R code for illustration of Called Strike package

called_strike_demo.R

# here are the packages I'm using
# CalledStrike depends on other packages

library(CalledStrike)
library(readr)
library(dplyr)
library(ggplot2)

# read in Statcast data for the 2019 season

statcast2019 <- read_csv("~/Dropbox/2016 WORK/BLOG Baseball R/OTHER/StatcastData/statcast2019.csv")

# DJ LeMahieu example

dj <- filter(statcast2019,
             player_name == "DJ LeMahieu")

# scatterplot of in-play locations with hits
# indicated by color variable

dj %>%
  setup_inplay() %>%
  ggplot(aes(plate_x, plate_z,
             color = H)) +
  geom_point() +
  add_zone() +
  increasefont() +
  coord_fixed()

# smoothed hit probabilities over zone

hit_contour(dj,
            L = seq(0, 1, by = 0.02),
            title = "2019 LeMahieu Probability of Hit")

# Swing and contact part

statcast2019 <- read_csv("~/Dropbox/2016 WORK/BLOG Baseball R/OTHER/StatcastData/statcast2019.csv")

# pick up FanGraph batting leaders for 2019 season

fg <- fg_bat_leaders(2019, 2019)

# identify low and high swingers

ggplot(fg, aes(Swing_pct_pi, Contact_pct_pi,
               label = Name)) +
  geom_point(size = 3, color = "blue") +
  geom_text(data = filter(fg,
                          Swing_pct_pi < 37 |
                            Swing_pct_pi > 57),
            nudge_y = -0.6, color = "red") +
  geom_point(data = filter(fg,
                           Swing_pct_pi < 37 |
                             Swing_pct_pi > 57),
             color = "red", size = 3) +
  xlim(30, 62) +
  ggtitle("2019 Swing and Contact Fractions") +
  centertitle() +
  increasefont()

low_swingers <- filter(fg, Swing_pct_pi < 37) %>%
  pull(Name)
high_swingers <- filter(fg, Swing_pct_pi > 57) %>%
  pull(Name)
swing <- c(low_swingers, high_swingers)

# compare nine players on probability of swing

sc2 <- filter(statcast2019,
              player_name %in% swing)
swing_contour(split(sc2,
                    sc2$player_name),
              L = seq(0, 1, by = 0.02),
              NCOL = 3)


# look at Mike Trout

# break down by side of pitcher

statcast2019 %>%
  filter(player_name == "Mike Trout") %>%
  split_LR() %>%
  swing_contour(L = seq(0, 1, by = 0.05),
                title="Mike Trout Swing Rate")


# break down by type of pitch

statcast2019 %>%
  filter(player_name == "Mike Trout") %>%
  split_pitchtype() %>%
  swing_contour(L = seq(0, 1, by = 0.05),
                title="Mike Trout Swing Rate")

# break down by pitch count

statcast2019 %>%
  filter(player_name == "Mike Trout") %>%
  split_count() %>%
  swing_contour(L = seq(0, 1, by = 0.05),
                title="Mike Trout Swing Rate")

# compare with Jeff McNeil

statcast2019 %>%
  filter(player_name == "Jeff McNeil") %>%
  split_count() %>%
  swing_contour(L = seq(0, 1, by = 0.05),
                title="Jeff McNeil Swing Rate")

# look at contact rates

ggplot(fg, aes(Swing_pct_pi, Contact_pct_pi,
               label = Name)) +
  geom_point(size = 3, color = "blue") +
  geom_text(data = filter(fg,
                          Contact_pct_pi < 67.5 |
                            Contact_pct_pi > 86.9),
            nudge_y = -0.6, color = "red") +
  geom_point(data = filter(fg,
                           Contact_pct_pi < 67.5 |
                             Contact_pct_pi > 86.9),
             color = "red", size = 3) +
  xlim(30, 62) +
  ggtitle("2019 Swing and Contact Fractions") +
  centertitle() +
  increasefont()

low_connect <- filter(fg, Contact_pct_pi < 67.5) %>%
  pull(Name)
high_connect <- filter(fg, Contact_pct_pi > 86.9) %>% pull(Name)
connect <- c(low_connect, high_connect)

#  plots of smoothed contact probabilities

sc1 <- filter(statcast2019,
              player_name %in% connect)
contact_swing_contour(split(sc1,
                            sc1$player_name),
                      L = seq(0, 1, by = 0.02),
                      NCOL = 3)

# Luke Voit -- how does eontact prob depend on pitch
# type?

filter(statcast2019,
       player_name == "Luke Voit") %>%
  split_pitchtype() %>%
  contact_swing_contour(L = seq(0, 1, by = 0.05),
                        title =
                          "Luke Voit - Prob(Contact)")

# depend on pitcher side?

filter(statcast2019,
       player_name == "Luke Voit")  %>%
  split_LR() %>%
  contact_swing_contour(L = seq(0, 1, by = 0.05),
                        title = "Luke Voit - Prob(Contact)")


# depend on both pitcher side and pitch type?

filter(statcast2019,
       player_name == "Luke Voit") %>%
  split_LR_pitchtype() %>%
  contact_swing_contour(L = seq(0, 1, by = 0.05),
                        title =
                          "Luke Voit - Prob(Contact)")