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Using Retrosheet game log data to explore game durations for the 2016 season
# game log data available at
# http://www.retrosheet.org/gamelogs/index.html
# csv file with headers available at
# https://github.com/maxtoki/baseball_R/blob/master/data/game_log_header.csv
# my gamelog data for the 2016 season stored in
# data frame d
# this is some theme information that I'll using in
# my ggplot2 plots
TH <- theme(
plot.title = element_text(
colour = "blue",
size = 18,
hjust = 0.5,
vjust = 0.8,
angle = 0
)
)
# load in some packages
library(lubridate)
library(ggplot2)
library(dplyr)
# convert Date variable to number of days past April 1?
d$date <- ymd(d$Date)
d$day <- yday(d$date) - yday("2016-04-01")
# graph game duration against day number
ggplot(d, aes(day, Duration)) + geom_point(size=0.5) +
geom_smooth() +
ggtitle("Length of Game by Date") + TH
# graph game duration against the home team id
ggplot(d, aes(HomeTeam, Duration)) + geom_boxplot() +
coord_flip()
# Day of Week effect?
# I first sort the days of week by the median game length
summarize(group_by(d, DayOfWeek), M=median(Duration)) %>%
arrange(M) -> S
d$DayOfWeek <- factor(d$DayOfWeek,
levels=S$DayOfWeek[order(S$M)])
ggplot(d, aes(DayOfWeek, Duration)) + geom_boxplot() +
coord_flip() +
ggtitle("Length of Game by Day of Week") + TH
# Compute some additional variables
# Number of non-outs, runs scored, number left-on-base,
# left-on-base fraction
d <- mutate(d,
NonOuts = HomeH + HomeBB + HomeHBP +
VisitorH + VisitorBB + VisitorHBP,
Runs = HomeRunsScore + VisitorRunsScored,
LOB = HomeLOB + VisitorLOB,
LOB_Frac = LOB / NonOuts)
# strong relationship of nonouts with duration
ggplot(d, aes(NonOuts, Duration)) +
geom_point(size=0.5) +
geom_smooth() +
ggtitle("Length of Game by NonOuts") + TH
# limit to 9 inning games
d9 <- filter(d, LengthInOuts > 50, LengthInOuts < 55)
# final regression
fit3 <- lm(Duration ~ NonOuts + LOB_Frac, data=d9)
# add residuals to data frame and then plot residuals
d9$Residual <- fit3$residuals
ggplot(d9, aes(NonOuts, Residual)) +
geom_point(size=0.5) + geom_hline(yintercept = 0) +
ggtitle("The Residuals") + TH
# fit is 95.77 + 2.71 * NonOuts + 35.32 * LOB_Frac
# NonOuts (10, 40)
# LOB (.2, .9)
FIT <- NULL
for (lob in seq(.2, .9, by=.3)){
df <- data.frame(NonOuts=10:40, LOB_Frac=lob)
df$Duration <- predict(fit3, df)
df$LOB_Frac <- factor(df$LOB_Frac)
FIT <- rbind(FIT, df)
}
# plot the fit
ggplot(FIT, aes(NonOuts, Duration,
color=LOB_Frac)) + TH +
geom_line() + ggtitle("Predicted Length of Game")
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