Does Plate Discipline Erode

erode_work.R

# read in Statcast data for two seasons

library(tidyverse)
sc <- read_csv("../StatcastData/statcast2018new.csv")
sc17 <- read_csv("../StatcastData/statcast2017.csv")

# erode function will complete individual regression estimates for all
# players who have seen 1000 called pitches

erode <- function(sc){
  require(tidyverse)
  require(lubridate)
  require(broom)

  sc %>% filter(description %in% c("ball", "blocked_ball",
                                 "called_strike")) %>% 
  mutate(Months = (as.numeric(ymd(game_date) - 
                          ymd(min(sc$game_date)))) / 30) %>%
  select(description, player_name, Months) -> sc_called

  sc_called %>% group_by(player_name) %>% 
    summarize(Called = n())  %>% 
    inner_join(sc_called)  %>% 
    filter(Called >= 1000) -> sc_called_1000

  regressions <- sc_called_1000 %>% 
    split(pull(., player_name)) %>% 
    map(~ glm(description == "ball" ~ Months, 
            data = ., 
            family = binomial)) %>% 
    map_df(tidy, .id = "Name") %>% 
    as_tibble()

  regressions %>% select(Name, term, estimate) %>% 
  spread(term, estimate) -> reg
  names(reg)[2] <- "Intercept"
  return(reg)
}

# get estimates for the two seasons
erode(sc) -> reg_18
erode(sc17) -> reg_17

# graph of estimates using 2018 data

library(ggrepel)
library(ggplot2)
ggplot(reg_18, aes(Intercept, Months, label = Name)) +
  geom_point() +
  geom_label_repel(data = filter(reg_18, Intercept > 1.0 |
                                   Intercept < 0.15 |
                                   Months > 0.10)) +
  geom_hline(yintercept = 0, color = "red") +
  ggtitle("Intercept and Slope Estimates for the Individual Logistic Regressions")
  
 # merge the estimates for the two seasons 
 # construct scatterplots of the estimates

inner_join(reg_17, reg_18, by = "Name") -> two_years

p1 <- ggplot(two_years, aes(Intercept.x, Intercept.y)) +
  geom_point() + 
  xlab("2017 Intercept Estimate") +
  ylab("2018 Intercept Estimate") +
  ggtitle("Scatterplot of Intercept Estimates: Correlation = 0.513") +
  theme(plot.title = element_text(colour = "blue",
                        size = 18, hjust = 0.5))

p2 <- ggplot(two_years, aes(Months.x, Months.y)) +
  geom_point() + 
  xlab("2017 Slope Estimate") +
  ylab("2018 Slope Estimate") +
  ggtitle("Scatterplot of Slope Estimates: Correlation = 0.056") +
  theme(plot.title = element_text(colour = "blue",
       size = 18, hjust = 0.5))

cor(select(two_years, Intercept.x, Intercept.y,
           Months.x, Months.y))

library(gridExtra)
grid.arrange(p1, p2)