Logistic Regression to predict the pass outcome in a match.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsbombpy import sb\n",
    "import matplotlib.pyplot as plt\n",
    "from mplsoccer.pitch import Pitch\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "credentials were not supplied. open data access only\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>competition_id</th>\n",
       "      <th>season_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>competition_name</th>\n",
       "      <th>competition_gender</th>\n",
       "      <th>season_name</th>\n",
       "      <th>match_updated</th>\n",
       "      <th>match_available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2021-04-11T22:02:53.549443</td>\n",
       "      <td>2021-04-11T22:02:53.549443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2017/2018</td>\n",
       "      <td>2021-01-23T21:55:30.425330</td>\n",
       "      <td>2021-01-23T21:55:30.425330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2016/2017</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>2020-08-26T12:33:15.869622</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>41</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2020-08-30T10:18:39.435424</td>\n",
       "      <td>2020-08-30T10:18:39.435424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2006/2007</td>\n",
       "      <td>2021-03-31T04:18:30.437060</td>\n",
       "      <td>2021-03-31T04:18:30.437060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2004/2005</td>\n",
       "      <td>2021-04-01T06:18:57.459032</td>\n",
       "      <td>2021-04-01T06:18:57.459032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>2021-04-01T00:34:59.472485</td>\n",
       "      <td>2021-04-01T00:34:59.472485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Champions League</td>\n",
       "      <td>male</td>\n",
       "      <td>1999/2000</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>England</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>female</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>2020-10-20T18:35:33.568528</td>\n",
       "      <td>2020-10-20T18:35:33.568528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>England</td>\n",
       "      <td>FA Women's Super League</td>\n",
       "      <td>female</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2021-04-09T20:39:24.201269</td>\n",
       "      <td>2021-04-09T20:39:24.201269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>International</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>male</td>\n",
       "      <td>2018</td>\n",
       "      <td>2020-10-25T14:03:50.263266</td>\n",
       "      <td>2020-10-25T14:03:50.263266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2019/2020</td>\n",
       "      <td>2020-12-18T12:10:38.985394</td>\n",
       "      <td>2020-12-18T12:10:38.985394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2018/2019</td>\n",
       "      <td>2021-04-12T07:10:50.252564</td>\n",
       "      <td>2021-04-12T07:10:50.252564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2017/2018</td>\n",
       "      <td>2021-04-12T07:10:50.238089</td>\n",
       "      <td>2021-04-12T07:10:50.238089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2016/2017</td>\n",
       "      <td>2021-02-02T23:24:58.985975</td>\n",
       "      <td>2021-02-02T23:24:58.985975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2015/2016</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2014/2015</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2013/2014</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2012/2013</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2011/2012</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2010/2011</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2009/2010</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2008/2009</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11</td>\n",
       "      <td>40</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2007/2008</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2006/2007</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2005/2006</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>11</td>\n",
       "      <td>37</td>\n",
       "      <td>Spain</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>male</td>\n",
       "      <td>2004/2005</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>NWSL</td>\n",
       "      <td>female</td>\n",
       "      <td>2018</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>England</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>male</td>\n",
       "      <td>2003/2004</td>\n",
       "      <td>2020-08-31T20:40:28.969635</td>\n",
       "      <td>2020-08-31T20:40:28.969635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>72</td>\n",
       "      <td>30</td>\n",
       "      <td>International</td>\n",
       "      <td>Women's World Cup</td>\n",
       "      <td>female</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    competition_id  season_id              country_name  \\\n",
       "0               16          4                    Europe   \n",
       "1               16          1                    Europe   \n",
       "2               16          2                    Europe   \n",
       "3               16         27                    Europe   \n",
       "4               16         26                    Europe   \n",
       "5               16         25                    Europe   \n",
       "6               16         24                    Europe   \n",
       "7               16         23                    Europe   \n",
