chapter04.R

# 4章
library(tidyverse)
library(Lahman)
tail(Teams, 3)
my_teams <- Teams %>%
  filter(yearID > 2000) %>%
  select(teamID, yearID, lgID, G, W, L, R, RA)
my_teams %>%
  tail()

my_teams <- my_teams %>%
  mutate(RD = R - RA, Wpct = W / (W + L))
run_diff <- ggplot(my_teams, aes(x = RD, y = Wpct)) +
  geom_point() +
  scale_x_continuous("Run differential") +
  scale_y_continuous("Winning percentage")

linfit <- lm(Wpct ~ RD, data = my_teams)
linfit
crcblue <- "#2905a1"
run_diff +
  geom_smooth(method = "lm", se = FALSE, color = crcblue)

library(broom)
my_teams_aug <- augment(linfit, data = my_teams)
base_plot <- ggplot(my_teams_aug, aes(x = RD, y = .resid)) +
  geom_point(alpha = 0.3) +
  geom_hline(yintercept = 0, linetype = 3) +
  xlab("Run differential") + ylab("Residual")
highlight_teams <- my_teams_aug %>%
  arrange(desc(abs(.resid))) %>%
  head(4)

library(ggrepel)
base_plot +
  geom_point(data = highlight_teams, color = crcblue) +
  geom_text_repel(data = highlight_teams, color = crcblue,
                  aes(label = paste(teamID, yearID)))
resid_summary <- my_teams_aug %>%
  summarize(N = n(), avg = mean(.resid),
            RMSE = sqrt(mean(.resid^2)))
resid_summary

rmse <- resid_summary %>%
  pull(RMSE)
my_teams_aug %>%
  summarize(N = n(),
            within_one = sum(abs(.resid) < rmse),
            within_two = sum(abs(.resid) < 2 * rmse)) %>%
  mutate(within_one_pct = within_one / N,
         within_two_pct = within_two / N)

my_teams <- my_teams %>%
  mutate(Wpct_pyt = R ^ 2 / (R ^ 2 + RA ^ 2))
my_teams <- my_teams %>%
  mutate(residuals_pyt = Wpct - Wpct_pyt)
my_teams %>%
  summarize(rmse = sqrt(mean(residuals_pyt^2)))
my_teams <- my_teams %>%
  mutate(logWratio = log(W / L),
         logRratio = log(R / RA))
pytFit <- lm(logWratio ~ 0 + logRratio, data = my_teams)
pytFit

glheaders <- read_csv("data/game_log_header.csv")
gl2011 <- read_csv("data/gl2011.txt",
                   col_names = names(glheaders),
                   na = character())
BOS2011 <- gl2011 %>%
  filter(HomeTeam == "BOS" | VisitingTeam == "BOS") %>%
  select(VisitingTeam, HomeTeam,
         VisitorRunsScored, HomeRunsScore)
head(BOS2011)
BOS2011 <- BOS2011 %>%
  mutate(ScoreDiff = ifelse(HomeTeam == "BOS",
                            HomeRunsScore - VisitorRunsScored,
                            VisitorRunsScored - HomeRunsScore),
         W = ScoreDiff > 0)

library(skimr)
BOS2011 %>%
  group_by(W) %>%
  skim(ScoreDiff)
results <- gl2011 %>%
  select(VisitingTeam, HomeTeam,
         VisitorRunsScored, HomeRunsScore) %>%
  mutate(winner = ifelse(HomeRunsScore > VisitorRunsScored,
                         HomeTeam, VisitingTeam),
         diff = abs(VisitorRunsScored - HomeRunsScore))

one_run_wins <- results %>%
  filter(diff == 1) %>%
  group_by(winner) %>%
  summarize(one_run_w = n())
teams2011 <- my_teams %>%
  filter(yearID == 2011) %>%
  mutate(teamID = ifelse(teamID == "LAA", "ANA",
                         as.character(teamID))) %>%
  inner_join(one_run_wins, by = c("teamID" = "winner"))
ggplot(data = teams2011, aes(x = one_run_w, y = residuals_pyt)) +
  geom_point() +
  geom_text_repel(aes(label = teamID)) +
  xlab("One run wins") + ylab("Pythagorean residuals")

top_closers <- Pitching %>%
  filter(GF > 50 & ERA < 2.5) %>%
  select(playerID, yearID, teamID)
my_teams %>%
  inner_join(top_closers) %>%
  pull(residuals_pyt) %>%
  summary()
D(expression(G * R ^ 2 / (R ^ 2 + RA ^ 2)), "R")
IR <- function(RS = 5, RA = 5){
  (RS ^ 2 + RA ^ 2)^2 / (2 * RS * RA ^ 2)
}

ir_table <- expand.grid(RS = seq(3, 6, .5),
                        RA = seq(3, 6, .5))
head(ir_table)
tail(ir_table)
ir_table %>%
  mutate(IRW = IR(RS, RA)) %>%
  spread(key = RA, value = IRW, sep = "=") %>%
  round(1)