Author: Michael Friendly
Date: 2026-02-11

# Team Payroll and the World Series

#### Michael Friendly

#### 2026-02-11

This vignette examines whether there is a relationship between total team salaries (payroll) and World Series success. It was inspired by Nolan & Lang (2015), “Baseball: Exploring Data in a Relational Database”, Chapter 10 in Data Science in R. They use `SQL` on the raw Lahman files `.csv`, rather than the Lahman package.

Here, We largely use `dplyr` for data munging and `ggplot2` for plotting. In the process, we discover a few errors in the data sets.

## The data files

Start with loading the files we will use here. We do some pre-processing to make them more convenient for the analyses done later.

### The `Salaries` data

The `Salaries` data.frame contains data on all players’ salaries from 1985-2016 in the latest release, v. 14.0.0, of the `Lahman` package. (Additions to the `Salaries` table in San Lahman’s database were discontinued after 2016.) We use the `sample_n` function to display a random sample of observations.

    data("Salaries", package="Lahman")
    str(Salaries)
    ## 'data.frame':    26428 obs. of  5 variables:
    ##  $ yearID  : int  2004 2007 2008 2009 2010 2011 2012 1986 1987 1988 ...
    ##  $ teamID  : Factor w/ 35 levels "ANA","ARI","ATL",..: 30 7 5 29 29 29 23 4 4 4 ...
    ##  $ lgID    : Factor w/ 2 levels "AL","NL": 2 1 1 1 1 1 1 1 1 1 ...
    ##  $ playerID: chr  "aardsda01" "aardsda01" "aardsda01" "aardsda01" ...
    ##  $ salary  : int  300000 387500 403250 419000 2750000 4500000 500000 600000 625000 675000 ...
    sample_n(Salaries, 10)
    ##    yearID teamID lgID  playerID  salary
    ## 1    2004    MIL   NL  fordbe01  310000
    ## 2    2013    SFN   NL garcija01 4166667
    ## 3    2003    ATL   NL  fickro01 1000000
    ## 4    1997    SFN   NL gardnma01  900000
    ## 5    1993    NYN   NL thompry01  109000
    ## 6    1993    KCA   AL gubicma01 1250000
    ## 7    1999    KCA   AL appieke01 4800000
    ## 8    2009    LAN   NL mcdonja03  400750
    ## 9    1999    HOU   NL  holtch01  279000
    ## 10   2009    SDN   NL  baekch01  429900

### The `Teams` data

The `Teams` data.frame contains a lot of information about all teams that have ever played, with a separate observation for each year. Here, we will mainly use this to get the team name (`team`) from `teamID` and also to get the information about World Series winners.

    data("Teams", package="Lahman")
    dim(Teams)
    ## [1] 3614   48
    names(Teams)
    ##  [1] "yearID"         "lgID"           "teamID"         "franchID"
    ##  [5] "divID"          "Rank"           "G"              "Ghome"
    ##  [9] "W"              "L"              "DivWin"         "WCWin"
    ## [13] "LgWin"          "WSWin"          "R"              "AB"
    ## [17] "H"              "X2B"            "X3B"            "HR"
    ## [21] "BB"             "SO"             "SB"             "CS"
    ## [25] "HBP"            "SF"             "RA"             "ER"
    ## [29] "ERA"            "CG"             "SHO"            "SV"
    ## [33] "IPouts"         "HA"             "HRA"            "BBA"
    ## [37] "SOA"            "E"              "DP"             "FP"
    ## [41] "name"           "park"           "attendance"     "BPF"
    ## [45] "PPF"            "teamIDBR"       "teamIDlahman45" "teamIDretro"

We are only going to use the observations from 1985 on, and a few variables, so we filter and select them now. Keep only the levels of `teamID` in the data.

