Last data update: 2014.03.03

 R: Pitching table
PitchingR Documentation

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?")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Lahman)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Lahman/Pitching.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Pitching
> ### Title: Pitching table
> ### Aliases: Pitching
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # Pitching data
> 
> require(plyr)
Loading required package: 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)
    playerID yearID stint teamID lgID  W  L  G GS CG SHO SV IPouts   H  ER HR
1  dayleke01   1993     1    TOR   AL  0  0  2  0  0   0  0      2   1   0  0
2   warddu01   1993     1    TOR   AL  2  3 71  0  0   0 45    215  49  17  4
3  eichhma01   1993     1    TOR   AL  3  1 54  0  0   0  0    218  76  22  3
4    coxda01   1993     1    TOR   AL  7  6 44  0  0   0  2    251  73  29  8
5  castito02   1993     1    TOR   AL  3  2 51  0  0   0  0    152  44  19  4
6  hentgpa01   1993     1    TOR   AL 19  9 34 32  3   0  0    649 215  93 27
7  guzmaju01   1993     1    TOR   AL 14  3 33 33  2   1  0    663 211  98 17
8  flenehu01   1993     1    TOR   AL  0  0  6  0  0   0  0     20   7   3  0
9  leiteal01   1993     1    TOR   AL  9  6 34 12  1   1  2    315  93  48  8
10 williwo02   1993     1    TOR   AL  3  1 30  0  0   0  0    111  40  18  2
11 stewada01   1993     1    TOR   AL 12  8 26 26  0   0  0    486 146  80 23
12 timlimi01   1993     1    TOR   AL  4  2 54  0  0   0  1    167  63  29  7
13 stottto01   1993     1    TOR   AL 11 12 30 28  1   1  0    530 204  95 11
14  browsc01   1993     1    TOR   AL  1  1  6  3  0   0  0     54  19  12  2
15 morrija02   1993     1    TOR   AL  7 12 27 27  4   1  0    458 189 105 18
16 lintodo01   1993     1    TOR   AL  0  1  4  1  0   0  0     33  11   8  0
    BB  SO BAOpp  ERA IBB WP HBP BK BFP GF   R WHIP KperBB
1    4   2 0.333 0.00   0  0   0  0   7  0   2 7.50   0.50
2   25  97 0.191 2.13   2  7   1  0 282 70  17 1.03   4.22
3   22  47 0.268 2.72   7  2   3  0 309 16  26 1.35   3.13
4   29  84 0.229 3.12   5  5   0  0 348 13  31 1.22   3.50
5   22  28 0.233 3.38   5  1   0  0 211 10  19 1.30   1.65
6   74 122 0.254 3.87   0 11   7  1 926  0 103 1.34   1.65
7  110 194 0.248 3.99   2 26   3  1 963  0 107 1.45   1.80
8    4   2 0.269 4.05   1  1   0  0  30  1   3 1.65   0.67
9   56  66 0.236 4.11   2  2   4  2 454  4  52 1.42   1.22
10  22  24 0.268 4.38   3  2   1  1 172  9  18 1.68   1.26
11  72  96 0.239 4.44   0  4   4  1 687  0  86 1.35   1.33
12  27  49 0.279 4.69   3  1   1  0 254 27  32 1.62   2.04
13  69  98 0.286 4.84   5  7   3  1 786  0 107 1.55   1.53
14  10   7 0.264 6.00   1  0   1  0  83  1  15 1.61   0.78
15  65 103 0.298 6.19   2 14   3  1 702  0 116 1.66   1.63
16   9   4 0.244 6.55   0  0   1  0  55  0   8 1.82   0.44
> 
> # Career pitching statistics, Greg Maddux
> subset(pitching, playerID == "maddugr01")
       playerID yearID stint teamID lgID  W  L  G GS CG SHO SV IPouts   H  ER
