Description Usage Format Details Source See Also Examples
Batting table - batting statistics
1 |
A data frame with 97889 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)
Variables X2B
and X3B
are named 2B
and 3B
in the original database
Lahman, S. (2014) Lahman's Baseball Database, 1871-2013, 2014 version, http://baseball1.com/statistics/
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | data(Batting)
head(Batting)
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')
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')
##########################################################
# after Chris Green,
# http://sabr.org/research/baseball-s-first-power-surge-home-runs-late-19th-century-major-leagues
# Total home runs by year
totalHR <- ddply(Batting, .(yearID), summarise,
HomeRuns = sum(as.numeric(HR), na.rm=TRUE),
Games = sum(as.numeric(G_batting), na.rm=TRUE)
)
plot(HomeRuns ~ yearID, data=subset(totalHR, yearID<=1918))
# take games into account?
plot(HomeRuns/Games ~ yearID, data=subset(totalHR, yearID<=1918))
# long term trend?
plot(HomeRuns ~ yearID, data=totalHR)
plot(HomeRuns/Games ~ yearID, data=totalHR)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.