    Teams <- Teams %>%
      select(yearID, lgID, teamID, name, divID, Rank, WSWin, attendance) %>%
      filter(yearID >= 1985) %>%
      mutate(teamID = droplevels(teamID))

    sample_n(Teams, 10)
    ##    yearID lgID teamID                  name divID Rank WSWin attendance
    ## 1    1999   NL    PHI Philadelphia Phillies     E    3     N    1825337
    ## 2    2016   AL    KCA    Kansas City Royals     C    3     N    2557712
    ## 3    1988   NL    PIT    Pittsburgh Pirates     E    2     N    1866713
    ## 4    2023   AL    TOR     Toronto Blue Jays     E    3     N    3021904
    ## 5    2010   AL    NYA      New York Yankees     E    2     N    3765807
    ## 6    1997   NL    CHN          Chicago Cubs     C    5     N    2190308
    ## 7    2023   AL    CHA     Chicago White Sox     C    4     N    1669628
    ## 8    2015   NL    SLN   St. Louis Cardinals     C    1     N    3520889
    ## 9    1995   NL    CHN          Chicago Cubs     C    3     N    1918265
    ## 10   1992   AL    BAL     Baltimore Orioles     E    3     N    3567819

### The `SeriesPost` data

Post season records go back to 1884. There are 440 observations covering all aspects of post-season play.

    data("SeriesPost", package="Lahman")
    names(SeriesPost)
    ## [1] "yearID"       "round"        "teamIDwinner" "lgIDwinner"   "teamIDloser"
    ## [6] "lgIDloser"    "wins"         "losses"       "ties"

For each year, there are number of observations for the various levels of post-season play (Division titles, League titles, etc. A number of these designations have changed over the years, and I don’t know what they all mean.)

    table(SeriesPost$round)
    ##
    ## AEDIV   ALC  ALCS ALDS1 ALDS2  ALWC ALWC1 ALWC2 ALWC3 ALWC4 AWDIV    CS NEDIV
    ##     1     5    56    31    31     9     5     5     1     1     1     1     1
    ##   NLC  NLCS NLDS1 NLDS2  NLP1  NLP2  NLWC NLWC1 NLWC2 NLWC3 NLWC4   NNC   NSC
    ##     5    56    31    31     1     1     9     5     5     1     1     5     1
    ## NWDIV   NWS    WS
    ##     1    11   128

We are interested only in the World Series (`WS`), which was first played in 1903. We filter for the years for which we have salary data, and drop a couple of variables. The league IDs of the winner and loser are factors, so we use `droplevels` to include only the levels in recent history.

    WS <- SeriesPost %>%
      filter(yearID >= 1985 & round == "WS") %>%
      select(-ties, -round) %>%
      mutate(lgIDloser = droplevels(lgIDloser),
             lgIDwinner = droplevels(lgIDwinner))
    dim(WS)
    ## [1] 40  7
    sample_n(WS, 6)
    ##   yearID teamIDwinner lgIDwinner teamIDloser lgIDloser wins losses
    ## 1   1988          LAN         NL         OAK        AL    4      1
    ## 2   2025          LAN         NL         TOR        AL    4      3
    ## 3   2006          SLN         NL         DET        AL    4      1
    ## 4   2005          CHA         AL         HOU        NL    4      0
    ## 5   1987          MIN         AL         SLN        NL    4      3
    ## 6   2016          CHN         NL         CLE        AL    4      3

## A first look at `Salaries`

How many players do we have in each year?

    table(Salaries$yearID)
    ##
    ## 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
    ##  550  738  627  663  711  867  685  769  923  884  986  931  925  998 1006  836
    ## 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
    ##  860  846  827  831  831  819  842  856  813  830  839  848  815  802  817  853

What is the range of salaries, across all years?

    range(Salaries$salary)
    ## [1]    10900 33000000

And, year by year?

    Salaries %>%
      group_by(yearID) %>%
      summarise(min=min(salary),
                max=max(salary))
    ## # A tibble: 32 × 3
    ##    yearID    min     max
    ##     <int>  <int>   <int>
    ##  1   1985  60000 2130300
    ##  2   1986  60000 2800000
    ##  3   1987  62500 2127333
    ##  4   1988  62500 2340000
    ##  5   1989  62500 2766667
    ##  6   1990 100000 3200000
    ##  7   1991 100000 3800000
    ##  8   1992 109000 6100000
    ##  9   1993  10900 6200000
    ## 10   1994  50000 6300000
    ## # ℹ 22 more rows

Hmm, there is a `salary==0` in 1993, maybe there are others.

    which(Salaries$salary==0)
    ## integer(0)

Who are they? (We could also look up their `playerID`s in `Lahman::People`.)

