Lahman (version 4.0-1)

Pitching: Pitching table

Description

Pitching table

Usage

data(Pitching)

Format

A data frame with 43330 observations on the following 30 variables.

playerID

Player ID code

yearID

Year

stint

player's stint (order of appearances within a season)

teamID

Team; a factor

lgID

League; a factor with levels AA AL FL NL PL UA

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

Opponent's 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

Sacrifices by opposing batters

SF

Sacrifice flies by opposing batters

GIDP

Grounded into double plays by opposing batter

Source

Lahman, S. (2015) Lahman's Baseball Database, 1871-2014, 2015 version, http://baseball1.com/statistics/

Examples

# Pitching data

require(plyr)

###################################
# cleanup, and add some other stats
###################################

# Restrict to AL and NL data, 1901+
# All data re SH, SF and GIDP are missing, so remove
# Intentional walks (IBB) not recorded until 1955
pitching <- subset(Pitching, yearID >= 1901 & lgID %in% c("AL", "NL"))[, -(28:30)]

# Approximate missing BAOpp values (most common remaining missing value)
pitching$BAOpp <- with(pitching, round(H/(BFP - BB - HBP), 3))
# Compute WHIP (hits + walks per inning pitched -- lower is better)
pitching <- mutate(pitching, 
                   WHIP = round((H + BB) * 3/IPouts, 2),
                   KperBB = round(ifelse(yearID >= 1955, 
                                         SO/(BB - IBB), SO/BB), 2))

#####################
# some simple queries
#####################

# Team pitching statistics, Toronto Blue Jays, 1993
tor93 <- subset(pitching, yearID == 1993 & teamID == "TOR")
arrange(tor93, ERA)

# Career pitching statistics, Greg Maddux
subset(pitching, playerID == "maddugr01")

# Best ERAs for starting pitchers post WWII
postwar <- subset(pitching, yearID >= 1946 & IPouts >= 600)
head(arrange(postwar, ERA), 10)

# Best K/BB ratios post-1955 among starters (excludes intentional walks)
post55 <- subset(pitching, yearID >= 1955 & IPouts >= 600)
post55 <- mutate(post55, KperBB = SO/(BB - IBB))
head(arrange(post55, desc(KperBB)), 10)

# Best K/BB ratios among relievers post-1950 (min. 20 saves)
head(arrange(subset(pitching, yearID >= 1950 & SV >= 20), desc(KperBB)), 10)

###############################################
# Winningest pitchers in each league each year:
###############################################

# Add name & throws information:
masterInfo <- Master[, c('playerID',
                         'nameLast', 'nameFirst', 'throws')]
pitching <- merge(pitching, masterInfo, all.x=TRUE)

wp <- ddply(pitching, .(yearID, lgID), subset, W == max(W), 
         select = c("playerID", "teamID", "W", "throws"))

anova(lm(formula = W ~ yearID + I(yearID^2) + lgID + throws, data = wp))

# an eye-catching, but naive, specious graph 

require('ggplot2') 
# compare loess smooth with quadratic fit                              
ggplot(wp, aes(x = yearID, y = W)) +
    geom_point(aes(colour = throws, shape=lgID), size = 2) +
    geom_smooth(method="loess", size=1.5, color="blue") +
    geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ poly(x,2)) +
    ylab("Maximum Wins") + xlab("Year") +
    ggtitle("Why can't pitchers win 30+ games any more?")