rating)</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30723</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>91</td>\n",
       "      <td>Alessandro Nesta</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30627</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>91</td>\n",
       "      <td>John Terry</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>34520</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>91</td>\n",
       "      <td>Fabio Cannavaro</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30661</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>89</td>\n",
       "      <td>Carles Puyol</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30931</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>89</td>\n",
       "      <td>Cris</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>39774</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>89</td>\n",
       "      <td>Lucio</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>30865</td>\n",
       "      <td>2012-08-31 00:00:00</td>\n",
       "      <td>89</td>\n",
       "      <td>Nemanja Vidic</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>30716</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>88</td>\n",
       "      <td>Marco Materazzi</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>80562</td>\n",
       "      <td>2015-09-21 00:00:00</td>\n",
       "      <td>88</td>\n",
       "      <td>Thiago Silva</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>30620</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>88</td>\n",
       "      <td>Rio Ferdinand</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>30617</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>88</td>\n",
       "      <td>Jamie Carragher</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>27301</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>88</td>\n",
       "      <td>Marcelo Jose Bordon</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>37482</td>\n",
       "      <td>2012-02-22 00:00:00</td>\n",
       "      <td>88</td>\n",
       "      <td>Gerard Pique</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>36183</td>\n",
       "      <td>2016-01-14 00:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>Jerome Boateng</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>30962</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>Sergio Ramos</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>30894</td>\n",
       "      <td>2016-01-28 00:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>Philipp Lahm</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>35606</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>Per Mertesacker</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>26564</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>Alex</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>41884</td>\n",
       "      <td>2015-11-12 00:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>Giorgio Chiellini</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>33085</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>Daniel van Buyten</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>30649</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>Christoph Metzelder</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>34305</td>\n",
       "      <td>2010-08-30 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Maicon</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>39027</td>\n",
       "      <td>2015-03-20 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Vincent Kompany</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>34005</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Roberto Fabian Ayala</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>30911</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Ricardo Carvalho</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>30740</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Rafael Marquez</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>36388</td>\n",
       "      <td>2016-01-14 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Mats Hummels</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>33988</td>\n",
       "      <td>2012-02-22 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Daniel Alves</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>30831</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Kolo Toure</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>26098</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Kakha Kaladze</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>38818</td>\n",
       "      <td>2013-02-15 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Javier Mascherano</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>34534</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Gianluca Zambrotta</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>30891</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Gabriel Milito</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>56678</td>\n",
       "      <td>2016-01-28 00:00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>Diego Godin</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>19327</td>\n",
       "      <td>2016-05-12 00:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>Miranda</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>38835</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>William Gallas</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>30492</td>\n",
       "      <td>2011-08-30 00:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>Walter Samuel</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>41060</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>Stephen McManus</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>30453</td>\n",
       "      <td>2008-08-30 00:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>Phillippe Mexes</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>25921</td>\n",
       "      <td>2012-08-31 00:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>Pepe</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>32569</td>\n",
       "      <td>2011-08-30 00:00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>Patrice Evra</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     player_api_id                 date  MAX(P.overall_rating)  \\\n",
       "6            30723  2007-08-30 00:00:00                     91   \n",
