Batting

0th

Percentile

Batting table

Batting table - batting statistics

Keywords
datasets
Usage
data(Batting)
Details

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

Format

A data frame with 107429 observations on the following 22 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

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

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.

Aliases
  • Batting
Examples
# NOT RUN {
data(Batting)
head(Batting)
require("dplyr")

## Prelude: Extract information from Salaries and People
## to be merged with the batting data.

# Subset of Salaries data
salaries <- Salaries %>%
              select(playerID, yearID, teamID, salary)

# Subset of People table (player metadata)
peopleInfo <- People %>%
              select(playerID, birthYear, birthMonth, nameLast,
                     nameFirst, bats)

# Left join salaries and peopleInfo to batting data,
# create an age variable and sort by playerID, yearID and stint
# Returns an ignorable warning.
batting <- battingStats() %>% 
             left_join(salaries, 
                       by =c("playerID", "yearID", "teamID")) %>%
             left_join(peopleInfo, by = "playerID") %>%
             mutate(age = yearID - birthYear - 
                            1L *(birthMonth >= 10)) %>%
             arrange(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 <- batting %>%
                     filter(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, whether they
# won a batting title or not, and add an indicator variable for
# .400 average in a season.

topHitters <- eligibleHitters %>%
                 group_by(yearID) %>%
                 filter(BA == max(BA)| BA >= .400) %>%
                 mutate(ba400 = BA >= 0.400) %>%
                 select(playerID, yearID, nameLast, 
                        nameFirst, BA, ba400)

# Sub-data frame for the .400 hitters plus the outliers after 1950
# (averages above .380) - used to produce labels in the plot below
bignames <- topHitters %>%
              filter(ba400 | (yearID > 1950 & BA > 0.380)) %>%
              arrange(desc(BA))

# Variable to provide a vertical offset to certain
# labels in the ggplot below
bignames$yoffset <-  c(0, 0, 0, 0, 0.002, 0, 0, 0,
                       0.001, -0.001, 0, -0.002, 0, 0,
                       0.002, 0, 0)

# Produce the plot

require("ggplot2")                               
ggplot(topHitters, aes(x = yearID, y = BA)) +
  geom_point(aes(colour = ba400), size = 2.5) +
  geom_hline(yintercept = 0.400, size = 1, colour = "gray70") +
  geom_text(data = bignames, aes(y = BA + yoffset,
                                 label = nameLast), 
                             size = 3, hjust = 1.2) +
  scale_colour_manual(values = c("FALSE" = "black", "TRUE" = "red")) +
  xlim(1899, 2015) +
  xlab("Year") +
  scale_y_continuous("Batting average",
                     limits = c(0.330, 0.430),
                     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 <- Batting %>%
             group_by(yearID) %>%
             summarise(HomeRuns = sum(as.numeric(HR), na.rm=TRUE),
                       Games = sum(as.numeric(G), na.rm=TRUE))

# Plot HR by year, pre-1919 (dead ball era)
totalHR %>% filter(yearID <= 1918) %>%
            ggplot(., aes(x = yearID, y = HomeRuns)) +
               geom_line() +
               geom_point() +
               labs(x = "Year", y = "Home runs hit")

# Take games into account
totalHR %>% filter(yearID <= 1918) %>%
  ggplot(., aes(x = yearID, y = HomeRuns/Games)) +
     geom_line() +
     geom_point() +
     labs(x = "Year", y = "Home runs per game played")

# Widen perspective to all years from 1871
ggplot(totalHR, aes(x = yearID, y = HomeRuns)) +
  geom_point() +
  geom_path() +
  geom_smooth() +
  labs(x = "Year", y = "Home runs hit")

# Similar plot for HR per game played by year -
# shows several eras with spikes in HR hit
ggplot(totalHR, aes(x = yearID, y = HomeRuns/Games)) +
  geom_point() +
  geom_path() +
  geom_smooth(se = FALSE) +
  labs(x = "Year", y = "Home runs per game played")


# }
Documentation reproduced from package Lahman, version 8.0-0, License: GPL

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