       "8               16         22                    Europe   \n",
       "9               16         21                    Europe   \n",
       "10              16         41                    Europe   \n",
       "11              16         39                    Europe   \n",
       "12              16         37                    Europe   \n",
       "13              16         44                    Europe   \n",
       "14              16         76                    Europe   \n",
       "15              37         42                   England   \n",
       "16              37          4                   England   \n",
       "17              43          3             International   \n",
       "18              11         42                     Spain   \n",
       "19              11          4                     Spain   \n",
       "20              11          1                     Spain   \n",
       "21              11          2                     Spain   \n",
       "22              11         27                     Spain   \n",
       "23              11         26                     Spain   \n",
       "24              11         25                     Spain   \n",
       "25              11         24                     Spain   \n",
       "26              11         23                     Spain   \n",
       "27              11         22                     Spain   \n",
       "28              11         21                     Spain   \n",
       "29              11         41                     Spain   \n",
       "30              11         40                     Spain   \n",
       "31              11         39                     Spain   \n",
       "32              11         38                     Spain   \n",
       "33              11         37                     Spain   \n",
       "34              49          3  United States of America   \n",
       "35               2         44                   England   \n",
       "36              72         30             International   \n",
       "\n",
       "           competition_name competition_gender season_name  \\\n",
       "0          Champions League               male   2018/2019   \n",
       "1          Champions League               male   2017/2018   \n",
       "2          Champions League               male   2016/2017   \n",
       "3          Champions League               male   2015/2016   \n",
       "4          Champions League               male   2014/2015   \n",
       "5          Champions League               male   2013/2014   \n",
       "6          Champions League               male   2012/2013   \n",
       "7          Champions League               male   2011/2012   \n",
       "8          Champions League               male   2010/2011   \n",
       "9          Champions League               male   2009/2010   \n",
       "10         Champions League               male   2008/2009   \n",
       "11         Champions League               male   2006/2007   \n",
       "12         Champions League               male   2004/2005   \n",
       "13         Champions League               male   2003/2004   \n",
       "14         Champions League               male   1999/2000   \n",
       "15  FA Women's Super League             female   2019/2020   \n",
       "16  FA Women's Super League             female   2018/2019   \n",
       "17           FIFA World Cup               male        2018   \n",
       "18                  La Liga               male   2019/2020   \n",
       "19                  La Liga               male   2018/2019   \n",
       "20                  La Liga               male   2017/2018   \n",
       "21                  La Liga               male   2016/2017   \n",
       "22                  La Liga               male   2015/2016   \n",
       "23                  La Liga               male   2014/2015   \n",
       "24                  La Liga               male   2013/2014   \n",
       "25                  La Liga               male   2012/2013   \n",
       "26                  La Liga               male   2011/2012   \n",
       "27                  La Liga               male   2010/2011   \n",
       "28                  La Liga               male   2009/2010   \n",
       "29                  La Liga               male   2008/2009   \n",
       "30                  La Liga               male   2007/2008   \n",
       "31                  La Liga               male   2006/2007   \n",
       "32                  La Liga               male   2005/2006   \n",
       "33                  La Liga               male   2004/2005   \n",
       "34                     NWSL             female        2018   \n",
       "35           Premier League               male   2003/2004   \n",
       "36        Women's World Cup             female        2019   \n",
       "\n",
       "                 match_updated             match_available  \n",
       "0   2021-04-11T22:02:53.549443  2021-04-11T22:02:53.549443  \n",
       "1   2021-01-23T21:55:30.425330  2021-01-23T21:55:30.425330  \n",
       "2   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "3   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "4   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "5   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "6   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "7   2020-08-26T12:33:15.869622            2020-07-29T05:00  \n",
       "8             2020-07-29T05:00            2020-07-29T05:00  \n",
       "9             2020-07-29T05:00            2020-07-29T05:00  \n",
       "10  2020-08-30T10:18:39.435424  2020-08-30T10:18:39.435424  \n",
       "11  2021-03-31T04:18:30.437060  2021-03-31T04:18:30.437060  \n",
