Post season pitching statistics
A data frame with 6308 observations on the following 30 variables.
Player ID code
Level of playoffs
Team; a factor
League; a factor with levels
Outs Pitched (innings pitched x 3)
Opponents' batting average
Earned Run Average
Batters Hit By Pitch
Batters faced by Pitcher
Sacrifice Hits allowed
Sacrifice Flies allowed
Grounded into Double Plays
Lahman, S. (2022) Lahman's Baseball Database, 1871-2021, 2021 version, https://www.seanlahman.com/baseball-archive/statistics/
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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