# Run Scoring Trends

#### Martin Monkman

#### 2025-09-06

## Run scoring trends: Sub-titles and captions with ggplot2

This vignette demonstrates how to create a plot (using `ggplot2`) showing Major League Baseball runscoring trends since the 1901 season.

First, we load the necessary packages: `Lahman` (containing the baseball data), `ggplot2` to create the plots, and the data carpentry package `dplyr` (note that `ggplot2` and `dplyr` are included in the `tidyverse` package):

    # package load
    library(Lahman)
    library(ggplot2)
    library(dplyr)

#### Read and summarize the data

For this example, we’ll use the data table `Teams` in the Lahman database.

Once it’s loaded, the data are filtered and summarized using dplyr.

- filters from 1901 (the establishment of the American League) to the most recent year,

- filters out (using `!=`) the Federal League

- after grouping by the year, summarizes the total number of runs scored, runs allowed, and games played

- finally, calculates the league runs (`leagueRPG`) and runs allowed (`leagueRAPG`) per game

    data(Teams)

    MLB_RPG <- Teams %>%
      filter(yearID > 1900, lgID != "FL") %>%
      group_by(yearID) %>%
      summarise(R=sum(R), RA=sum(RA), G=sum(G)) %>%
      mutate(leagueRPG=R/G, leagueRAPG=RA/G)

#### A basic plot

You may have heard that run scoring in Major League Baseball has been down in recent years–or is it going back up? This is a perfect opportunity to visualize the date; what can we see in a plot?

For the first version of the plot, we’ll make a basic X-Y plot, where the X axis has the years and the Y axis has the average number of runs scored. With `ggplot2`, it’s easy to add a trend line (using the `geom_smooth` option).

The `scale_x_continuous` options set the limits and breaks of the axes.

    MLBRPGplot <- ggplot(MLB_RPG, aes(x=yearID, y=leagueRPG)) +
      geom_point() +
      geom_smooth(span = 0.25) +
      scale_x_continuous(breaks = seq(1900, 2015, by = 20)) +
      scale_y_continuous(limits = c(3, 6), breaks = seq(3, 6, by = 1))

    MLBRPGplot

The way we would set the title, along with X and Y axis labels, would be something like this.

    MLBRPGplot +
      ggtitle("MLB run scoring, 1901-2016") +
      theme(plot.title = element_text(hjust=0, size=16)) +
      xlab("year") +
      ylab("team runs per game")

### Adding a subtitle: the function

So now we have a nice looking dot plot showing the average number of runs scored per game for the years 1901-2016. (The 2016 data is the most recent that has been added to the database.)

But a popular feature of charts–particularly in magazines–is a subtitle that has a summary of what the chart shows and/or what the author wants to emphasize.

In this case, we could legitimately say something like any of the following:

- The peak of run scoring in the 2000 season has been followed by a steady drop

- Teams scored 20% fewer runs in 2016 than in 2000

- Team run scoring has fallen to just over 4 runs per game from the 2000 peak of 5 runs

- Run scoring has been falling for 15 years, reversing a 30 year upward trend

I like this last one, drawing attention not only to the recent decline but also the longer trend that started with the low-scoring environment of 1968.

How can we add a subtitle to our chart that does that, as well as a caption that acknowledges the source of the data? The new `labs` function, available starting with `ggplot2` version 2.2.0, lets us do that.

Note that `labs` contains the title, subtitle, caption, as well as the X and Y axis labels.

    MLBRPGplot +
      labs(title = "MLB run scoring, 1901-2016",
           subtitle = "Run scoring in 2016 was the highest in seven years",
           caption = "Source: the Lahman baseball database",
           x = "year", y = "team runs per game")

Easy.

For more information about the `labs` function in `ggplot2`, the “Modify axis, legend, and plot labels” reference page within the `ggplot2` site, part of the Tidyverse.

This vignette is an update of the blog posts:

- Major League Baseball run scoring trends with R’s Lahman package

- Run scoring trends: using Shiny to create dynamic charts and tables in R

- github.com/MonkmanMH/MLBrunscoring_shiny