       "12  2021-04-01T06:18:57.459032  2021-04-01T06:18:57.459032  \n",
       "13  2021-04-01T00:34:59.472485  2021-04-01T00:34:59.472485  \n",
       "14            2020-07-29T05:00            2020-07-29T05:00  \n",
       "15  2020-10-20T18:35:33.568528  2020-10-20T18:35:33.568528  \n",
       "16  2021-04-09T20:39:24.201269  2021-04-09T20:39:24.201269  \n",
       "17  2020-10-25T14:03:50.263266  2020-10-25T14:03:50.263266  \n",
       "18  2020-12-18T12:10:38.985394  2020-12-18T12:10:38.985394  \n",
       "19  2021-04-12T07:10:50.252564  2021-04-12T07:10:50.252564  \n",
       "20  2021-04-12T07:10:50.238089  2021-04-12T07:10:50.238089  \n",
       "21  2021-02-02T23:24:58.985975  2021-02-02T23:24:58.985975  \n",
       "22            2020-07-29T05:00            2020-07-29T05:00  \n",
       "23            2020-07-29T05:00            2020-07-29T05:00  \n",
       "24            2020-07-29T05:00            2020-07-29T05:00  \n",
       "25            2020-07-29T05:00            2020-07-29T05:00  \n",
       "26            2020-07-29T05:00            2020-07-29T05:00  \n",
       "27            2020-07-29T05:00            2020-07-29T05:00  \n",
       "28            2020-07-29T05:00            2020-07-29T05:00  \n",
       "29            2020-07-29T05:00            2020-07-29T05:00  \n",
       "30            2020-07-29T05:00            2020-07-29T05:00  \n",
       "31            2020-07-29T05:00            2020-07-29T05:00  \n",
       "32            2020-07-29T05:00            2020-07-29T05:00  \n",
       "33            2020-07-29T05:00            2020-07-29T05:00  \n",
       "34            2020-07-29T05:00            2020-07-29T05:00  \n",
       "35  2020-08-31T20:40:28.969635  2020-08-31T20:40:28.969635  \n",
       "36            2020-07-29T05:00            2020-07-29T05:00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sb.competitions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "credentials were not supplied. open data access only\n"
     ]
    }
   ],
   "source": [
    "mat = sb.matches(competition_id=43, season_id=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'match_date', 'kick_off', 'competition', 'season',\n",
       "       'home_team', 'away_team', 'home_score', 'away_score', 'match_status',\n",
       "       'match_status_360', 'last_updated', 'last_updated_360', 'match_week',\n",
       "       'competition_stage', 'stadium', 'referee', 'data_version',\n",
       "       'shot_fidelity_version', 'xy_fidelity_version'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>match_date</th>\n",
       "      <th>kick_off</th>\n",
       "      <th>competition</th>\n",
       "      <th>season</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>match_status</th>\n",
       "      <th>match_status_360</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>last_updated_360</th>\n",
       "      <th>match_week</th>\n",
       "      <th>competition_stage</th>\n",
       "      <th>stadium</th>\n",
       "      <th>referee</th>\n",
       "      <th>data_version</th>\n",
       "      <th>shot_fidelity_version</th>\n",
       "      <th>xy_fidelity_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7580</td>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>21:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>France</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>Round of 16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8658</td>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>17:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>France</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>Final</td>\n",
       "      <td>Stadion Luzhniki</td>\n",
       "      <td>N. Pitana</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7552</td>\n",
       "      <td>2018-06-23</td>\n",
       "      <td>14:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>Otkrytiye Arena</td>\n",
       "      <td>J. Marrufo</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>7525</td>\n",
       "      <td>2018-06-14</td>\n",
       "      <td>17:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>Stadion Luzhniki</td>\n",
       "      <td>N. Pitana</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7554</td>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>14:00:00.000</td>\n",
       "      <td>International - FIFA World Cup</td>\n",
       "      <td>2018</td>\n",
       "      <td>England</td>\n",
       "      <td>Panama</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>available</td>\n",
       "      <td>unscheduled</td>\n",
       "      <td>2020-07-29T05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>Group Stage</td>\n",
       "      <td>Stadion Nizhny Novgorod</td>\n",
       "      <td>Ghead Grisha</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  match_date      kick_off                     competition season  \\\n",
       "21      7580  2018-06-30  21:00:00.000  International - FIFA World Cup   2018   \n",
       "24      8658  2018-07-15  17:00:00.000  International - FIFA World Cup   2018   \n",
       "31      7552  2018-06-23  14:00:00.000  International - FIFA World Cup   2018   \n",
       "44      7525  2018-06-14  17:00:00.000  International - FIFA World Cup   2018   \n",
       "50      7554  2018-06-24  14:00:00.000  International - FIFA World Cup   2018   \n",
       "\n",
       "   home_team     away_team  home_score  away_score match_status  \\\n",
       "21    France     Argentina           4           3    available   \n",
       "24    France       Croatia           4           2    available   \n",
       "31   Belgium       Tunisia           5           2    available   \n",
