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PitchingPost table

PitchingPost.Rd

Post season pitching statistics

Usage

data(PitchingPost)

Format

A data frame with 6991 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. (2025) Lahman's Baseball Database, 1871-2024, 2025 version, https://sabr.org/lahman-database/

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) 
#>     playerID yearID teamID lgID IPouts W L G CG SHO  H R SO BFP
#> 1   hoytwa01   1921    NYA   AL     81 2 1 3  3   1 18 2 18 106
#> 2  mathech01   1905    NY1   NL     81 3 0 3  3   3 13 0 18  94
#> 3  hubbeca01   1933    NY1   NL     60 2 0 2  2   0 13 3 15  80
#> 4   fordwh01   1960    NYA   AL     54 2 0 2  2   2 11 0  8  65
#> 5  mcginjo01   1905    NY1   NL     51 1 1 2  1   1 10 3  6  65
#> 6  mailsdu01   1920    CLE   AL     47 1 0 2  1   1  6 0  6  58
#> 7  bentoru01   1917    NY1   NL     42 1 1 2  1   1  9 3  8  53
#> 8   fordwh01   1961    NYA   AL     42 2 0 2  1   1  6 0  7  49
#> 9  billija01   1972    CIN   NL     41 1 0 3  0   0  6 1 11  50
#> 10 dobsojo01   1946    BOS   AL     38 1 0 3  1   0  4 3 10  48
#> 11 reynoal01   1949    NYA   AL     37 1 0 2  1   1  2 0 14  43
#> 12 labincl01   1956    BRO   NL     36 1 0 2  1   1  8 1  7  47
#> 13 brownmo01   1908    CHN   NL     33 2 0 2  1   1  6 1  5  40
#> 14 jamesbi02   1914    BSN   NL     33 2 0 2  1   1  2 0  9  37
#> 15 krameja01   1944    SLA   AL     33 1 0 2  1   0  9 2 12  46
#> 16 beardge01   1948    CLE   AL     32 1 0 2  1   1  6 0  4  36
#> 17 larsedo01   1956    NYA   AL     32 1 0 2  1   1  1 4  7  37

# 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)
#>     playerID yearID teamID lgID IPouts W L G CG SHO  H SO BFP  ERA
#> 1  phillde01   1903    PIT   NL    132 3 2 5  5   0 38 22 178 3.07
#> 2  dinnebi01   1903    BOS   AL    105 3 1 4  4   2 29 28 134 2.06
#> 3  youngcy01   1903    BOS   AL    102 2 1 4  3   0 31 17 142 1.85
#> 4  mullige01   1909    DET   AL     96 2 1 4  3   1 23 20 132 2.25
#> 5  mathech01   1912    NY1   NL     86 0 2 3  3   0 23 10 115 0.94
#> 6  spahnwa01   1958    ML1   NL     86 2 1 3  2   1 19 18 113 2.20
#> 7  adamsba01   1909    PIT   NL     81 3 0 3  3   1 18 11 106 1.33
#> 8  burdele01   1957    ML1   NL     81 3 0 3  3   2 21 13 104 0.67
#> 9  coombja01   1910    PHA   AL     81 3 0 3  3   0 24 17 116 3.33
#> 10 covelst01   1920    CLE   AL     81 3 0 3  3   1 15  8  97 0.67

# 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)
#>     playerID yearID teamID lgID IPouts  H SO    K_rate
#> 1  gibsobo01   1968    SLN   NL     81 18 35 11.666667
#> 2  schilcu01   2001    ARI   NL     64 12 26 10.968750
#> 3  koufasa01   1965    LAN   NL     72 13 29 10.875000
#> 4  gibsobo01   1964    SLN   NL     81 23 31 10.333333
#> 5  newhoha01   1945    DET   AL     62 25 22  9.580645
#> 6  gibsobo01   1967    SLN   NL     81 14 26  8.666667
#> 7   woodjo02   1912    BOS   AL     66 27 21  8.590909
#> 8  reynoal01   1952    NYA   AL     61 12 18  7.967213
#> 9  alexape01   1926    SLN   NL     61 12 17  7.524590
#> 10 earnsge01   1931    PHA   AL     72 12 20  7.500000
  
# 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)
#> # A tibble: 10 × 10
#>    playerID  IPouts    CG    BB    SO     W     L   ERA Kper9  WHIP
#>    <chr>      <int> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#>  1 fordwh01     438     7    34    94    10     8  2.71  5.80 1.14 
#>  2 mathech01    305    10    10    48     5     5  0.97  4.25 0.836
#>  3 ruffire01    257     8    27    61     7     2  2.63  6.41 1.18 
#>  4 bendech01    255     9    21    59     6     4  2.44  6.25 1.01 
#>  5 hoytwa01     251     6    22    49     6     4  1.83  5.27 1.23 
#>  6 gibsobo01    243     8    17    92     7     2  1.89 10.2  0.889
#>  7 nehfar01     237     6    32    28     4     4  2.16  3.19 1.04 
#>  8 pettian01    233     0    26    56     5     4  4.06  6.49 1.40 
#>  9 reynoal01    232     5    32    62     7     2  2.79  7.22 1.20 
#> 10 palmeji01    194     1    31    44     4     2  3.2   6.12 1.33 

# 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")
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'