25671 maddugr01   1986     1    CHN   NL  2  4  6  5  1   0  0     93  44  19
26128 maddugr01   1987     1    CHN   NL  6 14 30 27  1   1  0    467 181  97
26586 maddugr01   1988     1    CHN   NL 18  8 34 34  9   3  0    747 230  88
27071 maddugr01   1989     1    CHN   NL 19 12 35 35  7   1  0    715 222  78
27575 maddugr01   1990     1    CHN   NL 15 15 35 35  8   2  0    711 242  91
28099 maddugr01   1991     1    CHN   NL 15 11 37 37  7   2  0    789 232  98
28571 maddugr01   1992     1    CHN   NL 20 11 35 35  9   4  0    804 201  65
29088 maddugr01   1993     1    ATL   NL 20 10 36 36  8   1  0    801 228  70
29604 maddugr01   1994     1    ATL   NL 16  6 25 25 10   3  0    606 150  35
30156 maddugr01   1995     1    ATL   NL 19  2 28 28 10   3  0    629 147  38
30750 maddugr01   1996     1    ATL   NL 15 11 35 35  5   1  0    735 225  74
31357 maddugr01   1997     1    ATL   NL 19  4 33 33  5   2  0    698 200  57
31940 maddugr01   1998     1    ATL   NL 18  9 34 34  9   5  0    753 201  62
32568 maddugr01   1999     1    ATL   NL 19  9 33 33  4   0  0    658 258  87
33219 maddugr01   2000     1    ATL   NL 19  9 35 35  6   3  0    748 225  83
33897 maddugr01   2001     1    ATL   NL 17 11 34 34  3   3  0    699 220  79
34558 maddugr01   2002     1    ATL   NL 16  6 34 34  0   0  0    598 194  58
35231 maddugr01   2003     1    ATL   NL 16 11 36 36  1   0  0    655 225  96
35937 maddugr01   2004     1    CHN   NL 16 11 33 33  2   1  0    638 218  95
36601 maddugr01   2005     1    CHN   NL 13 15 35 35  3   0  0    675 239 106
37264 maddugr01   2006     1    CHN   NL  9 11 22 22  0   0  0    409 153  71
37265 maddugr01   2006     2    LAN   NL  6  3 12 12  0   0  0    221  66  27
37968 maddugr01   2007     1    SDN   NL 14 11 34 34  1   0  0    594 221  91
38683 maddugr01   2008     1    SDN   NL  6  9 26 26  0   0  0    460 161  68
38684 maddugr01   2008     2    LAN   NL  2  4  7  7  0   0  0    122  43  23
      HR BB  SO BAOpp  ERA IBB WP HBP BK  BFP GF   R WHIP KperBB
25671  3 11  20 0.333 5.52   2  2   1  0  144  1  20 1.77   2.22
26128 17 74 101 0.291 5.61  13  4   4  7  701  2 111 1.64   1.66
26586 13 81 140 0.240 3.18  16  3   9  6 1047  0  97 1.25   2.15
27071 13 82 135 0.243 2.95  13  5   6  3 1002  0  90 1.28   1.96
27575 11 71 144 0.259 3.46  10  3   4  3 1011  0 116 1.32   2.36
28099 18 66 198 0.232 3.35   9  6   6  3 1070  0 113 1.13   3.47
28571  7 70 199 0.206 2.18   7  5  14  0 1061  0  68 1.01   3.16
29088 14 52 197 0.227 2.36   7  5   6  1 1064  0  85 1.05   4.38
29604  4 31 156 0.204 1.56   3  3   6  1  774  0  44 0.90   5.57
30156  8 23 181 0.194 1.63   3  1   4  0  785  0  39 0.81   9.05
30750 11 28 172 0.238 2.72  11  4   3  0  978  0  85 1.03  10.12
31357  9 20 177 0.231 2.20   6  0   6  0  893  0  58 0.95  12.64
31940 13 45 204 0.215 2.22  10  4   7  0  987  0  75 0.98   5.83
32568 16 37 136 0.287 3.57   8  1   4  0  940  0 103 1.34   4.69
33219 19 42 190 0.234 3.00  12  1  10  2 1012  0  91 1.07   6.33
33897 20 27 173 0.246 3.05  10  2   7  0  927  0  86 1.06  10.18
34558 14 45 118 0.252 2.62   7  1   4  0  820  0  67 1.20   3.11