    Salaries[which(Salaries$salary==0),]
    ## [1] yearID   teamID   lgID     playerID salary
    ## <0 rows> (or 0-length row.names)

These must be errors. Get rid of them. Reminder: Check further; maybe file an issue in the Lahman package!

    Salaries <- Salaries %>%
        filter(salary !=0)

### Get team payrolls

We want to sum the `salary` for each team for each year. We might as well make it in millions. All those zeros hurt my eyes.

    payroll <- Salaries %>%
                    group_by(teamID, yearID) %>%
                    summarise(payroll = sum(salary)/1000000)
    head(payroll)
    ## # A tibble: 6 × 3
    ## # Groups:   teamID [1]
    ##   teamID yearID payroll
    ##   <fct>   <int>   <dbl>
    ## 1 ANA      1997    31.1
    ## 2 ANA      1998    41.3
    ## 3 ANA      1999    55.4
    ## 4 ANA      2000    51.5
    ## 5 ANA      2001    47.5
    ## 6 ANA      2002    61.7

### Merge team names into `payroll`

It will be more convenient to have the team names included in the `payroll` data.frame. The `Teams` data frame also contains the `Y/N` indicator `WSWin` for World Series winners, so we might as well include this too.

    payroll <- merge(payroll, Teams[,c("yearID", "teamID","name", "WSWin")],
                     by=c("yearID", "teamID"))
    sample_n(payroll, 10)
    ##    yearID teamID payroll                          name WSWin
    ## 1    2006    MIN   63.40               Minnesota Twins     N
    ## 2    2007    MIL   70.99             Milwaukee Brewers     N
    ## 3    2016    LAA  137.25 Los Angeles Angels of Anaheim     N
    ## 4    2005    OAK   55.43             Oakland Athletics     N
    ## 5    2003    FLO   49.45               Florida Marlins     Y
    ## 6    2009    COL   75.20              Colorado Rockies     N
    ## 7    2000    MIL   36.51             Milwaukee Brewers     N
    ## 8    1999    ANA   55.39                Anaheim Angels     N
    ## 9    2013    NYA  231.98              New York Yankees     N
    ## 10   1989    CAL   15.10             California Angels     N

Note that we could also do this using `left_join` in the `dplyr` package. There is probably a more `tidy` way to subset the variables from the `Teams` data set than using `Teams[, c()]`, but, hey– this works.

    left_join(payroll, Teams[,c("yearID", "teamID","name", "WSWin")],
              by=c("yearID", "teamID")) %>%
      sample_n(10)

`WSWin` is a character variable. Convert it to a factor.

    payroll <- payroll %>%
      mutate(WSWin = factor(WSWin))

Check the values:

    table(payroll$WSWin, useNA="ifany")
    ##
    ##    N    Y <NA>
    ##  859   31   28

There is something wrong here! There shouldn’t be any `NA`s. We leave this for further study, and another Reminder to file an issue if we figure out what the problem is.

## Boxplots of payroll

Let’s look at the distributions of payroll by year. The observations are teams.

    boxplot(payroll ~ yearID, data=payroll, ylab="Payroll ($ millions)")

What are the outliers? Are there any teams that crop up repeatedly? `car::Boxplot` makes this easy, and also returns the labels of the outliers. We don’t load the `car` package, because `car` also contains a `Salary` dataset.

    out <- car::Boxplot(payroll ~ yearID, data=payroll,
                 id=list(n=1,
                         labels=as.character(payroll$teamID)),
                 ylab="Payroll ($ millions)")

Most of the outliers are the New York Yankees (`NYA`):

    table(out)
    ## out
    ## CLE LAN MON NYA PIT SEA
    ##   1   2   1  11   1   1

Payroll has obviously increased dramatically over time. So has the variability across teams. For any modelling, we would probably want to use `\log(payroll)`. We might also want to look separately at the American and National leagues.