       "10           30627  2007-02-22 00:00:00                     91   \n",
       "8            34520  2007-02-22 00:00:00                     91   \n",
       "26           30661  2007-02-22 00:00:00                     89   \n",
       "28           30931  2007-08-30 00:00:00                     89   \n",
       "33           39774  2007-02-22 00:00:00                     89   \n",
       "34           30865  2012-08-31 00:00:00                     89   \n",
       "57           30716  2007-08-30 00:00:00                     88   \n",
       "64           80562  2015-09-21 00:00:00                     88   \n",
       "61           30620  2007-08-30 00:00:00                     88   \n",
       "50           30617  2007-08-30 00:00:00                     88   \n",
       "56           27301  2007-08-30 00:00:00                     88   \n",
       "48           37482  2012-02-22 00:00:00                     88   \n",
       "82           36183  2016-01-14 00:00:00                     87   \n",
       "99           30962  2016-03-31 00:00:00                     87   \n",
       "93           30894  2016-01-28 00:00:00                     87   \n",
       "92           35606  2007-08-30 00:00:00                     87   \n",
       "68           26564  2007-02-22 00:00:00                     87   \n",
       "80           41884  2015-11-12 00:00:00                     87   \n",
       "70           33085  2007-02-22 00:00:00                     87   \n",
       "69           30649  2007-02-22 00:00:00                     87   \n",
       "133          34305  2010-08-30 00:00:00                     86   \n",
       "157          39027  2015-03-20 00:00:00                     86   \n",
       "148          34005  2007-02-22 00:00:00                     86   \n",
       "145          30911  2007-08-30 00:00:00                     86   \n",
       "143          30740  2007-02-22 00:00:00                     86   \n",
       "136          36388  2016-01-14 00:00:00                     86   \n",
       "110          33988  2012-02-22 00:00:00                     86   \n",
       "130          30831  2007-08-30 00:00:00                     86   \n",
       "128          26098  2007-08-30 00:00:00                     86   \n",
       "124          38818  2013-02-15 00:00:00                     86   \n",
       "121          34534  2007-02-22 00:00:00                     86   \n",
       "120          30891  2007-08-30 00:00:00                     86   \n",
       "114          56678  2016-01-28 00:00:00                     86   \n",
       "203          19327  2016-05-12 00:00:00                     85   \n",
       "225          38835  2007-08-30 00:00:00                     85   \n",
       "224          30492  2011-08-30 00:00:00                     85   \n",
       "219          41060  2007-08-30 00:00:00                     85   \n",
       "215          30453  2008-08-30 00:00:00                     85   \n",
       "213          25921  2012-08-31 00:00:00                     85   \n",
       "210          32569  2011-08-30 00:00:00                     85   \n",
       "\n",
       "              player_name y_position  \n",
       "6        Alessandro Nesta         DF  \n",
       "10             John Terry         DF  \n",
       "8         Fabio Cannavaro         DF  \n",
       "26           Carles Puyol         DF  \n",
       "28                   Cris         DF  \n",
       "33                  Lucio         DF  \n",
       "34          Nemanja Vidic         DF  \n",
       "57        Marco Materazzi         DF  \n",
       "64           Thiago Silva         DF  \n",
       "61          Rio Ferdinand         DF  \n",
       "50        Jamie Carragher         DF  \n",
       "56    Marcelo Jose Bordon         DF  \n",
       "48           Gerard Pique         DF  \n",
       "82         Jerome Boateng         DF  \n",
       "99           Sergio Ramos         DF  \n",
       "93           Philipp Lahm         DF  \n",
       "92        Per Mertesacker         DF  \n",
       "68                   Alex         DF  \n",
       "80      Giorgio Chiellini         DF  \n",
       "70      Daniel van Buyten         DF  \n",
       "69    Christoph Metzelder         DF  \n",
       "133                Maicon         DF  \n",
       "157       Vincent Kompany         DF  \n",
       "148  Roberto Fabian Ayala         DF  \n",
       "145      Ricardo Carvalho         DF  \n",
       "143        Rafael Marquez         DF  \n",
       "136          Mats Hummels         DF  \n",
       "110          Daniel Alves         DF  \n",
       "130            Kolo Toure         DF  \n",
       "128         Kakha Kaladze         DF  \n",
       "124     Javier Mascherano         DF  \n",
       "121    Gianluca Zambrotta         DF  \n",
       "120        Gabriel Milito         DF  \n",
       "114           Diego Godin         DF  \n",
       "203               Miranda         DF  \n",
       "225        William Gallas         DF  \n",
       "224         Walter Samuel         DF  \n",
       "219       Stephen McManus         DF  \n",
       "215       Phillippe Mexes         DF  \n",
       "213                  Pepe         DF  \n",
       "210          Patrice Evra         DF  "
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mejores defensas\n",
    "best_scores[best_scores.y_position==\"DF\"].sort_values(\"MAX(P.overall_rating)\", ascending=False).head(41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>MAX(P.overall_rating)</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39854</td>\n",
       "      <td>2011-08-30 00:00:00</td>\n",
       "      <td>92</td>\n",
       "      <td>Xavi Hernandez</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30955</td>\n",
       "      <td>2012-02-22 00:00:00</td>\n",
       "      <td>91</td>\n",
       "      <td>Andres Iniesta</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30834</td>\n",
       "      <td>2015-09-25 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Arjen Robben</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30924</td>\n",
       "      <td>2014-02-28 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Franck Ribery</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30631</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Frank Lampard</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>41044</td>\n",
