Code to download Retrosheet game log data with the focus on studying game scores.

baseball_scores.R

# Main function to get Retrosheet game scores

get_scores <- function(season){
  require(dplyr)
  require(readr)
  load_gamelog <- function(season) {
    glheaders <- read_csv("https://raw.githubusercontent.com/beanumber/baseball_R/master/data/game_log_header.csv")
    remote <- paste0("http://www.retrosheet.org/gamelogs/gl", 
                     season, ".zip")
    local <- paste0("gl", season, ".zip")
    download.file(url = remote, destfile = local)
    unzip(local)
    local_txt <- gsub(".zip", ".txt", local) %>%
      toupper()
    gamelog <- read_csv(local_txt, 
                        col_names = names(glheaders),
                        na = character())
    file.remove(local)
    file.remove(local_txt)
    return(gamelog)
  }
  load_gamelog(season) %>% 
    select(Date, HomeTeam, VisitingTeam,
           HomeRunsScore, VisitorRunsScored) %>% 
    mutate(Season = season)
}

# get scores for the 2019 season

d <- get_scores(2019)

# get scores for the past 50 seasons

library(purrr)
df <- map_df(1970:2019, get_scores)

library(dplyr)
library(ggplot2)
library(ProbBayes)

# most runs scored in a game?

df %>% 
  mutate(Runs = HomeRunsScore +
           VisitorRunsScored) %>% 
  filter(Runs == max(Runs)) 

# greatest blowout?

df %>% 
  mutate(Margin_Victory = abs(HomeRunsScore -
           VisitorRunsScored)) %>% 
  filter(Margin_Victory == max(Margin_Victory)) 

# graph of mean total runs scored against season

p1 <- df %>% 
  group_by(Season) %>% 
  summarize(Runs = mean(HomeRunsScore +
              VisitorRunsScored), 
            .groups = "drop") %>% 
  ggplot(aes(Season, Runs)) +
  geom_point() + 
  geom_smooth(method = "loess",
              span = 0.3) +
  ggtitle("Total Runs Scored") + 
  centertitle() + increasefont()

# graph of mean win margin against season

p2 <- df %>% 
  group_by(Season) %>% 
  summarize(Win_Margin = mean(abs(HomeRunsScore -
                          VisitorRunsScored)), 
            .groups = "drop") %>% 
  ggplot(aes(Season, Win_Margin)) +
  geom_point() + 
  geom_smooth(method = "loess",
              span = 0.3) +
  ggtitle("Win Margin") + 
  centertitle() + increasefont()

# place both plots in the same plotting window

library(gridExtra)
grid.arrange(p1, p2)