       "44    Russia  Saudi Arabia           5           0    available   \n",
       "50   England        Panama           6           1    available   \n",
       "\n",
       "   match_status_360      last_updated last_updated_360  match_week  \\\n",
       "21      unscheduled  2020-07-29T05:00             None           4   \n",
       "24      unscheduled  2020-07-29T05:00             None           7   \n",
       "31      unscheduled  2020-07-29T05:00             None           2   \n",
       "44      unscheduled  2020-07-29T05:00             None           1   \n",
       "50      unscheduled  2020-07-29T05:00             None           2   \n",
       "\n",
       "   competition_stage                  stadium       referee data_version  \\\n",
       "21       Round of 16                      NaN           NaN        1.0.2   \n",
       "24             Final         Stadion Luzhniki     N. Pitana        1.0.2   \n",
       "31       Group Stage          Otkrytiye Arena    J. Marrufo        1.0.2   \n",
       "44       Group Stage         Stadion Luzhniki     N. Pitana        1.0.2   \n",
       "50       Group Stage  Stadion Nizhny Novgorod  Ghead Grisha        1.0.2   \n",
       "\n",
       "   shot_fidelity_version xy_fidelity_version  \n",
       "21                  None                None  \n",
       "24                  None                None  \n",
       "31                  None                None  \n",
       "44                  None                None  \n",
       "50                  None                None  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E = mat[(mat['away_score']>3) | (mat['home_score']>3)]\n",
    "E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "credentials were not supplied. open data access only\n"
     ]
    },
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3544</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yellow Card</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>00:48:18.547</td>\n",
       "      <td>Bad Behaviour</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3545</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>00:10:12.600</td>\n",
       "      <td>Error</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>{'outcome': {'id': 1, 'name': 'Lost'}}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>00:19:59.160</td>\n",
       "      <td>50/50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3547</th>\n",
       "      <td>{'outcome': {'id': 4, 'name': 'Won'}}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>00:19:59.160</td>\n",
       "      <td>50/50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3548</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'formation': 442, 'lineup': [{'player': {'id'...</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>00:22:14.801</td>\n",
       "      <td>Tactical Shift</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3549 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       50_50 bad_behaviour_card  \\\n",
       "0                                        NaN                NaN   \n",
       "1                                        NaN                NaN   \n",
       "2                                        NaN                NaN   \n",
       "3                                        NaN                NaN   \n",
       "4                                        NaN                NaN   \n",
       "...                                      ...                ...   \n",
       "3544                                     NaN        Yellow Card   \n",
       "3545                                     NaN                NaN   \n",
       "3546  {'outcome': {'id': 1, 'name': 'Lost'}}                NaN   \n",
       "3547   {'outcome': {'id': 4, 'name': 'Won'}}                NaN   \n",
       "3548                                     NaN                NaN   \n",
       "\n",
       "     ball_receipt_outcome ball_recovery_offensive  \\\n",
       "0                     NaN                     NaN   \n",
       "1                     NaN                     NaN   \n",
       "2                     NaN                     NaN   \n",
       "3                     NaN                     NaN   \n",
       "4                     NaN                     NaN   \n",
       "...                   ...                     ...   \n",
       "3544                  NaN                     NaN   \n",
       "3545                  NaN                     NaN   \n",
       "3546                  NaN                     NaN   \n",
       "3547                  NaN                     NaN   \n",
       "3548                  NaN                     NaN   \n",
       "\n",
       "     ball_recovery_recovery_failure block_deflection block_offensive  \\\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   \n",
       "...                             ...              ...             ...   \n",
       "3544                            NaN              NaN             NaN   \n",
       "3545                            NaN              NaN             NaN   \n",
       "3546                            NaN              NaN             NaN   \n",
       "3547                            NaN              NaN             NaN   \n",
       "3548                            NaN              NaN             NaN   \n",
       "\n",
       "     carry_end_location clearance_aerial_won counterpress  ...  \\\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  ...   \n",
       "...                 ...                  ...          ...  ...   \n",
       "3544                NaN                  NaN          NaN  ...   \n",
       "3545                NaN                  NaN          NaN  ...   \n",