       "      <td>2008-08-30 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Kaka</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>38843</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Ze Roberto</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30613</td>\n",
       "      <td>2012-02-22 00:00:00</td>\n",
       "      <td>89</td>\n",
       "      <td>Cesc Fabregas</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>30618</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>89</td>\n",
       "      <td>Steven Gerrard</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>30696</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>88</td>\n",
       "      <td>Luis Figo</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    player_api_id                 date  MAX(P.overall_rating)     player_name  \\\n",
       "5           39854  2011-08-30 00:00:00                     92  Xavi Hernandez   \n",
       "7           30955  2012-02-22 00:00:00                     91  Andres Iniesta   \n",
       "13          30834  2015-09-25 00:00:00                     90    Arjen Robben   \n",
       "16          30924  2014-02-28 00:00:00                     90   Franck Ribery   \n",
       "17          30631  2007-02-22 00:00:00                     90   Frank Lampard   \n",
       "18          41044  2008-08-30 00:00:00                     90            Kaka   \n",
       "23          38843  2007-02-22 00:00:00                     90      Ze Roberto   \n",
       "27          30613  2012-02-22 00:00:00                     89   Cesc Fabregas   \n",
       "38          30618  2007-08-30 00:00:00                     89  Steven Gerrard   \n",
       "54          30696  2007-02-22 00:00:00                     88       Luis Figo   \n",
       "\n",
       "   y_position  \n",
       "5          MC  \n",
       "7          MC  \n",
       "13         MC  \n",
       "16         MC  \n",
       "17         MC  \n",
       "18         MC  \n",
       "23         MC  \n",
       "27         MC  \n",
       "38         MC  \n",
       "54         MC  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_scores[best_scores.y_position==\"MC\"].sort_values(\"MAX(P.overall_rating)\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>MAX(P.overall_rating)</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30981</td>\n",
       "      <td>2015-12-17 00:00:00</td>\n",
       "      <td>94</td>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30829</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>93</td>\n",
       "      <td>Wayne Rooney</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30893</td>\n",
       "      <td>2015-10-16 00:00:00</td>\n",
       "      <td>93</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30743</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>91</td>\n",
       "      <td>Ronaldinho</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30626</td>\n",
       "      <td>2007-08-30 00:00:00</td>\n",
       "      <td>91</td>\n",
       "      <td>Thierry Henry</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19533</td>\n",
       "      <td>2016-02-04 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Neymar</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22543</td>\n",
       "      <td>2013-09-20 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Radamel Falcao</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>35724</td>\n",
       "      <td>2014-10-17 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Zlatan Ibrahimovic</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40636</td>\n",
       "      <td>2015-10-16 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Luis Suarez</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30714</td>\n",
       "      <td>2007-02-22 00:00:00</td>\n",
       "      <td>90</td>\n",
       "      <td>Francesco Totti</td>\n",
       "      <td>DEL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    player_api_id                 date  MAX(P.overall_rating)  \\\n",
       "0           30981  2015-12-17 00:00:00                     94   \n",
       "3           30829  2007-02-22 00:00:00                     93   \n",
       "1           30893  2015-10-16 00:00:00                     93   \n",
       "11          30743  2007-08-30 00:00:00                     91   \n",
       "12          30626  2007-08-30 00:00:00                     91   \n",
       "21          19533  2016-02-04 00:00:00                     90   \n",
       "22          22543  2013-09-20 00:00:00                     90   \n",
       "24          35724  2014-10-17 00:00:00                     90   \n",
       "19          40636  2015-10-16 00:00:00                     90   \n",
       "15          30714  2007-02-22 00:00:00                     90   \n",
       "\n",
       "           player_name y_position  \n",
       "0         Lionel Messi        DEL  \n",
       "3         Wayne Rooney        DEL  \n",
       "1    Cristiano Ronaldo        DEL  \n",
       "11          Ronaldinho        DEL  \n",
       "12       Thierry Henry        DEL  \n",
       "21              Neymar        DEL  \n",
       "22      Radamel Falcao        DEL  \n",
       "24  Zlatan Ibrahimovic        DEL  \n",
       "19         Luis Suarez        DEL  \n",
       "15     Francesco Totti        DEL  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_scores[best_scores.y_position==\"DEL\"].sort_values(\"MAX(P.overall_rating)\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al identificar las posiciones de los jugadores se concluye que el mejor equipo, siguiendo tal metodología corresponde a:\n",
    "\n",
    "Delanteros: Cristiano Ronaldo, Luis Suarez, Lionel Messi\n",
    "\n",
    "Medio Campo: Iniesta, Lampard, Xavi Hernandez\n",
    "\n",
    "Defensas: Patrice Evra, Alejandro Nesta, Cannavaro, Philip Lahm\n",
    "\n",
    "Arquero: Buffon\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como **Segunda aproximación**, es posible considerar todos lo datos que se tienen hasta la fecha, de manera de encontrar los jugadores que en los años que se tienen registrados, tienen las mejores estadisticas. Esto tiene una desventaja, pues puede suceder que exista un jugador que haya tenido una excelente temporada, pero que en el resto de los años haya jugado mal.\n",
    "\n",
    "Para esta solución del problema, se hace tremendamente útil la tabla creada **player_features**, pues resume caracteristicas interesantes de los jugadores desde que se tiene registro.\n",
    "\n",
