PitchingPost: PitchingPost table
In Lahman: Sean 'Lahman' Baseball Database

PitchingPost

R Documentation

PitchingPost table

Description

Post season pitching statistics

Usage

data(PitchingPost)

Format

A data frame with 6757 observations on the following 30 variables.

playerID: Player ID code
yearID: Year
round: Level of playoffs
teamID: Team; a factor
lgID: League; a factor with levels AA AL NL
W: Wins
L: Losses
G: Games
GS: Games Started
CG: Complete Games
SHO: Shutouts
SV: Saves
IPouts: Outs Pitched (innings pitched x 3)
H: Hits
ER: Earned Runs
HR: Homeruns
BB: Walks
SO: Strikeouts
BAOpp: Opponents' batting average
ERA: Earned Run Average
IBB: Intentional Walks
WP: Wild Pitches
HBP: Batters Hit By Pitch
BK: Balks
BFP: Batters faced by Pitcher
GF: Games Finished
R: Runs Allowed
SH: Sacrifice Hits allowed
SF: Sacrifice Flies allowed
GIDP: Grounded into Double Plays

Source

Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/

Examples

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")

Lahman documentation built on Sept. 27, 2024, 1:06 a.m.