       "3546                NaN                  NaN          NaN  ...   \n",
       "3547                NaN                  NaN          NaN  ...   \n",
       "3548                NaN                  NaN          NaN  ...   \n",
       "\n",
       "     shot_statsbomb_xg shot_technique shot_type substitution_outcome  \\\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",
       "3544               NaN            NaN       NaN                  NaN   \n",
       "3545               NaN            NaN       NaN                  NaN   \n",
       "3546               NaN            NaN       NaN                  NaN   \n",
       "3547               NaN            NaN       NaN                  NaN   \n",
       "3548               NaN            NaN       NaN                  NaN   \n",
       "\n",
       "     substitution_replacement  \\\n",
       "0                         NaN   \n",
       "1                         NaN   \n",
       "2                         NaN   \n",
       "3                         NaN   \n",
       "4                         NaN   \n",
       "...                       ...   \n",
       "3544                      NaN   \n",
       "3545                      NaN   \n",
       "3546                      NaN   \n",
       "3547                      NaN   \n",
       "3548                      NaN   \n",
       "\n",
       "                                                tactics       team  \\\n",
       "0     {'formation': 4231, 'lineup': [{'player': {'id...     France   \n",
       "1     {'formation': 433, 'lineup': [{'player': {'id'...  Argentina   \n",
       "2                                                   NaN     France   \n",
       "3                                                   NaN  Argentina   \n",
       "4                                                   NaN  Argentina   \n",
       "...                                                 ...        ...   \n",
       "3544                                                NaN     France   \n",
       "3545                                                NaN  Argentina   \n",
       "3546                                                NaN     France   \n",
       "3547                                                NaN  Argentina   \n",
       "3548  {'formation': 442, 'lineup': [{'player': {'id'...  Argentina   \n",
       "\n",
       "         timestamp            type under_pressure  \n",
       "0     00:00:00.000     Starting XI            NaN  \n",
       "1     00:00:00.000     Starting XI            NaN  \n",
       "2     00:00:00.000      Half Start            NaN  \n",
       "3     00:00:00.000      Half Start            NaN  \n",
       "4     00:00:00.000      Half Start            NaN  \n",
       "...            ...             ...            ...  \n",
       "3544  00:48:18.547   Bad Behaviour            NaN  \n",
       "3545  00:10:12.600           Error            NaN  \n",
       "3546  00:19:59.160           50/50           True  \n",
       "3547  00:19:59.160           50/50           True  \n",
       "3548  00:22:14.801  Tactical Shift            NaN  \n",
       "\n",
       "[3549 rows x 77 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = sb.events(match_id = 7580)\n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['50_50', 'bad_behaviour_card', 'ball_receipt_outcome',\n",
       "       'ball_recovery_offensive', 'ball_recovery_recovery_failure',\n",
       "       'block_deflection', 'block_offensive', 'carry_end_location',\n",
       "       'clearance_aerial_won', 'counterpress', 'dribble_nutmeg',\n",
       "       'dribble_outcome', 'dribble_overrun', 'duel_outcome', 'duel_type',\n",
       "       'duration', 'foul_committed_advantage', 'foul_committed_card',\n",
       "       'foul_committed_penalty', 'foul_won_advantage', 'foul_won_defensive',\n",
       "       'foul_won_penalty', 'goalkeeper_body_part', 'goalkeeper_end_location',\n",
       "       'goalkeeper_outcome', 'goalkeeper_position', 'goalkeeper_technique',\n",
       "       'goalkeeper_type', 'id', 'index', 'interception_outcome', 'location',\n",
       "       'match_id', 'minute', 'pass_aerial_won', 'pass_angle',\n",
       "       'pass_assisted_shot_id', 'pass_backheel', 'pass_body_part',\n",
       "       'pass_cross', 'pass_deflected', 'pass_end_location', 'pass_goal_assist',\n",
       "       'pass_height', 'pass_length', 'pass_miscommunication', 'pass_outcome',\n",
       "       'pass_recipient', 'pass_shot_assist', 'pass_switch', 'pass_type',\n",
       "       'period', 'play_pattern', 'player', 'position', 'possession',\n",
       "       'possession_team', 'related_events', 'second', 'shot_body_part',\n",
       "       'shot_end_location', 'shot_first_time', 'shot_freeze_frame',\n",
       "       'shot_key_pass_id', 'shot_one_on_one', 'shot_outcome', 'shot_redirect',\n",
       "       'shot_statsbomb_xg', 'shot_technique', 'shot_type',\n",
       "       'substitution_outcome', 'substitution_replacement', 'tactics', 'team',\n",
       "       'timestamp', 'type', 'under_pressure'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>type</th>\n",
       "      <th>pass_angle</th>\n",
       "      <th>pass_height</th>\n",
       "      <th>pass_length</th>\n",
       "      <th>pass_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Starting XI</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>Starting XI</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>Half Start</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>Half Start</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>Half Start</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>3544</th>\n",