    "La manera en que se evaluara cada jugador depende de la posición en que juege, esto se define a partir de la función score, la cual recibe una fila de la tabla *player_features* y retorna su evaluación, en la siguiente celda se define,\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score(X):\n",
    "    if X.y_position == \"ARQ\":\n",
    "        score = (-0.8*X.goalsR_per_match + 0.2*X.blocks_per_match)/(X.log_matches+0.1)\n",
    "    elif X.y_position == \"DF\":\n",
    "        score = (-0.8*X.goalsR_per_match-0.2*X.fouls_per_match)/(X.log_matches+0.1)\n",
    "    elif X.y_position == \"MC\":\n",
    "        score = (0.3*X.asst_per_match - 0.2*X.goalsR_per_match+0.3*X.goals_per_match+0.1*X.cross_per_match+0.1*X.foulsR_per_match)*(X.log_matches+0.1)\n",
    "    elif X.y_position == \"DEL\":\n",
    "        score = (0.1*X.asst_per_match + 0.7*X.goals_per_match + 0.1*X.precision + 0.1*X.foulsR_per_match)*(X.log_matches+0.1)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En general se le da un peso a cada atributo dependiendo de la importancia que tiene para la posición, en caso de ser un atributo negativo (goles recibidos) se le da un peso negativo. Notar que se multiplica/divide (dependiendo si mientras más alto es el score mejor, o al revés) por (log_matches+0.1) que de alguna manera nos dice la experiencia que tiene el jugador.\n",
    "\n",
    "Por último para que las features sean comparables, se normalizan entre 0 y 1 utilizando la función minmax_scale de sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>goalsR_per_match</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>155782</td>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.521505</td>\n",
       "      <td>0.079700</td>\n",
       "      <td>0.142955</td>\n",
       "      <td>0.188718</td>\n",
       "      <td>0.188718</td>\n",
       "      <td>0.250435</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.047092</td>\n",
       "      <td>0.478692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162549</td>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.005155</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.601622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23780</td>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.497810</td>\n",
       "      <td>0.083647</td>\n",
       "      <td>0.061219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.114332</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.005450</td>\n",
       "      <td>0.768287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27316</td>\n",
       "      <td>Aaron Hunt</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.655880</td>\n",
       "      <td>0.057318</td>\n",
       "      <td>0.101788</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.184920</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.212362</td>\n",
       "      <td>0.758885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30895</td>\n",
       "      <td>Aaron Lennon</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.494564</td>\n",
       "      <td>0.084188</td>\n",
       "      <td>0.145127</td>\n",
       "      <td>0.342116</td>\n",
       "      <td>0.342116</td>\n",
       "      <td>0.335816</td>\n",
       "      <td>0.189781</td>\n",
       "      <td>0.126837</td>\n",
       "      <td>0.809990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_api_id      player_name y_position  goalsR_per_match  \\\n",
       "1         155782  Aaron Cresswell         DF          0.521505   \n",
       "2         162549      Aaron Doran         MC          0.000000   \n",
       "4          23780     Aaron Hughes         DF          0.497810   \n",
       "5          27316       Aaron Hunt         MC          0.655880   \n",
       "7          30895     Aaron Lennon         MC          0.494564   \n",
       "\n",
       "   blocks_per_match  fouls_per_match  asst_per_match  cross_per_match  \\\n",
       "1          0.079700         0.142955        0.188718         0.188718   \n",
       "2          0.166565         0.005155        0.000000         0.000000   \n",
       "4          0.083647         0.061219        0.000000         0.000000   \n",
       "5          0.057318         0.101788        0.123174         0.123174   \n",
       "7          0.084188         0.145127        0.342116         0.342116   \n",
       "\n",
       "   foulsR_per_match  precision  goals_per_match  log_matches  \n",
       "1          0.250435   0.153846         0.047092     0.478692  \n",
       "2          0.012542   0.000000         0.000000     0.601622  \n",
       "4          0.114332   0.076923         0.005450     0.768287  \n",
       "5          0.184920   0.447059         0.212362     0.758885  \n",
       "7          0.335816   0.189781         0.126837     0.809990  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar solo las columnas númericas\n",
    "player_features_num = player_features.select_dtypes(['int', 'float'])\n",
    "\n",
    "# Normalizarlas\n",
    "player_features_norm = minmax_scale(player_features_num)\n",
    "\n",
    "# Reemplazar los valores no normalizados por los normalizados\n",
    "player_features[player_features_num.columns] = player_features_norm\n",
    "\n",
    "player_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se calcula ahora sí el score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_features[\"score\"] = player_features.apply(score, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se buscan los mejores jugadores de forma analoga a lo realizado en la aproximación anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>goalsR_per_match</th>\n",
       "      <th>blocks_per_match</th>\n",
       "      <th>fouls_per_match</th>\n",
       "      <th>asst_per_match</th>\n",
       "      <th>cross_per_match</th>\n",
       "      <th>foulsR_per_match</th>\n",
       "      <th>precision</th>\n",
       "      <th>goals_per_match</th>\n",
       "      <th>log_matches</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10678</th>\n",
       "      <td>210046</td>\n",
       "      <td>Vladan Giljen</td>\n",
       "      <td>ARQ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.33313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6804</th>\n",
       "      <td>40186</td>\n",
       "      <td>Mario Felgueiras</td>\n",
       "      <td>ARQ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.33313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7114</th>\n",
