# NOT RUN {
library("dplyr")
library(ggplot2)
# Restrict data to World Series in modern era
ws <- PitchingPost %>%
filter(yearID >= 1903 & round == "WS")
# Pitchers with ERA 0.00 in WS play (> 10 IP)
ws %>%
filter(IPouts > 30 & ERA == 0.00) %>%
arrange(desc(IPouts)) %>%
select(playerID, yearID, teamID, lgID, IPouts, W, L, G,
CG, SHO, H, R, SO, BFP)
# Pitchers with the most IP in a series
# 1903 Series went eight games - for details, see
# https://en.wikipedia.org/wiki/1903_World_Series
ws %>%
arrange(desc(IPouts)) %>%
select(playerID, yearID, teamID, lgID, IPouts, W, L, G,
CG, SHO, H, SO, BFP, ERA) %>%
head(., 10)
# Pitchers with highest strikeout rate in WS
# (minimum 20 IP)
ws %>%
filter(IPouts >= 60) %>%
mutate(K_rate = 27 * SO/IPouts) %>%
arrange(desc(K_rate)) %>%
select(playerID, yearID, teamID, lgID, IPouts,
H, SO, K_rate) %>%
head(., 10)
# Pitchers with the most IP in WS history
ws %>%
group_by(playerID) %>%
summarise_at(vars(IPouts, H, ER, CG, BB, SO, W, L),
sum, na.rm = TRUE) %>%
mutate(ERA = round(27 * ER/IPouts, 2),
Kper9 = round(27 * SO/IPouts, 3),
WHIP = round(3 * (H + BB)/IPouts, 3)) %>%
arrange(desc(IPouts)) %>%
select(-H, -ER) %>%
head(., 10)
# Plot of K/9 by year
ws %>%
group_by(yearID) %>%
summarise(Kper9 = 27 * sum(SO)/sum(IPouts)) %>%
ggplot(., aes(x = yearID, y = Kper9)) +
geom_point() +
geom_smooth() +
labs(x = "Year", y = "K per 9 innings")
# }
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