Teams {Lahman} | R Documentation

## Teams table

### Description

Yearly statistics and standings for teams

### Usage

    data(Teams)

### Format

A data frame with 2835 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. (2017) Lahman's Baseball Database, 1871-2016, 2016 version, http://www.seanlahman.com/baseball-archive/statistics/

### 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)

[Package Lahman version 6.0-0 Index]