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Last data update: 2014.03.03 |
Hall of Fame Voting DataDescriptionHall of Fame table. This is comprised of the voting results for all candidates nominated for the Baseball Hall of Fame. Usagedata(HallOfFame) FormatA data frame with 4088 observations on the following 9 variables.
DetailsThis table links to the
SourceLahman, S. (2015) Lahman's Baseball Database, 1871-2014, 2015 version, http://baseball1.com/statistics/ Examples
## Some examples for Hall of Fame induction data
data('HallOfFame')
require('plyr') ## extensive use of plyr for data manipulation
require('ggplot2')
############################################################
## Some simple queries
# What are the different types of votedBy?
table(HallOfFame$votedBy)
# What was the first year of Hall of Fame elections?
sort(unique(HallOfFame$yearID))[1]
# Who comprised the original class?
subset(HallOfFame, yearID == 1936 & inducted == 'Y')
# Result of a player's last year on the BBWAA ballot
# Restrict to players voted by BBWAA:
HOFplayers <- subset(HallOfFame, votedBy == 'BBWAA' & category == 'Player')
# Function to calculate number of years as HOF candidate, last pct vote, etc.
# for a given player
HOFun <- function(d) {
nyears <- nrow(d)
fy <- d[nyears, ]
lastPct <- with(fy, 100 * round(votes/ballots, 3))
data.frame(playerID = fy$playerID, nyears, induct = fy$inducted,
lastPct, lastYear = fy$yearID)
}
playerOutcomesHOF <- ddply(HOFplayers, .(playerID), HOFun)
############################################################
# How many voting years until election?
inducted <- subset(playerOutcomesHOF,induct == 'Y')
table(inducted$nyears)
barplot(table(inducted$nyears), main="Number of voting years until election",
ylab="Number of players", xlab="Years")
# What is the form of this distribution?
require('vcd')
goodfit(inducted$nyears)
plot(goodfit(inducted$nyears), xlab='Number of years',
main="Poissonness plot of number of years voting until election")
Ord_plot(table(inducted$nyears), xlab='Number of years')
# First ballot inductees:
subset(playerOutcomesHOF, nyears == 1L & induct == 'Y')
# Who took at least ten years on the ballot before induction?
# (Doesn't include Bert Blyleven, who was inducted in 2011.)
subset(playerOutcomesHOF, nyears >= 10L & induct == 'Y')
############################################################
## Plots of voting percentages over time for the borderline
## HOF candidates, according to the BBWAA:
# (1) Set up the data:
longTimers <- as.character(unlist(subset(playerOutcomesHOF,
nyears >= 10, select = 'playerID')))
HOFlt <- subset(HallOfFame, playerID %in% longTimers & votedBy == 'BBWAA')
HOFlt <- ddply(HOFlt, .(playerID), mutate,
elected = ifelse(any(inducted == 'Y'),"Elected", "Not elected"),
pct = 100 * round(votes/ballots, 3))
# Plot the voting profiles:
ggplot(HOFlt, aes(x = yearID, y = pct,
group = playerID)) +
ggtitle("Profiles of voting percentage for long-time HOF candidates") +
geom_line() +
geom_hline(yintercept = 75, col = 'red') +
labs(list(x = "Year", y = "Percentage of votes")) +
facet_wrap(~ elected, ncol = 1)
# Note: All but one of the players whose maximum voting percentage
# was over 60% and was not elected by the BBWAA has eventually been inducted
# into the HOF. Red Ruffing was elected in a 1967 runoff election while
# the others have been voted in by the Veterans Committee. The lone
# exception is Gil Hodges; his profile is the one that flatlines around 60%
# for several years in the late 70s and early 80s.
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'q()' to quit R.
> library(Lahman)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Lahman/HallOfFame.Rd_%03d_medium.png", width=480, height=480)
> ### Name: HallOfFame
> ### Title: Hall of Fame Voting Data
> ### Aliases: HallOfFame
> ### Keywords: datasets
>
> ### ** Examples
>
> ## Some examples for Hall of Fame induction data
>
> data('HallOfFame')
> require('plyr') ## extensive use of plyr for data manipulation
Loading required package: plyr
> require('ggplot2')
Loading required package: ggplot2
>
> ############################################################
> ## Some simple queries
>
> # What are the different types of votedBy?