       "      <td>110382</td>\n",
       "      <td>Matias Omar Degra</td>\n",
       "      <td>ARQ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.33313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>16199</td>\n",
       "      <td>Boban Bajkovic</td>\n",
       "      <td>ARQ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017494</td>\n",
       "      <td>0.28353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>37839</td>\n",
       "      <td>Bart Deelkens</td>\n",
       "      <td>ARQ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017494</td>\n",
       "      <td>0.28353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       player_api_id        player_name y_position  goalsR_per_match  \\\n",
       "10678         210046      Vladan Giljen        ARQ               0.0   \n",
       "6804           40186   Mario Felgueiras        ARQ               0.0   \n",
       "7114          110382  Matias Omar Degra        ARQ               0.0   \n",
       "1290           16199     Boban Bajkovic        ARQ               0.0   \n",
       "1113           37839      Bart Deelkens        ARQ               0.0   \n",
       "\n",
       "       blocks_per_match  fouls_per_match  asst_per_match  cross_per_match  \\\n",
       "10678          0.166565              0.0             0.0              0.0   \n",
       "6804           0.166565              0.0             0.0              0.0   \n",
       "7114           0.166565              0.0             0.0              0.0   \n",
       "1290           0.166565              0.0             0.0              0.0   \n",
       "1113           0.166565              0.0             0.0              0.0   \n",
       "\n",
       "       foulsR_per_match  precision  goals_per_match  log_matches    score  \n",
       "10678               0.0        0.0              0.0     0.000000  0.33313  \n",
       "6804                0.0        0.0              0.0     0.000000  0.33313  \n",
       "7114                0.0        0.0              0.0     0.000000  0.33313  \n",
       "1290                0.0        0.0              0.0     0.017494  0.28353  \n",
       "1113                0.0        0.0              0.0     0.017494  0.28353  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_features[player_features.y_position==\"ARQ\"].sort_values(\"score\", ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>goalsR_per_match</th>\n",
       "      <th>blocks_per_match</th>\n",
       "      <th>fouls_per_match</th>\n",
       "      <th>asst_per_match</th>\n",
       "      <th>cross_per_match</th>\n",
       "      <th>foulsR_per_match</th>\n",
       "      <th>precision</th>\n",
       "      <th>goals_per_match</th>\n",
       "      <th>log_matches</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3578</th>\n",
       "      <td>38626</td>\n",
       "      <td>Frederic Page</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.597988</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2623</th>\n",
       "      <td>303227</td>\n",
       "      <td>Diego Galo</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.555518</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9338</th>\n",
       "      <td>94467</td>\n",
       "      <td>Ryan McGowan</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.502961</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8046</th>\n",
       "      <td>39627</td>\n",
       "      <td>Nuno Andre Coelho</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.407110</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9309</th>\n",
       "      <td>32411</td>\n",
       "      <td>Russell Anderson</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.413127</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2665</th>\n",
       "      <td>148313</td>\n",
       "      <td>Dimitri Daeselaire</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223018</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9293</th>\n",
       "      <td>500618</td>\n",
       "      <td>Rui Correia</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.302951</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2678</th>\n",
       "      <td>277848</td>\n",
       "      <td>Dino Arslanagic</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223018</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8051</th>\n",
       "      <td>164470</td>\n",
       "      <td>Nuno Henrique</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.146456</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2697</th>\n",
       "      <td>139898</td>\n",
       "      <td>Dirceu</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.065565</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      player_api_id         player_name y_position  goalsR_per_match  \\\n",
       "3578          38626       Frederic Page         DF               0.0   \n",
       "2623         303227          Diego Galo         DF               0.0   \n",
       "9338          94467        Ryan McGowan         DF               0.0   \n",
       "8046          39627   Nuno Andre Coelho         DF               0.0   \n",
       "9309          32411    Russell Anderson         DF               0.0   \n",
       "2665         148313  Dimitri Daeselaire         DF               0.0   \n",
       "9293         500618         Rui Correia         DF               0.0   \n",
       "2678         277848     Dino Arslanagic         DF               0.0   \n",
       "8051         164470       Nuno Henrique         DF               0.0   \n",
       "2697         139898              Dirceu         DF               0.0   \n",
       "\n",
       "      blocks_per_match  fouls_per_match  asst_per_match  cross_per_match  \\\n",
       "3578          0.166565              0.0             0.0              0.0   \n",
       "2623          0.166565              0.0             0.0              0.0   \n",
       "9338          0.166565              0.0             0.0              0.0   \n",
       "8046          0.166565              0.0             0.0              0.0   \n",
       "9309          0.166565              0.0             0.0              0.0   \n",
       "2665          0.166565              0.0             0.0              0.0   \n",
       "9293          0.166565              0.0             0.0              0.0   \n",
       "2678          0.166565              0.0             0.0              0.0   \n",
