PitchingPost {Lahman} | R Documentation

## PitchingPost table

### Description

Post season pitching statistics

### Usage

    data(PitchingPost)

### Format

A data frame with 5271 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. (2017) Lahman's Baseball Database, 1871-2016, 2016 version, http://www.seanlahman.com/baseball-archive/statistics/

### 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")

[Package Lahman version 6.0-0 Index]