battingStats: Calculcate additional batting statistics

In Lahman: Sean Lahman's Baseball Database

Description

The Batting does not contain batting statistics derived from those present in the data.frame. This function calculates batting average (BA), plate appearances (PA), total bases (TB), slugging percentage (SlugPct), on-base percentage (OBP), on-base percentage + slugging (OPS), and batting average on balls in play (BABIP) for each record in a Batting-like data.frame.

Usage

battingStats(data = Lahman::Batting, 
             idvars = c("playerID", "yearID", "stint", "teamID", "lgID"), 
             cbind = TRUE)

Arguments

data	input data, typically Batting
idvars	ID variables to include in the output data.frame
cbind	If TRUE, the calculated statistics are appended to the input data as additional columns

Details

Standard calculations, e.g., BA <- H/AB are problematic because of the presence of NAs and zeros. This function tries to deal with those problems.

Value

A data.frame with all the observations in data. If cbind==FALSE, only the idvars and the calculated variables are returned.

Author(s)

Michael Friendly, Dennis Murphy

See Also

Batting, BattingPost

Examples

	bstats <- battingStats()
	str(bstats)
	bstats <- battingStats(cbind=FALSE)
	str(bstats)

Example output

'data.frame':	102816 obs. of  29 variables:
 $ playerID: chr  "abercda01" "addybo01" "allisar01" "allisdo01" ...
 $ yearID  : int  1871 1871 1871 1871 1871 1871 1871 1871 1871 1871 ...
 $ stint   : int  1 1 1 1 1 1 1 1 1 1 ...
 $ teamID  : Factor w/ 149 levels "ALT","ANA","ARI",..: 136 111 39 142 111 56 111 24 56 24 ...
 $ lgID    : Factor w/ 7 levels "AA","AL","FL",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ G       : int  1 25 29 27 25 12 1 31 1 18 ...
 $ AB      : int  4 118 137 133 120 49 4 157 5 86 ...
 $ R       : int  0 30 28 28 29 9 0 66 1 13 ...
 $ H       : int  0 32 40 44 39 11 1 63 1 13 ...
 $ X2B     : int  0 6 4 10 11 2 0 10 1 2 ...
 $ X3B     : int  0 0 5 2 3 1 0 9 0 1 ...
 $ HR      : int  0 0 0 2 0 0 0 0 0 0 ...
 $ RBI     : int  0 13 19 27 16 5 2 34 1 11 ...
 $ SB      : int  0 8 3 1 6 0 0 11 0 1 ...
 $ CS      : int  0 1 1 1 2 1 0 6 0 0 ...
 $ BB      : int  0 4 2 0 2 0 1 13 0 0 ...
 $ SO      : int  0 0 5 2 1 1 0 1 0 0 ...
 $ IBB     : int  NA NA NA NA NA NA NA NA NA NA ...
 $ HBP     : int  NA NA NA NA NA NA NA NA NA NA ...
 $ SH      : int  NA NA NA NA NA NA NA NA NA NA ...
 $ SF      : int  NA NA NA NA NA NA NA NA NA NA ...
 $ GIDP    : int  NA NA NA NA NA NA NA NA NA NA ...
 $ BA      : num  0 0.271 0.292 0.331 0.325 0.224 0.25 0.401 0.2 0.151 ...
 $ PA      : num  4 122 139 133 122 49 5 170 5 86 ...
 $ TB      : num  0 38 54 64 56 15 1 91 2 17 ...
 $ SlugPct : num  0 0.322 0.394 0.481 0.467 0.306 0.25 0.58 0.4 0.198 ...
 $ OBP     : num  0 0.295 0.302 0.331 0.336 0.224 0.4 0.447 0.2 0.151 ...
 $ OPS     : num  0 0.617 0.696 0.812 0.803 ...
 $ BABIP   : num  0 0.271 0.303 0.326 0.328 0.229 0.25 0.404 0.2 0.151 ...
'data.frame':	102816 obs. of  12 variables:
 $ playerID: chr  "abercda01" "addybo01" "allisar01" "allisdo01" ...
 $ yearID  : int  1871 1871 1871 1871 1871 1871 1871 1871 1871 1871 ...
 $ stint   : int  1 1 1 1 1 1 1 1 1 1 ...
 $ teamID  : Factor w/ 149 levels "ALT","ANA","ARI",..: 136 111 39 142 111 56 111 24 56 24 ...
 $ lgID    : Factor w/ 7 levels "AA","AL","FL",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ BA      : num  0 0.271 0.292 0.331 0.325 0.224 0.25 0.401 0.2 0.151 ...
 $ PA      : num  4 122 139 133 122 49 5 170 5 86 ...
 $ TB      : num  0 38 54 64 56 15 1 91 2 17 ...
 $ SlugPct : num  0 0.322 0.394 0.481 0.467 0.306 0.25 0.58 0.4 0.198 ...
 $ OBP     : num  0 0.295 0.302 0.331 0.336 0.224 0.4 0.447 0.2 0.151 ...
 $ OPS     : num  0 0.617 0.696 0.812 0.803 ...
 $ BABIP   : num  0 0.271 0.303 0.326 0.328 0.229 0.25 0.404 0.2 0.151 ...

Lahman documentation built on May 2, 2019, 5:25 p.m.