       "      <td>Bad Behaviour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3545</th>\n",
       "      <td>Error</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>50/50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3547</th>\n",
       "      <td>50/50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3548</th>\n",
       "      <td>Tactical Shift</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3549 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                type  pass_angle pass_height  pass_length pass_outcome\n",
       "0        Starting XI         NaN         NaN          NaN          NaN\n",
       "1        Starting XI         NaN         NaN          NaN          NaN\n",
       "2         Half Start         NaN         NaN          NaN          NaN\n",
       "3         Half Start         NaN         NaN          NaN          NaN\n",
       "4         Half Start         NaN         NaN          NaN          NaN\n",
       "...              ...         ...         ...          ...          ...\n",
       "3544   Bad Behaviour         NaN         NaN          NaN          NaN\n",
       "3545           Error         NaN         NaN          NaN          NaN\n",
       "3546           50/50         NaN         NaN          NaN          NaN\n",
       "3547           50/50         NaN         NaN          NaN          NaN\n",
       "3548  Tactical Shift         NaN         NaN          NaN          NaN\n",
       "\n",
       "[3549 rows x 5 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass = events[['type', 'pass_angle', 'pass_height', 'pass_length', 'pass_outcome']]\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>type</th>\n",
       "      <th>pass_angle</th>\n",
       "      <th>pass_height</th>\n",
       "      <th>pass_length</th>\n",
       "      <th>pass_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pass</td>\n",
       "      <td>2.692869</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>29.966648</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pass</td>\n",
       "      <td>0.249979</td>\n",
       "      <td>High Pass</td>\n",
       "      <td>48.507732</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pass</td>\n",
       "      <td>0.018180</td>\n",
       "      <td>High Pass</td>\n",
       "      <td>55.009090</td>\n",
       "      <td>Incomplete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pass</td>\n",
       "      <td>1.190290</td>\n",
       "      <td>Low Pass</td>\n",
       "      <td>21.540659</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pass</td>\n",
       "      <td>-3.036716</td>\n",
       "      <td>High Pass</td>\n",
       "      <td>19.104973</td>\n",
       "      <td>NaN</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>886</th>\n",
       "      <td>Pass</td>\n",
       "      <td>-0.759763</td>\n",
       "      <td>Ground Pass</td>\n",
       "      <td>27.586230</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Pass</td>\n",
       "      <td>0.699893</td>\n",
       "      <td>High Pass</td>\n",
       "      <td>49.678970</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>Pass</td>\n",
       "      <td>-0.982794</td>\n",
       "      <td>High Pass</td>\n",
       "      <td>36.055510</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Pass</td>\n",
       "      <td>-0.896055</td>\n",
       "      <td>Low Pass</td>\n",
       "      <td>6.403124</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Pass</td>\n",
       "      <td>1.352127</td>\n",
       "      <td>Low Pass</td>\n",
       "      <td>18.439089</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     type  pass_angle  pass_height  pass_length pass_outcome\n",
       "6    Pass    2.692869  Ground Pass    29.966648          NaN\n",
       "7    Pass    0.249979    High Pass    48.507732      Unknown\n",
       "8    Pass    0.018180    High Pass    55.009090   Incomplete\n",
       "9    Pass    1.190290     Low Pass    21.540659          NaN\n",
       "10   Pass   -3.036716    High Pass    19.104973          NaN\n",
       "..    ...         ...          ...          ...          ...\n",
       "886  Pass   -0.759763  Ground Pass    27.586230          NaN\n",
       "887  Pass    0.699893    High Pass    49.678970          NaN\n",
       "888  Pass   -0.982794    High Pass    36.055510          NaN\n",
       "889  Pass   -0.896055     Low Pass     6.403124          NaN\n",
       "890  Pass    1.352127     Low Pass    18.439089          NaN\n",
       "\n",
       "[885 rows x 5 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass = E_pass[E_pass['type'] == 'Pass']\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ground Pass', 'High Pass', 'Low Pass'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass.pass_height.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Unknown', 'Incomplete', 'Out', 'Pass Offside'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass.pass_outcome.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply a particular score to the *pass_height* and to the *pass_outcome*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-fc7b1a033553>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  E_pass['pass_height'] = E_pass['pass_height'].replace(['Ground Pass', 'High Pass', 'Low Pass'], [3, 1, 2])\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>pass_angle</th>\n",
       "      <th>pass_height</th>\n",
       "      <th>pass_length</th>\n",
       "      <th>pass_outcome</th>\n",
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       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pass</td>\n",
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       "      <td>1</td>\n",
