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R code to measure the change in wOBA weights from one season to a second season
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data_work <- function(){ | |
require(readr) | |
require(dplyr) | |
require(lubridate) | |
sc_2021 <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/statcast2021.csv") | |
sc_2022 <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/statcast_2022.csv") | |
sc_old <- read_csv("https://raw.githubusercontent.com/bayesball/HomeRuns2021/main/SC_BB_mini.csv") | |
names(sc_old)[2] <- "Game_Date" | |
hits <- c("single", "double", "triple", | |
"home_run") | |
sc_2021 %>% | |
mutate(HR = ifelse(events == "home_run", 1, 0), | |
H = ifelse(events %in% hits, 1, 0)) %>% | |
select(game_year, Game_Date, launch_angle, | |
launch_speed, events, HR, H) -> sc_2021 | |
sc_2022 %>% | |
mutate(HR = ifelse(events == "home_run", 1, 0), | |
H = ifelse(events %in% hits, 1, 0)) %>% | |
select(game_year, Game_Date, launch_angle, | |
launch_speed, events, HR, H) -> | |
sc_2022 | |
sc <- rbind(sc_old, sc_2021, sc_2022) | |
sc %>% | |
mutate(Season = year(Game_Date)) | |
} |
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offense_loss <- function(sc_ip, | |
season1, season2, | |
LA, LS){ | |
require(mgcv) | |
require(dplyr) | |
require(ggplot2) | |
require(stringr) | |
sc_ip_1 <- filter(sc_ip, | |
game_year ==season1, | |
launch_angle >= LA[1], | |
launch_angle <= LA[2], | |
launch_speed >= LS[1], | |
launch_speed <= LS[2]) | |
sc_ip_1 %>% | |
mutate(Type_Hit = | |
ifelse(events == "single", 2, | |
ifelse(events == "double", 3, | |
ifelse(events == "triple", 4, | |
ifelse(events == "home_run", 5, 1) | |
)))) -> sc_ip_1 | |
newfit <- gam(Type_Hit ~ s(launch_angle, | |
launch_speed), | |
family = ocat(R = 5), | |
data = sc_ip_1) | |
sc_ip_2 <- filter(sc_ip, | |
game_year ==season2, | |
launch_angle >= LA[1], | |
launch_angle <= LA[2], | |
launch_speed >= LS[1], | |
launch_speed <= LS[2]) | |
LA_breaks <- seq(LA[1], LA[2], by = LA[3]) | |
LS_breaks <- seq(LS[1], LS[2], by = LS[3]) | |
sc_ip_2 %>% | |
mutate(LA = cut(launch_angle, | |
LA_breaks), | |
LS = cut(launch_speed, | |
LS_breaks)) -> sc_ip_2 | |
sc_ip_2 %>% | |
mutate(wOBA_wt = ifelse(events == "single", 0.9, | |
ifelse(events == "double", 1.25, | |
ifelse(events == "triple", 1.6, | |
ifelse(events == "home_run", 2, 0) | |
)))) -> | |
sc_ip_2 | |
probs <- predict(newfit, sc_ip_2, | |
type = "response") | |
sc_ip_2$e_woba <- 0.9 * probs[, 2] + | |
1.25 * probs[, 3] + | |
1.6 * probs[, 4] + | |
2 * probs[, 5] | |
sc_ip_2 %>% | |
group_by(LA, LS) %>% | |
summarize(IP = n(), | |
wOBA = sum(wOBA_wt), | |
E_wOBA = sum(e_woba), | |
Change = wOBA - E_wOBA, | |
Z = Change / sqrt(E_wOBA), | |
.groups = "drop") -> S | |
S %>% | |
filter(is.na(LA) == FALSE) %>% | |
filter(is.na(LS) == FALSE) -> S | |
convert_string <- function(y){ | |
y1 <- gsub("[,(]", " ", y) | |
y2 <- gsub("[][]", "", y1) | |
y3 <- gsub("^ ", "", y2) | |
mean(as.numeric(str_split(y3, " ")[[1]])) | |
} | |
S$la <- sapply(S$LA, convert_string) | |
S$ls <- sapply(S$LS, convert_string) | |
the_plot <- ggplot(S, | |
aes(la, ls, label = round(Z, 2))) + | |
geom_label(size = 6, | |
aes(fill = Z < -3), | |
color = "white") + | |
geom_vline(xintercept = LA_breaks, | |
color = "blue") + | |
geom_hline(yintercept = LS_breaks, | |
color = "blue") + | |
xlim(LA[1], LA[2]) + | |
ylim(LS[1], LS[2]) + | |
scale_fill_manual(values = | |
c("dodgerblue", | |
"red")) + | |
theme(text = element_text(size = 18)) + | |
theme(plot.title = element_text(colour = "red", | |
size = 18, | |
hjust = 0.5, vjust = 0.8, angle = 0), | |
plot.subtitle = element_text(colour = "blue", | |
size = 14, | |
hjust = 0.5, vjust = 0.8, angle = 0)) + | |
xlab("Launch Angle") + | |
ylab("Exit Velocity") + | |
labs(title = paste("Z Change in wOBA - ", | |
season1, "to", season2), | |
subtitle = paste("(games through ", | |
max(sc_ip_2$Game_Date), | |
")", sep = "")) | |
list(S = S, the_plot = the_plot) | |
} | |
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source("data_work.R") | |
source("offense_loss.R") | |
sc_ip <- data_work() | |
LA <- c(0, 50, 10) | |
LS <- c(90, 115, 5) | |
season1 <- 2021 | |
season2 <- 2022 | |
out <- offense_loss(sc_ip, | |
season1, season2, | |
LA, LS) |
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