35231 24 33 124 0.262 3.96   7  3   8  0  901  0 112 1.18   4.77
35937 35 33 151 0.263 4.02   4  2   9  0  872  0 103 1.18   5.21
36601 29 36 136 0.268 4.24   4  8   7  0  936  0 112 1.22   4.25
37264 14 23  81 0.279 4.69   3  0   0  0  572  0  78 1.29   4.05
37265  6 14  36 0.239 3.30   4  0   0  0  290  0  31 1.09   3.60
37968 14 25 104 0.277 4.14   3  5   6  0  830  0  92 1.24   4.73
38683 16 26  80 0.265 3.99   4  2   5  2  638  0  80 1.22   3.64
38684  5  4  18 0.267 5.09   1  0   1  0  166  0  25 1.16   6.00
> 
> # Best ERAs for starting pitchers post WWII
> postwar <- subset(pitching, yearID >= 1946 & IPouts >= 600)
> head(arrange(postwar, ERA), 10)
    playerID yearID stint teamID lgID  W L  G GS CG SHO SV IPouts   H ER HR BB
1  gibsobo01   1968     1    SLN   NL 22 9 34 34 28  13  0    914 198 38 11 62
2  goodedw01   1985     1    NYN   NL 24 4 35 35 16   8  0    830 198 47 13 69
3  maddugr01   1994     1    ATL   NL 16 6 25 25 10   3  0    606 150 35  4 31
4  tiantlu01   1968     1    CLE   AL 21 9 34 32 19   9  0    775 152 46 16 73
5  maddugr01   1995     1    ATL   NL 19 2 28 28 10   3  0    629 147 38  8 23
6  chancde01   1964     1    LAA   AL 20 9 46 35 15  11  4    835 194 51  7 86
7  koufasa01   1966     1    LAN   NL 27 9 41 41 27   5  0    969 241 62 19 77
8  koufasa01   1964     1    LAN   NL 19 5 29 28 15   7  1    669 154 43 13 53
9  guidrro01   1978     1    NYA   AL 25 3 35 35 16   9  0    821 187 53 13 72
10 martipe02   2000     1    BOS   AL 18 6 29 29  7   4  0    651 128 42 17 32
    SO BAOpp  ERA IBB WP HBP BK  BFP GF  R WHIP KperBB
1  268 0.181 1.12   6  4   7  0 1161  0 49 0.85   4.79
2  268 0.199 1.53   4  6   2  2 1065  0 51 0.97   4.12
3  156 0.204 1.56   3  3   6  1  774  0 44 0.90   5.57
4  264 0.167 1.60   4  3   4  0  987  0 53 0.87   3.83
5  181 0.194 1.63   3  1   4  0  785  0 39 0.81   9.05
6  207 0.193 1.65   9  9   2  0 1093  7 56 1.01   2.69
7  317 0.201 1.73   4  7   0  0 1274  0 74 0.98   4.34
8  223 0.188 1.74   5  9   0  0  870  1 49 0.93   4.65
9  248 0.190 1.74   1  7   1  1 1057  0 61 0.95   3.49
10 284 0.166 1.74   0  1  14  0  817  0 44 0.74   8.88
> 
> # 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)
    playerID yearID stint teamID lgID  W  L  G GS CG SHO SV IPouts   H  ER HR
1  maddugr01   1997     1    ATL   NL 19  4 33 33  5   2  0    698 200  57  9
2  hugheph01   2014     1    MIN   AL 16 10 32 32  1   0  0    629 221  82 16
3  maddugr01   2001     1    ATL   NL 17 11 34 34  3   3  0    699 220  79 20
4  maddugr01   1996     1    ATL   NL 15 11 35 35  5   1  0    735 225  74 11
5  schilcu01   2002     1    ARI   NL 23  7 36 35  5   1  0    778 218  93 29
6  maddugr01   1995     1    ATL   NL 19  2 28 28 10   3  0    629 147  38  8
7  martipe02   2000     1    BOS   AL 18  6 29 29  7   4  0    651 128  42 17
8  martipe02   1999     1    BOS   AL 23  4 31 29  5   1  0    640 160  49  9
9  sheetbe01   2004     1    MIL   NL 12 14 34 34  5   0  0    711 201  71 25
10 jenkife01   1971     1    CHN   NL 24 13 39 39 30   3  0    975 304 100 29
