Batting table

Description

Batting table - batting statistics

Usage

data(Batting)

Format

A data frame with 96600 observations on the following 24 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

G

Games: number of games in which a player played

G_batting

Game as batter

AB

At Bats

R

Runs

H

Hits: times reached base because of a batted, fair ball without error by the defense

X2B

Doubles: hits on which the batter reached second base safely

X3B

Triples: hits on which the batter reached third base safely

HR

Homeruns

RBI

Runs Batted In

SB

Stolen Bases

CS

Caught Stealing

BB

Base on Balls

SO

Strikeouts

IBB

Intentional walks

HBP

Hit by pitch

SH

Sacrifice hits

SF

Sacrifice flies

GIDP

Grounded into double plays

G_old

Old version of games (deprecated)

Details

Variables X2B and X3B are named 2B and 3B in the original database

Source

Lahman, S. (2010) Lahman's Baseball Database, 1871-2012, 2012 version, http://baseball1.com/statistics/

See Also

battingStats for calculating batting average (BA) and other derived statistics

baseball for a similar dataset, but a subset of players who played 15 or more seasons.

Baseball for data on batting in the 1987 season.

Examples

data(Batting)
head(Batting)

##    playerID yearID stint teamID lgID  G G_batting AB R H X2B X3B HR RBI SB
## 1 aardsda01   2004     1    SFN   NL 11        11  0 0 0   0   0  0   0  0
## 2 aardsda01   2006     1    CHN   NL 45        43  2 0 0   0   0  0   0  0
## 3 aardsda01   2007     1    CHA   AL 25         2  0 0 0   0   0  0   0  0
## 4 aardsda01   2008     1    BOS   AL 47         5  1 0 0   0   0  0   0  0
## 5 aardsda01   2009     1    SEA   AL 73         3  0 0 0   0   0  0   0  0
## 6 aardsda01   2010     1    SEA   AL 53         4  0 0 0   0   0  0   0  0
##   CS BB SO IBB HBP SH SF GIDP G_old
## 1  0  0  0   0   0  0  0    0    11
## 2  0  0  0   0   0  1  0    0    45
## 3  0  0  0   0   0  0  0    0     2
## 4  0  0  1   0   0  0  0    0     5
## 5  0  0  0   0   0  0  0    0    NA
## 6  0  0  0   0   0  0  0    0    NA

require('plyr')

# calculate batting average and other stats
batting <- battingStats()

# add salary to Batting data; need to match by player, year and team
batting <- merge(batting, 
                 Salaries[,c("playerID", "yearID", "teamID", "salary")], 
                 by=c("playerID", "yearID", "teamID"), all.x=TRUE)

# Add name, age and bat hand information:
masterInfo <- Master[, c('playerID', 'birthYear', 'birthMonth',
                          'nameLast', 'nameFirst', 'bats')]
batting <- merge(batting, masterInfo, all.x = TRUE)
batting$age <- with(batting, yearID - birthYear -
                             ifelse(birthMonth < 10, 0, 1))

batting <- arrange(batting, playerID, yearID, stint)

## Generate a ggplot similar to the NYT graph in the story about Ted
## Williams and the last .400 MLB season 
# http://www.nytimes.com/interactive/2011/09/18/sports/baseball/WILLIAMS-GRAPHIC.html

# Restrict the pool of eligible players to the years after 1899 and
# players with a minimum of 450 plate appearances (this covers the
# strike year of 1994 when Tony Gwynn hit .394 before play was suspended
# for the season - in a normal year, the minimum number of plate appearances is 502)
eligibleHitters <- subset(batting, yearID >= 1900 & PA > 450)

# Find the hitters with the highest BA in MLB each year (there are a
# few ties).  Include all players with BA > .400
topHitters <- ddply(eligibleHitters, .(yearID), subset, (BA == max(BA))|BA > .400)

# Create a factor variable to distinguish the .400 hitters
topHitters$ba400 <- with(topHitters, BA >= 0.400)

# Sub-data frame for the .400 hitters plus the outliers after 1950
# (averages above .380) - used to produce labels in the plot below
bignames <- rbind(subset(topHitters, ba400),
                  subset(topHitters, yearID > 1950 & BA > 0.380))
# Cut to the relevant set of variables
bignames <- subset(bignames, select = c('playerID', 'yearID', 'nameLast',
                                        'nameFirst', 'BA'))

# Ditto for the original data frame
topHitters <- subset(topHitters, select = c('playerID', 'yearID', 'BA', 'ba400'))

# Positional offsets to spread out certain labels
#                     NL TC JJ TC GS TC RH GS HH RH RH BT TW TW  RC GB TG 
bignames$xoffset <- c(0, 0, 0, 0, 0, 0, 0, 0, -8, 0, 3, 3, 0, 0, -2, 0, 0)
bignames$yoffset <- c(0, 0, -0.003, 0, 0, 0, 0, 0, -0.004, 0, 0, 0, 0, 0, -0.003, 0, 0)  +  0.002

require('ggplot2')                               

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 2.15.3

ggplot(topHitters, aes(x = yearID, y = BA)) +
    geom_point(aes(colour = ba400), size = 2.5) +
    geom_hline(yintercept = 0.400, size = 1) +
    geom_text(data = bignames, aes(x = yearID + xoffset, y = BA + yoffset,
                                   label = nameLast), size = 3) +
    scale_colour_manual(values = c('FALSE' = 'black', 'TRUE' = 'red')) +
    ylim(0.330, 0.430) +
    xlab('Year') +
    scale_y_continuous('Batting average',
                       breaks = seq(0.34, 0.42, by = 0.02),
                       labels = c('.340', '.360', '.380', '.400', '.420')) +
    geom_smooth() +
    theme(legend.position = 'none')

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

## geom_smooth: method="auto" and size of largest group is <1000, so using
## loess. Use 'method = x' to change the smoothing method.