Teams: Teams table

In Lahman: Sean 'Lahman' Baseball Database

Teams table

Description

Yearly statistics and standings for teams

Usage

data(Teams)

Format

A data frame with 3045 observations on the following 48 variables.

yearID

Year

lgID

League; a factor with levels AA AL FL NL PL UA

teamID

Team; a factor

franchID

Franchise (links to TeamsFranchises table)

divID

Team's division; a factor with levels C E W

Rank

Position in final standings

G

Games played

Ghome

Games played at home

W

Wins

L

Losses

DivWin

Division Winner (Y or N)

WCWin

Wild Card Winner (Y or N)

LgWin

League Champion(Y or N)

WSWin

World Series Winner (Y or N)

R

Runs scored

AB

At bats

H

Hits by batters

X2B

Doubles

X3B

Triples

HR

Homeruns by batters

BB

Walks by batters

SO

Strikeouts by batters

SB

Stolen bases

CS

Caught stealing

HBP

Batters hit by pitch

SF

Sacrifice flies

RA

Opponents runs scored

ER

Earned runs allowed

ERA

Earned run average

CG

Complete games

SHO

Shutouts

SV

Saves

IPouts

Outs Pitched (innings pitched x 3)

HA

Hits allowed

HRA

Homeruns allowed

BBA

Walks allowed

SOA

Strikeouts by pitchers

E

Errors

DP

Double Plays

FP

Fielding percentage

name

Team's full name

park

Name of team's home ballpark

attendance

Home attendance total

BPF

Three-year park factor for batters

PPF

Three-year park factor for pitchers

teamIDBR

Team ID used by Baseball Reference website

teamIDlahman45

Team ID used in Lahman database version 4.5

teamIDretro

Team ID used by Retrosheet

Details

Variables X2B and X3B are named 2B and 3B in the original database

Source

Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/

Examples

data(Teams)
library("dplyr")
library("tidyr")

# Add some selected measures to the Teams data frame
# Restrict to AL and NL in modern era
teams <- Teams %>% 
  filter(yearID >= 1901 & lgID %in% c("AL", "NL")) %>%
  group_by(yearID, teamID) %>%
  mutate(TB = H + X2B + 2 * X3B + 3 * HR,
         WinPct = W/G,
         rpg = R/G,
         hrpg = HR/G,
         tbpg = TB/G,
         kpg = SO/G,
         k2bb = SO/BB,
         whip = 3 * (H + BB)/IPouts)

# Function to create a ggplot by year for selected team stats
# Both arguments are character strings
yrPlot <- function(yvar, label)
{
    require("ggplot2")
    ggplot(teams, aes_string(x = "yearID", y = yvar)) +
       geom_point(size = 0.5) +
       geom_smooth(method="loess") +
       labs(x = "Year", y = paste(label, "per game"))
}

## Run scoring in the modern era by year
yrPlot("rpg", "Runs")

## Home runs per game by year
yrPlot("hrpg", "Home runs")

## Total bases per game by year
yrPlot("tbpg", "Total bases")

## Strikeouts per game by year
yrPlot("kpg", "Strikeouts")

## Plot win percentage vs. run differential (R - RA)
ggplot(teams, aes(x = R - RA, y = WinPct)) +
   geom_point(size = 0.5) +
   geom_smooth(method="loess") + 
   geom_hline(yintercept = 0.5, color = "orange") +
   geom_vline(xintercept = 0, color = "orange") +
   labs(x = "Run differential", y = "Win percentage")

## Plot attendance vs. win percentage by league, post-1980
teams %>%  filter(yearID >= 1980) %>%
ggplot(., aes(x = WinPct, y = attendance/1000)) +
   geom_point(size = 0.5) +
   geom_smooth(method="loess", se = FALSE) +
   facet_wrap(~ lgID) +
   labs(x = "Win percentage", y = "Attendance (1000s)")

## Teams with over 4 million attendance in a season
teams %>% 
  filter(attendance >= 4e6) %>%
  select(yearID, lgID, teamID, Rank, attendance) %>%
  arrange(desc(attendance))

## Average season HRs by park, post-1980
teams %>% 
   filter(yearID >= 1980) %>%
   group_by(park) %>%
     summarise(meanHRpg = mean((HR + HRA)/Ghome), nyears = n()) %>%
     filter(nyears >= 10) %>%
     arrange(desc(meanHRpg)) %>%
     head(., 10)

## Home runs per game at Fenway Park and Wrigley Field,
## the two oldest MLB parks, by year. Fenway opened in 1912.
teams %>% 
  filter(yearID >= 1912 & teamID %in% c("BOS", "CHN")) %>%
  mutate(hrpg = (HR + HRA)/Ghome) %>%
  ggplot(., aes(x = yearID, y = hrpg, color = teamID)) +
    geom_line(size = 1) +
    geom_point() +
    labs(x = "Year", y = "Home runs per game", color = "Team") +
    scale_color_manual(values = c("red", "blue"))

## Ditto for total strikeouts per game
teams %>% 
  filter(yearID >= 1912 & teamID %in% c("BOS", "CHN")) %>%
  mutate(kpg = (SO + SOA)/Ghome) %>%
  ggplot(., aes(x = yearID, y = kpg, color = teamID)) +
  geom_line(size = 1) +
  geom_point() +
  labs(x = "Year", y = "Strikeouts per game", color = "Team") +
  scale_color_manual(values = c("red", "blue"))  


## Not run: 
if(require(googleVis)) {
motion1 <- gvisMotionChart(as.data.frame(teams), 
             idvar="teamID", timevar="yearID", chartid="gvisTeams",
	           options=list(width=700, height=600))
plot(motion1)
#print(motion1, file="gvisTeams.html")

# Merge with avg salary for years where salary is available

teamsal <- Salaries %>%
                group_by(yearID, teamID) %>%
                summarise(Salary = sum(salary, na.rm = TRUE)) %>%
                select(yearID, teamID, Salary)

teamsSal <- teams %>%
                filter(yearID >= 1985) %>%
                left_join(teamsal, by = c("yearID", "teamID")) %>%
                select(yearID, teamID, attendance, Salary, WinPct) %>%
                as.data.frame(.)

motion2 <- gvisMotionChart(teamsSal, idvar="teamID", timevar="yearID",
  xvar="attendance", yvar="salary", sizevar="WinPct",
	chartid="gvisTeamsSal", options=list(width=700, height=600))
plot(motion2)
#print(motion2, file="gvisTeamsSal.html")

}

## End(Not run)

Lahman documentation built on Sept. 27, 2024, 1:06 a.m.