### Correcting for inflation

For proper comparisons, we should correct for inflation. Lets do this by scaling salary back to 1985 dollars, The data below gives inflation rates for all subsequent years. It comes from Nolan & Lang, extended to 2015 using (https://www.in2013dollars.com/).

    inflation = c(1,    1.02, 1.06, 1.10, 1.15, 1.21,
                  1.27, 1.30, 1.34, 1.38, 1.42, 1.46, 1.49, 1.51, 1.55, 1.60,
                  1.65, 1.67, 1.71, 1.76, 1.82, 1.87, 1.93, 2.00, 1.99, 2.03,
                  2.09, 2.13, 2.16, 2.20, 2.20 )

    inflation.df <- data.frame(year=1985:2015, inflation)

    # plot inflation rate
    ggplot(inflation.df, aes(y=inflation, x=year)) +
      geom_point() +
      geom_line() +
      geom_smooth(method="lm")

This is close enough to linear, that we could use the linear regression predicted value as a simple computation of the inflation rate. (A better way, of course, would be to use the actual inflation rate; this would entail merging `payroll` with `inflation.df` by year, and doing the computation.)

    infl.lm <- lm(inflation ~ year, data=inflation.df)
    (coefs <- coef(infl.lm))
    ## (Intercept)        year
    ##   -81.57032     0.04159

Scale `payroll` by dividing by linear prediction of inflation rate, producing `payrollStd`.

    payroll <- payroll %>%
      mutate(payrollStd = payroll / (coefs[1] + coefs[2] * yearID))

Boxplot again, of inflation-adjusted payroll. The increase after 2000 doesn’t seem so large.

    car::Boxplot(payrollStd ~ yearID, data=payroll,
                 id = list(labels=as.character(payroll$teamID)),
                 ylab="Payroll (1985-adjusted $ millions)")

    ##  [1] "SEA" "CLE" "MON" "PIT" "NYA" "NYA" "NYA" "NYA" "NYA" "NYA" "NYA" "NYA"
    ## [13] "NYA" "NYA" "LAN" "NYA" "LAN" "NYA" "BOS" "DET" "LAN" "NYA"

## Salaries of World Series winning teams

To what extent are the World Series winners those among the highest in payroll? A simple way to look at this is to plot the team payrolls across years, and mark the World Series winner for each year.

This plot shows inflation-adjusted payroll on a log scale to avoid the dominating influence of the most recent years. We jitter the points to avoid overplotting, and use a transparent gray color for the non-winners, red for the winner in each year.

    Cols <- ifelse(payroll$WSWin=='Y', "red", gray(.7, alpha=0.5))
    with(payroll, {
      plot(payrollStd ~ jitter(yearID, 0.5),
           ylab = "Payroll (inflation-adjusted $ millions)",
           ylim = c(5,125), log = "y",
           xlab = "Year",
           pch = 19, cex = 0.8, col = Cols)
    })
    with(payroll[payroll$WSWin == 'Y',],
         text(y = payrollStd, x = yearID, labels = teamID, pos = 3, cex = 0.8) )

By and large, the World Series winners tend to be in the upper portion of the payrolls for each year.

## Further analyses: Your turn

Here are some questions to provoke further analyses of these data sets. If you find something interesting, post it in a Github Gist or forward it to Team Lahman as in a Lahman issue.

- Our boxplots used total payroll in millions. Would the plots be clearer if we plotted payroll on a log scale?

- Follow-up the suggestion to fit a linear model predicting `log(payroll)` from some of the available predictors.

- Examine the trend of team salaries over time for World Series losers. Does it look the same or different than that for the winning teams?

- What kind of analysis would you do to address this statement: “For teams that make it to the World series, the one with the larger payroll is more likely to win.”

- Is there any relationship between the number of team wins in a season and winning the World series?