       "8051          0.166565              0.0             0.0              0.0   \n",
       "2697          0.166565              0.0             0.0              0.0   \n",
       "\n",
       "      foulsR_per_match  precision  goals_per_match  log_matches  score  \n",
       "3578               0.0        0.0              0.0     0.597988   -0.0  \n",
       "2623               0.0        0.0              0.0     0.555518   -0.0  \n",
       "9338               0.0        0.0              0.0     0.502961   -0.0  \n",
       "8046               0.0        0.0              0.0     0.407110   -0.0  \n",
       "9309               0.0        0.0              0.0     0.413127   -0.0  \n",
       "2665               0.0        0.0              0.0     0.223018   -0.0  \n",
       "9293               0.0        0.0              0.0     0.302951   -0.0  \n",
       "2678               0.0        0.0              0.0     0.223018   -0.0  \n",
       "8051               0.0        0.0              0.0     0.146456   -0.0  \n",
       "2697               0.0        0.0              0.0     0.065565   -0.0  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_features[player_features.y_position==\"DF\"].sort_values(\"score\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>goalsR_per_match</th>\n",
       "      <th>blocks_per_match</th>\n",
       "      <th>fouls_per_match</th>\n",
       "      <th>asst_per_match</th>\n",
       "      <th>cross_per_match</th>\n",
       "      <th>foulsR_per_match</th>\n",
       "      <th>precision</th>\n",
       "      <th>goals_per_match</th>\n",
       "      <th>log_matches</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>46509</td>\n",
       "      <td>Angel Di Maria</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.258717</td>\n",
       "      <td>0.123472</td>\n",
       "      <td>0.092962</td>\n",
       "      <td>0.664740</td>\n",
       "      <td>0.664740</td>\n",
       "      <td>0.214124</td>\n",
       "      <td>0.212500</td>\n",
       "      <td>0.173533</td>\n",
       "      <td>0.792991</td>\n",
       "      <td>0.256846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>37459</td>\n",
       "      <td>David Silva</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.371637</td>\n",
       "      <td>0.104663</td>\n",
       "      <td>0.251983</td>\n",
       "      <td>0.587026</td>\n",
       "      <td>0.587026</td>\n",
       "      <td>0.331797</td>\n",
       "      <td>0.176692</td>\n",
       "      <td>0.191244</td>\n",
       "      <td>0.878205</td>\n",
       "      <td>0.245565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>30613</td>\n",
       "      <td>Cesc Fabregas</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.405084</td>\n",
       "      <td>0.099092</td>\n",
       "      <td>0.234690</td>\n",
       "      <td>0.578873</td>\n",
       "      <td>0.578873</td>\n",
       "      <td>0.266880</td>\n",
       "      <td>0.248908</td>\n",
       "      <td>0.231935</td>\n",
       "      <td>0.878205</td>\n",
       "      <td>0.241422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>30834</td>\n",
       "      <td>Arjen Robben</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.339406</td>\n",
       "      <td>0.110032</td>\n",
       "      <td>0.142698</td>\n",
       "      <td>0.369120</td>\n",
       "      <td>0.369120</td>\n",
       "      <td>0.350954</td>\n",
       "      <td>0.328185</td>\n",
       "      <td>0.539951</td>\n",
       "      <td>0.710706</td>\n",
       "      <td>0.224442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>30631</td>\n",
       "      <td>Frank Lampard</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.335633</td>\n",
       "      <td>0.110660</td>\n",
       "      <td>0.184375</td>\n",
       "      <td>0.429524</td>\n",
       "      <td>0.429524</td>\n",
       "      <td>0.206585</td>\n",
       "      <td>0.214099</td>\n",
       "      <td>0.418522</td>\n",
       "      <td>0.792991</td>\n",
       "      <td>0.224050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10227</th>\n",
       "      <td>116772</td>\n",
       "      <td>Thomas Mueller</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.276963</td>\n",
       "      <td>0.120433</td>\n",
       "      <td>0.196293</td>\n",
       "      <td>0.357420</td>\n",
       "      <td>0.357420</td>\n",
       "      <td>0.256917</td>\n",
       "      <td>0.362550</td>\n",
       "      <td>0.405813</td>\n",
       "      <td>0.843748</td>\n",
       "      <td>0.221791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7316</th>\n",
       "      <td>36378</td>\n",
       "      <td>Mesut Oezil</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.401350</td>\n",
       "      <td>0.099714</td>\n",
       "      <td>0.107216</td>\n",
       "      <td>0.567800</td>\n",
       "      <td>0.567800</td>\n",
       "      <td>0.230536</td>\n",
       "      <td>0.284810</td>\n",
       "      <td>0.184810</td>\n",
       "      <td>0.874723</td>\n",
       "      <td>0.219650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3656</th>\n",
       "      <td>31921</td>\n",
       "      <td>Gareth Bale</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.444939</td>\n",
       "      <td>0.092454</td>\n",
       "      <td>0.146564</td>\n",
       "      <td>0.427056</td>\n",
       "      <td>0.427056</td>\n",
       "      <td>0.325123</td>\n",
       "      <td>0.243017</td>\n",
       "      <td>0.378419</td>\n",
       "      <td>0.853126</td>\n",
       "      <td>0.217191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3510</th>\n",
       "      <td>30924</td>\n",
       "      <td>Franck Ribery</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.301694</td>\n",
       "      <td>0.116313</td>\n",
       "      <td>0.177409</td>\n",
       "      <td>0.445490</td>\n",
       "      <td>0.445490</td>\n",
       "      <td>0.371598</td>\n",
       "      <td>0.376623</td>\n",
       "      <td>0.368437</td>\n",
       "      <td>0.710706</td>\n",
       "      <td>0.215281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7776</th>\n",
       "      <td>40945</td>\n",
       "      <td>Nani</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.287546</td>\n",
       "      <td>0.118670</td>\n",
       "      <td>0.248268</td>\n",
       "      <td>0.609079</td>\n",
       "      <td>0.609079</td>\n",
       "      <td>0.325018</td>\n",