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       "      <th>886</th>\n",
       "      <td>Pass</td>\n",
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       "      <td>3</td>\n",
       "      <td>27.586230</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Pass</td>\n",
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       "      <td>1</td>\n",
       "      <td>49.678970</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Pass</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Pass</td>\n",
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       "      <td>2</td>\n",
       "      <td>18.439089</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     type  pass_angle  pass_height  pass_length pass_outcome\n",
       "6    Pass    2.692869            3    29.966648          NaN\n",
       "7    Pass    0.249979            1    48.507732      Unknown\n",
       "8    Pass    0.018180            1    55.009090   Incomplete\n",
       "9    Pass    1.190290            2    21.540659          NaN\n",
       "10   Pass   -3.036716            1    19.104973          NaN\n",
       "..    ...         ...          ...          ...          ...\n",
       "886  Pass   -0.759763            3    27.586230          NaN\n",
       "887  Pass    0.699893            1    49.678970          NaN\n",
       "888  Pass   -0.982794            1    36.055510          NaN\n",
       "889  Pass   -0.896055            2     6.403124          NaN\n",
       "890  Pass    1.352127            2    18.439089          NaN\n",
       "\n",
       "[885 rows x 5 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass['pass_height'] = E_pass['pass_height'].replace(['Ground Pass', 'High Pass', 'Low Pass'], [3, 1, 2])\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Unknown', 'Incomplete', 'Out', 'Pass Offside'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass.pass_outcome.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-49-3e78a7b7e735>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  E_pass['pass_outcome'] = E_pass['pass_outcome'].fillna(1)\n"
     ]
    },
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       "      <td>27.586230</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
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       "<p>885 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "     type  pass_angle  pass_height  pass_length pass_outcome\n",
       "6    Pass    2.692869            3    29.966648            1\n",
       "7    Pass    0.249979            1    48.507732      Unknown\n",
       "8    Pass    0.018180            1    55.009090   Incomplete\n",
       "9    Pass    1.190290            2    21.540659            1\n",
       "10   Pass   -3.036716            1    19.104973            1\n",
       "..    ...         ...          ...          ...          ...\n",
       "886  Pass   -0.759763            3    27.586230            1\n",
       "887  Pass    0.699893            1    49.678970            1\n",
       "888  Pass   -0.982794            1    36.055510            1\n",
       "889  Pass   -0.896055            2     6.403124            1\n",
       "890  Pass    1.352127            2    18.439089            1\n",
       "\n",
       "[885 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
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   "source": [
    "E_pass['pass_outcome'] = E_pass['pass_outcome'].fillna(1)\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-50-1a6d08efdb19>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  E_pass['pass_outcome'] = E_pass['pass_outcome'].replace(['Unknown', 'Incomplete', 'Out', 'Pass Offside'], 0)\n"
     ]
    },
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     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     type  pass_angle  pass_height  pass_length  pass_outcome\n",
       "6    Pass    2.692869            3    29.966648             1\n",
       "7    Pass    0.249979            1    48.507732             0\n",
       "8    Pass    0.018180            1    55.009090             0\n",
       "9    Pass    1.190290            2    21.540659             1\n",
       "10   Pass   -3.036716            1    19.104973             1\n",
       "..    ...         ...          ...          ...           ...\n",
       "886  Pass   -0.759763            3    27.586230             1\n",
       "887  Pass    0.699893            1    49.678970             1\n",
       "888  Pass   -0.982794            1    36.055510             1\n",
       "889  Pass   -0.896055            2     6.403124             1\n",
       "890  Pass    1.352127            2    18.439089             1\n",
       "\n",
       "[885 rows x 5 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass['pass_outcome'] = E_pass['pass_outcome'].replace(['Unknown', 'Incomplete', 'Out', 'Pass Offside'], 0)\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass.pass_outcome.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>pass_angle</th>\n",
       "      <th>pass_height</th>\n",
       "      <th>pass_length</th>\n",
       "      <th>pass_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.692869</td>\n",
       "      <td>3</td>\n",
       "      <td>29.966648</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.249979</td>\n",
       "      <td>1</td>\n",
       "      <td>48.507732</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.018180</td>\n",
       "      <td>1</td>\n",
       "      <td>55.009090</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.190290</td>\n",
       "      <td>2</td>\n",
       "      <td>21.540659</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-3.036716</td>\n",
       "      <td>1</td>\n",
