# HallOfFame

##### Hall of Fame Voting Data

Hall of Fame table. This is composed of the voting results for all candidates nominated for the Baseball Hall of Fame.

- Keywords
- datasets

##### Usage

`data(HallOfFame)`

##### Details

This table links to the `People`

table via the `playerID`

.

`votedBy`

: Most Hall of Fame inductees have been elected by the
Baseball Writers Association of America (`BBWAA`

). Rules for election are
described in http://en.wikipedia.org/wiki/National_Baseball_Hall_of_Fame_and_Museum#Selection_process.

##### Format

A data frame with 4191 observations on the following 9 variables.

`playerID`

Player ID code

`yearID`

Year of ballot

`votedBy`

Method by which player was voted upon. See Details

`ballots`

Total ballots cast in that year

`needed`

Number of votes needed for selection in that year

`votes`

Total votes received

`inducted`

Whether player was inducted by that vote or not (Y or N)

`category`

Category of candidate; a factor with levels

`Manager`

`Pioneer/Executive`

`Player`

`Umpire`

`needed_note`

Explanation of qualifiers for special elections

##### Examples

```
# NOT RUN {
## Some examples for Hall of Fame induction data
require("dplyr")
require("ggplot2")
############################################################
## Some simple queries
# What are the different types of HOF voters?
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")
# Number of years as HOF candidate, last pct vote, etc.
# for a given player
playerOutcomes <- HallOfFame %>%
filter(votedBy == "BBWAA" & category == "Player") %>%
group_by(playerID) %>%
mutate(nyears = length(ballots)) %>%
arrange(yearID) %>%
do(tail(., 1)) %>%
mutate(lastPct = 100 * round(votes/ballots, 3)) %>%
select(playerID, nyears, inducted, lastPct, yearID) %>%
rename(lastYear = yearID)
############################################################
# How many voting years until election?
inducted <- subset(playerOutcomes, inducted == "Y")
table(inducted$nyears)
# Bar chart of years to induction for inductees
barplot(table(inducted$nyears),
main="Number of voting years until election",
ylab="Number of players", xlab="Years")
box()
# 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$nyears), xlab="Number of years")
# First ballot inductees sorted by vote percentage:
playerOutcomes %>%
filter(nyears == 1L & inducted == "Y") %>%
arrange(desc(lastPct))
# Who took at least ten years on the ballot before induction?
playerOutcomes %>%
filter(nyears >= 10L & inducted == "Y")
############################################################
## Plots of voting percentages over time for the borderline
## HOF candidates, according to the BBWAA:
# Identify players on the BBWAA ballot for at least 10 years
# Returns a character vector of playerIDs
longTimers <- as.character(unlist(subset(playerOutcomes,
nyears >= 10, select = "playerID")))
# Extract their information from the HallOfFame data
HOFlt <- HallOfFame %>%
filter(playerID %in% longTimers & votedBy == "BBWAA") %>%
group_by(playerID) %>%
mutate(elected = ifelse(any(inducted == "Y"),
"Elected", "Not elected"),
pct = 100 * round(votes/ballots, 3))
# Plot the voting profiles:
ggplot(HOFlt, aes(x = yearID, y = pct,
group = playerID)) +
ggtitle("Profiles of BBWAA voting percentage, long-time HOF candidates") +
geom_line() +
geom_hline(yintercept = 75, colour = 'red') +
labs(x = "Year", y = "Percentage of votes") +
facet_wrap(~ elected, ncol = 1)
## Eventual inductees tend to have increasing support over time.
## Fit simple linear regression models to each player's voting
## percentage profile and extract the slopes. Then compare the
## distributions of the slopes in each group.
# data frame for playerID and induction status among
# long term candidates
HOFstatus <- HOFlt %>%
group_by(playerID) %>%
select(playerID, elected, inducted) %>%
do(tail(., 1))
# data frame of regression slopes, which represent average
# increase in percentage support by BBWAA members over a
# player's candidacy.
HOFslope <- HOFlt %>%
group_by(playerID) %>%
do(mod = lm(pct ~ yearID, data = .)) %>%
do(data.frame(slope = coef(.$mod)[2]))
## Boxplots of regression slopes by induction group
ggplot(data.frame(HOFstatus, HOFslope),
aes(x = elected, y = slope)) +
geom_boxplot(width = 0.5) +
geom_point(position = position_jitter(width = 0.2))
# Note 1: Only two players whose maximum voting percentage
# was over 60% were not eventually inducted
# into the HOF: Gil Hodges and Jack Morris.
# Red Ruffing was elected in a 1967 runoff election while
# the others have been voted in by the Veterans Committee.
# Note 2: Of the players whose slope was >= 2.5 among
# non-inductees, only Jack Morris has not (yet) been
# subsequently inducted into the HOF; however, his last year of
# eligibility was 2014 so he could be inducted by a future
# Veterans Committee.
# }
```

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