   BB  SO BAOpp  ERA IBB WP HBP BK  BFP GF   R WHIP    KperBB
1  20 177 0.231 2.20   6  0   6  0  893  0  58 0.95 12.642857
2  16 186 0.265 3.52   1  1   5  0  855  0  88 1.13 12.400000
3  27 173 0.246 3.05  10  2   7  0  927  0  86 1.06 10.176471
4  28 172 0.238 2.72  11  4   3  0  978  0  85 1.03 10.117647
5  33 316 0.222 3.23   1  6   3  0 1017  0  95 0.97  9.875000
6  23 181 0.194 1.63   3  1   4  0  785  0  39 0.81  9.050000
7  32 284 0.166 1.74   0  1  14  0  817  0  44 0.74  8.875000
8  37 313 0.203 2.07   1  6   9  0  835  1  56 0.92  8.694444
9  32 264 0.223 2.70   1  8   4  1  937  0  85 0.98  8.516129
10 37 263 0.242 2.77   6  3   5  4 1299  0 114 1.05  8.483871
> 
> # Best K/BB ratios among relievers post-1950 (min. 20 saves)
> head(arrange(subset(pitching, yearID >= 1950 & SV >= 20), desc(KperBB)), 10)
    playerID yearID stint teamID lgID W L  G GS CG SHO SV IPouts  H ER HR BB
1  eckerde01   1990     1    OAK   AL 4 2 63  0  0   0 48    220 41  5  2  4
2  eckerde01   1992     1    OAK   AL 7 1 69  0  0   0 51    240 62 17  5 11
3  eckerde01   1989     1    OAK   AL 4 0 51  0  0   0 33    173 32 10  5  3
4  eckerde01   1991     1    OAK   AL 5 4 67  0  0   0 43    228 60 25 11  9
5  ueharko01   2013     1    BOS   AL 4 1 73  0  0   0 21    223 33  9  5  9
6  riverma01   2008     1    NYA   AL 6 5 64  0  0   0 39    212 41 11  4  6
7  doolise01   2014     1    OAK   AL 2 4 61  0  0   0 22    188 38 19  5  8
8  eckerde01   1996     1    SLN   NL 0 6 63  0  0   0 30    180 65 22  8  6
9  hoffmtr01   2000     1    SDN   NL 4 7 70  0  0   0 43    217 61 24  7 11
10 wagnebi02   2004     1    PHI   NL 4 0 45  0  0   0 21    145 31 13  5  6
    SO BAOpp  ERA IBB WP HBP BK BFP GF  R WHIP KperBB
1   73 0.159 0.61   1  0   0  0 262 61  9 0.61  24.33
2   93 0.209 1.91   6  0   1  0 309 65 17 0.91  18.60
3   55 0.158 1.56   0  0   1  0 206 46 10 0.61  18.33
4   87 0.208 2.96   3  1   1  0 299 59 26 0.91  14.50
5  101 0.129 1.09   2  1   1  0 265 40 10 0.57  14.43
6   77 0.163 1.40   0  1   2  0 259 60 11 0.67  12.83
7   89 0.167 2.73   1  0   0  0 236 40 19 0.73  12.71
8   49 0.270 3.30   2  0   4  0 251 53 26 1.18  12.25
9   85 0.218 2.99   4  4   0  0 291 59 29 1.00  12.14
10  59 0.178 2.42   1  1   2  0 182 38 16 0.77  11.80
> 
> ###############################################
> # 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))
Analysis of Variance Table

Response: W
             Df  Sum Sq Mean Sq  F value    Pr(>F)    
yearID        1 1911.58 1911.58 210.0022 < 2.2e-16 ***
I(yearID^2)   1  204.63  204.63  22.4807 3.304e-06 ***
lgID          1   28.68   28.68   3.1511   0.07690 .  
throws        1   32.76   32.76   3.5984   0.05881 .  
Residuals   296 2694.39    9.10                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> 
> # an eye-catching, but naive, specious graph 
> 
> require('ggplot2') 
Loading required package: 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?")
> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


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