       "      <td>19.104973</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>-0.759763</td>\n",
       "      <td>3</td>\n",
       "      <td>27.586230</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0.699893</td>\n",
       "      <td>1</td>\n",
       "      <td>49.678970</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>-0.982794</td>\n",
       "      <td>1</td>\n",
       "      <td>36.055510</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>-0.896055</td>\n",
       "      <td>2</td>\n",
       "      <td>6.403124</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1.352127</td>\n",
       "      <td>2</td>\n",
       "      <td>18.439089</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pass_angle  pass_height  pass_length  pass_outcome\n",
       "6      2.692869            3    29.966648             1\n",
       "7      0.249979            1    48.507732             0\n",
       "8      0.018180            1    55.009090             0\n",
       "9      1.190290            2    21.540659             1\n",
       "10    -3.036716            1    19.104973             1\n",
       "..          ...          ...          ...           ...\n",
       "886   -0.759763            3    27.586230             1\n",
       "887    0.699893            1    49.678970             1\n",
       "888   -0.982794            1    36.055510             1\n",
       "889   -0.896055            2     6.403124             1\n",
       "890    1.352127            2    18.439089             1\n",
       "\n",
       "[885 rows x 4 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_pass = E_pass[['pass_angle', 'pass_height', 'pass_length', 'pass_outcome']]\n",
    "E_pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here *pass_outcome* is the dependent variable and the other columns represent the independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((885, 3), (885,))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = E_pass[['pass_angle', 'pass_height', 'pass_length']]\n",
    "y = E_pass['pass_outcome']\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We have a large data set. So, we will split it into training and test data using *scikit learn* module. Out of 885 rows, we will use the first 309 rows as a training data set, which is around 35% of the whole data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.35, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>pass_angle</th>\n",
       "      <th>pass_height</th>\n",
       "      <th>pass_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.587285</td>\n",
       "      <td>3</td>\n",
       "      <td>24.698177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>-0.909753</td>\n",
       "      <td>3</td>\n",
       "      <td>34.205260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>-2.176341</td>\n",
       "      <td>3</td>\n",
       "      <td>15.811388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>2.265535</td>\n",
       "      <td>3</td>\n",
       "      <td>7.810250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>0.708626</td>\n",
       "      <td>3</td>\n",
       "      <td>9.219544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>1.373401</td>\n",
       "      <td>1</td>\n",
       "      <td>5.099019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-2.356194</td>\n",
       "      <td>3</td>\n",
       "      <td>36.769554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>0.750929</td>\n",
       "      <td>3</td>\n",
       "      <td>20.518284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>-2.574863</td>\n",
       "      <td>3</td>\n",
       "      <td>13.038404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>2.553590</td>\n",
       "      <td>3</td>\n",
       "      <td>10.816654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pass_angle  pass_height  pass_length\n",
       "19     2.587285            3    24.698177\n",
       "790   -0.909753            3    34.205260\n",
       "461   -2.176341            3    15.811388\n",
       "848    2.265535            3     7.810250\n",
       "166    0.708626            3     9.219544\n",
       "..          ...          ...          ...\n",
       "841    1.373401            1     5.099019\n",
       "198   -2.356194            3    36.769554\n",
       "635    0.750929            3    20.518284\n",
       "565   -2.574863            3    13.038404\n",
       "690    2.553590            3    10.816654\n",
       "\n",
       "[309 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we apply the logistic regression and build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We use the training data to fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can now predict on the remaining test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = lr.predict(x_test)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will now generate the *Error Matrix* also known as the *Confusion Matrix* which will tell us how accurate our model is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Predicted', ylabel='Original'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_matrix = pd.crosstab(y_test, y_predicted, rownames=['Original'], colnames=['Predicted'])\n",
    "sns.heatmap(error_matrix, annot=True, cmap = 'plasma', linewidths = 3, linecolor = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8680555555555556"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = metrics.accuracy_score(y_test, y_predicted)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d167ec3bb0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot((y_test - y_predicted), bins = 50, color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}