> table(HallOfFame$votedBy)
BBWAA Centennial Final Ballot Negro League
3657 6 21 26
Nominating Vote Old Timers Run Off Special Election
76 30 81 2
Veterans
189
>
> # What was the first year of Hall of Fame elections?
> sort(unique(HallOfFame$yearID))[1]
[1] 1936
> # Who comprised the original class?
> subset(HallOfFame, yearID == 1936 & inducted == 'Y')
playerID yearID votedBy ballots needed votes inducted category needed_note
1 cobbty01 1936 BBWAA 226 170 222 Y Player <NA>
2 ruthba01 1936 BBWAA 226 170 215 Y Player <NA>
3 wagneho01 1936 BBWAA 226 170 215 Y Player <NA>
4 mathech01 1936 BBWAA 226 170 205 Y Player <NA>
5 johnswa01 1936 BBWAA 226 170 189 Y Player <NA>
>
> # Result of a player's last year on the BBWAA ballot
> # Restrict to players voted by BBWAA:
> HOFplayers <- subset(HallOfFame, votedBy == 'BBWAA' & category == 'Player')
>
>
> # Function to calculate number of years as HOF candidate, last pct vote, etc.
> # for a given player
> HOFun <- function(d) {
+ nyears <- nrow(d)
+ fy <- d[nyears, ]
+ lastPct <- with(fy, 100 * round(votes/ballots, 3))
+ data.frame(playerID = fy$playerID, nyears, induct = fy$inducted,
+ lastPct, lastYear = fy$yearID)
+ }
>
> playerOutcomesHOF <- ddply(HOFplayers, .(playerID), HOFun)
>
>
> ############################################################
> # How many voting years until election?
> inducted <- subset(playerOutcomesHOF,induct == 'Y')
> table(inducted$nyears)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
49 10 9 7 8 4 2 4 6 3 3 1 4 1 2
> barplot(table(inducted$nyears), main="Number of voting years until election",
+ ylab="Number of players", xlab="Years")
>
> # What is the form of this distribution?
> require('vcd')
Loading required package: vcd
Loading required package: grid
> goodfit(inducted$nyears)
Observed and fitted values for poisson distribution
with parameters estimated by `ML'
count observed fitted pearson residual
0 0 1.945348067 -1.394757
1 49 7.901900557 14.620289
2 10 16.048550247 -1.509849
3 9 21.729452989 -2.730771
4 7 22.065971066 -3.207271
5 8 17.926160565 -2.344432
6 4 12.135852064 -2.335436
7 2 7.042169529 -1.900046
8 4 3.575614838 0.224432
9 6 1.613773069 3.452788
10 3 0.655506052 2.895748
11 3 0.242057343 5.605652
12 1 0.081935340 3.207286
13 4 0.025601308 24.839357
14 1 0.007427939 11.516695
15 2 0.002011460 38.588634
> plot(goodfit(inducted$nyears), xlab='Number of years',
+ main="Poissonness plot of number of years voting until election")
> Ord_plot(table(inducted$nyears), xlab='Number of years')
>
>
>
> # First ballot inductees:
> subset(playerOutcomesHOF, nyears == 1L & induct == 'Y')
playerID nyears induct lastPct lastYear
1 aaronha01 1 Y 97.8 1982
41 bankser01 1 Y 83.8 1977
60 benchjo01 1 Y 96.4 1989
87 boggswa01 1 Y 91.9 2005
106 brettge01 1 Y 98.2 1999
111 brocklo01 1 Y 79.7 1985
151 carewro01 1 Y 90.5 1991
153 carltst01 1 Y 95.6 1994
188 cobbty01 1 Y 98.2 1936
283 eckerde01 1 Y 83.2 2004
304 fellebo01 1 Y 93.8 1962
355 gibsobo01 1 Y 84.0 1981
360 glavito01 1 Y 91.9 2014
402 gwynnto01 1 Y 97.6 2007