       "      <td>0.151316</td>\n",
       "      <td>0.166463</td>\n",
       "      <td>0.661650</td>\n",
       "      <td>0.204551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       player_api_id     player_name y_position  goalsR_per_match  \\\n",
       "794            46509  Angel Di Maria         MC          0.258717   \n",
       "2410           37459     David Silva         MC          0.371637   \n",
       "1642           30613   Cesc Fabregas         MC          0.405084   \n",
       "948            30834    Arjen Robben         MC          0.339406   \n",
       "3540           30631   Frank Lampard         MC          0.335633   \n",
       "10227         116772  Thomas Mueller         MC          0.276963   \n",
       "7316           36378     Mesut Oezil         MC          0.401350   \n",
       "3656           31921     Gareth Bale         MC          0.444939   \n",
       "3510           30924   Franck Ribery         MC          0.301694   \n",
       "7776           40945            Nani         MC          0.287546   \n",
       "\n",
       "       blocks_per_match  fouls_per_match  asst_per_match  cross_per_match  \\\n",
       "794            0.123472         0.092962        0.664740         0.664740   \n",
       "2410           0.104663         0.251983        0.587026         0.587026   \n",
       "1642           0.099092         0.234690        0.578873         0.578873   \n",
       "948            0.110032         0.142698        0.369120         0.369120   \n",
       "3540           0.110660         0.184375        0.429524         0.429524   \n",
       "10227          0.120433         0.196293        0.357420         0.357420   \n",
       "7316           0.099714         0.107216        0.567800         0.567800   \n",
       "3656           0.092454         0.146564        0.427056         0.427056   \n",
       "3510           0.116313         0.177409        0.445490         0.445490   \n",
       "7776           0.118670         0.248268        0.609079         0.609079   \n",
       "\n",
       "       foulsR_per_match  precision  goals_per_match  log_matches     score  \n",
       "794            0.214124   0.212500         0.173533     0.792991  0.256846  \n",
       "2410           0.331797   0.176692         0.191244     0.878205  0.245565  \n",
       "1642           0.266880   0.248908         0.231935     0.878205  0.241422  \n",
       "948            0.350954   0.328185         0.539951     0.710706  0.224442  \n",
       "3540           0.206585   0.214099         0.418522     0.792991  0.224050  \n",
       "10227          0.256917   0.362550         0.405813     0.843748  0.221791  \n",
       "7316           0.230536   0.284810         0.184810     0.874723  0.219650  \n",
       "3656           0.325123   0.243017         0.378419     0.853126  0.217191  \n",
       "3510           0.371598   0.376623         0.368437     0.710706  0.215281  \n",
       "7776           0.325018   0.151316         0.166463     0.661650  0.204551  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_features[player_features.y_position==\"MC\"].sort_values(\"score\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>y_position</th>\n",
       "      <th>goalsR_per_match</th>\n",
       "      <th>blocks_per_match</th>\n",
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       "      <th>asst_per_match</th>\n",
       "      <th>cross_per_match</th>\n",
       "      <th>foulsR_per_match</th>\n",
       "      <th>precision</th>\n",
       "      <th>goals_per_match</th>\n",
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       "      <th>score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6169</th>\n",
       "      <td>30981</td>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.312540</td>\n",
       "      <td>0.114507</td>\n",
       "      <td>0.138865</td>\n",
       "      <td>0.632376</td>\n",
       "      <td>0.632376</td>\n",
       "      <td>0.438973</td>\n",
       "      <td>0.391123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929932</td>\n",
       "      <td>0.871577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>30893</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.358077</td>\n",
       "      <td>0.106922</td>\n",
       "      <td>0.165585</td>\n",
       "      <td>0.444015</td>\n",
       "      <td>0.444015</td>\n",
       "      <td>0.424039</td>\n",
       "      <td>0.325909</td>\n",
       "      <td>0.947753</td>\n",
       "      <td>0.944739</td>\n",
       "      <td>0.817846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11041</th>\n",
       "      <td>35724</td>\n",
       "      <td>Zlatan Ibrahimovic</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.298559</td>\n",
       "      <td>0.116836</td>\n",
       "      <td>0.325071</td>\n",
       "      <td>0.376432</td>\n",
       "      <td>0.376432</td>\n",
       "      <td>0.218686</td>\n",
       "      <td>0.373106</td>\n",
       "      <td>0.740199</td>\n",
       "      <td>0.908171</td>\n",
       "      <td>0.619987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>25759</td>\n",
       "      <td>Gonzalo Higuain</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.409127</td>\n",
       "      <td>0.098419</td>\n",
       "      <td>0.206588</td>\n",
       "      <td>0.293433</td>\n",
       "      <td>0.293433</td>\n",
       "      <td>0.199788</td>\n",
       "      <td>0.405868</td>\n",
       "      <td>0.714997</td>\n",
       "      <td>0.856813</td>\n",
       "      <td>0.564909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9659</th>\n",
       "      <td>37412</td>\n",
       "      <td>Sergio Aguero</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.467085</td>\n",
       "      <td>0.088765</td>\n",
       "      <td>0.191631</td>\n",
       "      <td>0.304212</td>\n",
       "      <td>0.304212</td>\n",
       "      <td>0.329333</td>\n",
       "      <td>0.322718</td>\n",
       "      <td>0.607298</td>\n",
       "      <td>0.885073</td>\n",
       "      <td>0.512962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2860</th>\n",
       "      <td>49677</td>\n",
       "      <td>Edinson Cavani</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.388042</td>\n",
       "      <td>0.101931</td>\n",
       "      <td>0.114394</td>\n",
       "      <td>0.118442</td>\n",
       "      <td>0.118442</td>\n",
       "      <td>0.129889</td>\n",
       "      <td>0.492212</td>\n",
