# Managers

From Lahman v8.0-0
by Chris Dalzell

##### Managers table

Managers table: information about individual team managers, teams they managed and some basic statistics for those teams in each year.

- Keywords
- datasets

##### Usage

`data(Managers)`

##### Format

A data frame with 3536 observations on the following 10 variables.

`playerID`

Manager (player) ID code

`yearID`

Year

`teamID`

Team; a factor

`lgID`

League; a factor with levels

`AA`

`AL`

`FL`

`NL`

`PL`

`UA`

`inseason`

Managerial order. Zero if the individual managed the team the entire year. Otherwise denotes where the manager appeared in the managerial order (1 for first manager, 2 for second, etc.)

`G`

Games managed

`W`

Wins

`L`

Losses

`rank`

Team's final position in standings that year

`plyrMgr`

Player Manager (denoted by 'Y'); a factor with levels

`N`

`Y`

##### Examples

```
# NOT RUN {
####################################
# Basic career summaries by manager
####################################
library("dplyr")
mgrSumm <- Managers %>%
group_by(playerID) %>%
summarise(nyear = length(unique(yearID)),
yearBegin = min(yearID),
yearEnd = max(yearID),
nTeams = length(unique(teamID)),
nfirst = sum(rank == 1L),
W = sum(W),
L = sum(L),
WinPct = round(W/(W + L), 3))
MgrInfo <- People %>%
filter(!is.na(playerID)) %>%
select(playerID, nameLast, nameFirst)
# Merge names into the table
mgrTotals <- right_join(MgrInfo, mgrSumm, by = "playerID")
# add total games managed
mgrTotals <- mgrTotals %>%
mutate(games = W + L)
##########################
# Some basic queries
##########################
# Top 20 managers in terms of years of service:
mgrTotals %>%
arrange(desc(nyear)) %>%
head(., 20)
# Top 20 winningest managers (500 games minimum)
mgrTotals %>%
filter((W + L) >= 500) %>%
arrange(desc(WinPct)) %>%
head(., 20)
# Most of these are 19th century managers.
# How about the modern era?
mgrTotals %>%
filter(yearBegin >= 1901 & (W + L) >= 500) %>%
arrange(desc(WinPct)) %>%
head(., 20)
# Top 10 managers in terms of percentage of titles
# (league or divisional) - should bias toward managers
# post-1970 since more first place finishes are available
mgrTotals %>%
filter(yearBegin >= 1901 & (W + L) >= 500) %>%
arrange(desc(round(nfirst/nyear, 3))) %>%
head(., 10)
# How about pre-1969?
mgrTotals %>%
filter(yearBegin >= 1901 & yearEnd <= 1969 &
(W + L) >= 500) %>%
arrange(desc(round(nfirst/nyear, 3))) %>%
head(., 10)
## Tony LaRussa's managerial record by team
Managers %>%
filter(playerID == "larusto01") %>%
group_by(teamID) %>%
summarise(nyear = length(unique(yearID)),
yearBegin = min(yearID),
yearEnd = max(yearID),
games = sum(G),
nfirst = sum(rank == 1L),
W = sum(W),
L = sum(L),
WinPct = round(W/(W + L), 3))
##############################################
# Density plot of the number of games managed:
##############################################
library("ggplot2")
ggplot(mgrTotals, aes(x = games)) +
geom_density(fill = "red", alpha = 0.3) +
labs(x = "Number of games managed")
# Who managed more than 4000 games?
mgrTotals %>%
filter(W + L >= 4000) %>%
arrange(desc(W + L))
# Connie Mack's advantage: he owned the Philadelphia A's :)
# Table of Tony LaRussa's team finishes (rank order):
Managers %>%
filter(playerID == "larusto01") %>%
count(rank)
##############################################
# Scatterplot of winning percentage vs. number
# of games managed (min 100)
##############################################
ggplot(subset(mgrTotals, yearBegin >= 1900 & games >= 100),
aes(x = games, y = WinPct)) +
geom_point() + geom_smooth() +
labs(x = "Number of games managed")
############################################
# Division titles
############################################
# Plot of number of first place finishes by managers who
# started in the divisional era (>= 1969) with
# at least 8 years of experience
mgrTotals %>%
filter(yearBegin >= 1969 & nyear >= 8) %>%
ggplot(., aes(x = nyear, y = nfirst)) +
geom_point(position = position_jitter(width = 0.2)) +
labs(x = "Number of years",
y = "Number of divisional titles") +
geom_smooth()
# Change response to proportion of titles relative
# to years managed
mgrTotals %>%
filter(yearBegin >= 1969 & nyear >= 8) %>%
ggplot(., aes(x = nyear, y = round(nfirst/nyear, 3))) +
geom_point(position = position_jitter(width = 0.2)) +
labs(x = "Number of years",
y = "Proportion of divisional titles") +
geom_smooth()
# }
```

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

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