429 henderi01 1 Y 94.8 2009
478 jacksre01 1 Y 93.6 1993
491 johnsra05 1 Y 97.3 2015
492 johnswa01 1 Y 83.6 1936
510 kalinal01 1 Y 88.3 1980
542 koufasa01 1 Y 86.9 1972
601 maddugr01 1 Y 97.2 2014
607 mantlmi01 1 Y 88.2 1974
621 martipe02 1 Y 91.1 2015
623 mathech01 1 Y 90.7 1936
635 mayswi01 1 Y 94.7 1979
641 mccovwi01 1 Y 81.4 1986
683 molitpa01 1 Y 85.2 2004
694 morgajo02 1 Y 81.8 1990
708 murraed02 1 Y 85.3 2003
711 musiast01 1 Y 93.2 1969
748 palmeji01 1 Y 92.6 1990
788 puckeki01 1 Y 82.1 2001
821 ripkeca01 1 Y 98.5 2007
829 robinbr01 1 Y 92.0 1983
830 robinfr02 1 Y 89.2 1982
831 robinja02 1 Y 77.5 1962
851 ruthba01 1 Y 95.1 1936
853 ryanno01 1 Y 98.8 1999
874 schmimi01 1 Y 96.5 1995
884 seaveto01 1 Y 98.8 1992
918 smithoz01 1 Y 91.7 2002
921 smoltjo01 1 Y 82.9 2015
937 stargwi01 1 Y 82.4 1988
976 thomafr04 1 Y 83.7 2014
1021 wagneho01 1 Y 95.1 1936
1063 willite01 1 Y 93.4 1966
1069 winfida01 1 Y 84.5 2001
1082 yastrca01 1 Y 94.6 1989
1090 yountro01 1 Y 77.5 1999
>
> # Who took at least ten years on the ballot before induction?
> # (Doesn't include Bert Blyleven, who was inducted in 2011.)
> subset(playerOutcomesHOF, nyears >= 10L & induct == 'Y')
playerID nyears induct lastPct lastYear
83 blylebe01 14 Y 79.7 2011
97 boudrlo01 10 Y 77.3 1970
216 cronijo01 10 Y 78.8 1956
271 drysddo01 10 Y 78.4 1984
420 hartnga01 11 Y 77.7 1955
425 heilmha01 11 Y 86.8 1952
528 kinerra01 13 Y 75.4 1975
570 lemonbo01 12 Y 78.6 1976
609 maranra01 13 Y 82.9 1954
813 riceji01 15 Y 76.4 2009
922 snidedu01 11 Y 86.5 1980
960 suttebr01 13 Y 76.9 2006
972 terrybi01 13 Y 77.4 1954
1007 vanceda01 15 Y 81.7 1955
>
> ############################################################
> ## Plots of voting percentages over time for the borderline
> ## HOF candidates, according to the BBWAA:
>
> # (1) Set up the data:
> longTimers <- as.character(unlist(subset(playerOutcomesHOF,
+ nyears >= 10, select = 'playerID')))
> HOFlt <- subset(HallOfFame, playerID %in% longTimers & votedBy == 'BBWAA')
> HOFlt <- ddply(HOFlt, .(playerID), mutate,
+ elected = ifelse(any(inducted == 'Y'),"Elected", "Not elected"),
+ pct = 100 * round(votes/ballots, 3))
>
> # Plot the voting profiles:
> ggplot(HOFlt, aes(x = yearID, y = pct,
+ group = playerID)) +
+ ggtitle("Profiles of voting percentage for long-time HOF candidates") +
+ geom_line() +
+ geom_hline(yintercept = 75, col = 'red') +
+ labs(list(x = "Year", y = "Percentage of votes")) +
+ facet_wrap(~ elected, ncol = 1)
>
> # Note: All but one of the players whose maximum voting percentage
> # was over 60% and was not elected by the BBWAA has eventually been inducted
> # into the HOF. Red Ruffing was elected in a 1967 runoff election while
> # the others have been voted in by the Veterans Committee. The lone
> # exception is Gil Hodges; his profile is the one that flatlines around 60%
> # for several years in the late 70s and early 80s.
>
>
>
>
>
> dev.off()
null device
1
>
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Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and