       "      <td>0.583727</td>\n",
       "      <td>0.914518</td>\n",
       "      <td>0.489670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>27734</td>\n",
       "      <td>Antonio Di Natale</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.512003</td>\n",
       "      <td>0.081283</td>\n",
       "      <td>0.037401</td>\n",
       "      <td>0.164580</td>\n",
       "      <td>0.164580</td>\n",
       "      <td>0.071587</td>\n",
       "      <td>0.508651</td>\n",
       "      <td>0.603711</td>\n",
       "      <td>0.874723</td>\n",
       "      <td>0.484515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9025</th>\n",
       "      <td>93447</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.335212</td>\n",
       "      <td>0.110730</td>\n",
       "      <td>0.229288</td>\n",
       "      <td>0.191844</td>\n",
       "      <td>0.191844</td>\n",
       "      <td>0.254584</td>\n",
       "      <td>0.401993</td>\n",
       "      <td>0.643617</td>\n",
       "      <td>0.777459</td>\n",
       "      <td>0.469769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9083</th>\n",
       "      <td>30843</td>\n",
       "      <td>Robin van Persie</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.457146</td>\n",
       "      <td>0.090421</td>\n",
       "      <td>0.345960</td>\n",
       "      <td>0.514311</td>\n",
       "      <td>0.514311</td>\n",
       "      <td>0.288170</td>\n",
       "      <td>0.279518</td>\n",
       "      <td>0.595497</td>\n",
       "      <td>0.790811</td>\n",
       "      <td>0.467719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6369</th>\n",
       "      <td>40636</td>\n",
       "      <td>Luis Suarez</td>\n",
       "      <td>DEL</td>\n",
       "      <td>0.325474</td>\n",
       "      <td>0.112352</td>\n",
       "      <td>0.277657</td>\n",
       "      <td>0.444291</td>\n",
       "      <td>0.444291</td>\n",
       "      <td>0.411776</td>\n",
       "      <td>0.271762</td>\n",
       "      <td>0.506822</td>\n",
       "      <td>0.888461</td>\n",
       "      <td>0.462163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       player_api_id         player_name y_position  goalsR_per_match  \\\n",
       "6169           30981        Lionel Messi        DEL          0.312540   \n",
       "1992           30893   Cristiano Ronaldo        DEL          0.358077   \n",
       "11041          35724  Zlatan Ibrahimovic        DEL          0.298559   \n",
       "3932           25759     Gonzalo Higuain        DEL          0.409127   \n",
       "9659           37412       Sergio Aguero        DEL          0.467085   \n",
       "2860           49677      Edinson Cavani        DEL          0.388042   \n",
       "892            27734   Antonio Di Natale        DEL          0.512003   \n",
       "9025           93447  Robert Lewandowski        DEL          0.335212   \n",
       "9083           30843    Robin van Persie        DEL          0.457146   \n",
       "6369           40636         Luis Suarez        DEL          0.325474   \n",
       "\n",
       "       blocks_per_match  fouls_per_match  asst_per_match  cross_per_match  \\\n",
       "6169           0.114507         0.138865        0.632376         0.632376   \n",
       "1992           0.106922         0.165585        0.444015         0.444015   \n",
       "11041          0.116836         0.325071        0.376432         0.376432   \n",
       "3932           0.098419         0.206588        0.293433         0.293433   \n",
       "9659           0.088765         0.191631        0.304212         0.304212   \n",
       "2860           0.101931         0.114394        0.118442         0.118442   \n",
       "892            0.081283         0.037401        0.164580         0.164580   \n",
       "9025           0.110730         0.229288        0.191844         0.191844   \n",
       "9083           0.090421         0.345960        0.514311         0.514311   \n",
       "6369           0.112352         0.277657        0.444291         0.444291   \n",
       "\n",
       "       foulsR_per_match  precision  goals_per_match  log_matches     score  \n",
       "6169           0.438973   0.391123         1.000000     0.929932  0.871577  \n",
       "1992           0.424039   0.325909         0.947753     0.944739  0.817846  \n",
       "11041          0.218686   0.373106         0.740199     0.908171  0.619987  \n",
       "3932           0.199788   0.405868         0.714997     0.856813  0.564909  \n",
       "9659           0.329333   0.322718         0.607298     0.885073  0.512962  \n",
       "2860           0.129889   0.492212         0.583727     0.914518  0.489670  \n",
       "892            0.071587   0.508651         0.603711     0.874723  0.484515  \n",
       "9025           0.254584   0.401993         0.643617     0.777459  0.469769  \n",
       "9083           0.288170   0.279518         0.595497     0.790811  0.467719  \n",
       "6369           0.411776   0.271762         0.506822     0.888461  0.462163  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_features[player_features.y_position==\"DEL\"].sort_values(\"score\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con esto, el mejor 11 de la historia, segun este criterio sería\n",
    "\n",
    "Cristiano Ronaldo, Zlatan Ibrahimovic, Lionel Messi\n",
    "\n",
    "David Silva, Frank Lampard, Di Maria\n",
    "\n",
    "Maxwell, T. Silva, Bonucci, Philip Lahm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se decidió realizar esta segunda aproximación solamente ocupando los features creados, esto con el objetivo de simular cómo a partir de datos crudos, poder extraer información, en este caso de la calidad de los jugadores de futbol.\n",
    "\n",
    "Una forma de poder mejorar esto ultimo, es seguir creando más características de los jugadores y mejorar el tipo de evaluación, esto es posible a través del trabajo colaborativo con expertos en el área.\n",
    "\n",
    "Además cabe destacar que también es posible realizar una revisión bibliográfica acerca del problema e implementarla con estos datos, pero consideré más valioso proponer una solución desde cero.\n",
    "\n",
    "Por ultimo y solo como idea, seria interesante construir un modelo que prediga la resultado entre dos equipos (los datos existen) y luego encontrar el optimo de jugadores a través de algún algoritmo de optimización."
   ]
  }
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