Managers

0th

Percentile

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

Aliases
  • Managers
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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