Why R for Sports Analytics?

R is a powerful, open-source language widely used by analysts and researchers. Communities like R-Bloggers regularly publish sports-focused tutorials, and resources such as R Programming Books highlight how R turns raw data into actionable insights. With rich packages for wrangling and visualization, R is an excellent first choice for aspiring sports analysts.

Setting Up R and RStudio

1. Install R from CRAN.
2. Install RStudio Desktop from Posit (formerly RStudio).
3. Open RStudio and create a new R Script (File → New File → R Script).

First Steps with tidyverse

Install and load the core packages:

# Install once
install.packages("tidyverse")

# Load on each session
library(tidyverse)

Sample Sports Datasets

Let’s begin with a tiny, made-up game log (think basketball or football scores). We’ll compute team-level summaries and visualize points scored.

library(tidyverse)

games <- tibble(
  game_id = 1:8,
  date    = as.Date(c("2025-01-08","2025-01-12","2025-01-19","2025-01-26",
                      "2025-02-02","2025-02-09","2025-02-16","2025-02-23")),
  team    = c("Lions","Lions","Wolves","Wolves","Lions","Wolves","Lions","Wolves"),
  opponent= c("Wolves","Wolves","Lions","Lions","Wolves","Lions","Wolves","Lions"),
  team_pts= c(88, 92, 81, 77, 101, 95, 84, 99),
  opp_pts = c(85, 89, 79, 82, 96, 97, 90, 93)
)

glimpse(games)

Load, Clean, and Explore

1) Loading CSV data

# If your data is a CSV file
# games <- read_csv("data/games.csv")

2) Basic cleaning with dplyr

# Create a result column and a point differential
clean_games <- games %>%
  mutate(
    result = case_when(team_pts > opp_pts ~ "W",
                       team_pts < opp_pts ~ "L",
                       TRUE ~ "T"),
    diff = team_pts - opp_pts
  )

clean_games %>% count(team, result)

3) Team summaries

team_summary <- clean_games %>%
  group_by(team) %>%
  summarise(
    gp = n(),
    wins = sum(result == "W"),
    losses = sum(result == "L"),
    avg_pts = mean(team_pts),
    avg_allowed = mean(opp_pts),
    avg_diff = mean(diff)
  ) %>% arrange(desc(avg_diff))

team_summary

Quick Visualizations with ggplot2

Average Points by Team

team_summary %>%
  ggplot(aes(x = reorder(team, avg_pts), y = avg_pts)) +
  geom_col() +
  coord_flip() +
  labs(title = "Average Points by Team",
       x = "Team", y = "Avg Points")

Point Differential Over Time

clean_games %>%
  ggplot(aes(x = date, y = diff, color = team)) +
  geom_line() +
  geom_point() +
  labs(title = "Point Differential Over Time",
       x = "Date", y = "Point Differential")

Beginner FAQ

How can I do sports analytics?
Start small. Learn the basics of R syntax, then practice with tiny datasets like the one above. Gradually incorporate real data (e.g., CSVs from official league sites) and repeat the same workflow: load → clean → summarize → visualize.

Why use R instead of spreadsheets?
R makes analyses reproducible, scalable, and automatable. You can version-control your code, share it, and re-run it with new data.

Do I need math or coding experience?
Basic familiarity helps, but many concepts are learned by doing. Focus on data wrangling and visualization first.

Next Steps & Resources

• Practice with tidy data principles in tidyr and transformations in dplyr.
• Build charts with ggplot2; try lines, bars, and scatterplots first.
• Explore communities like R-Bloggers for sports tutorials.
• Read foundational texts from R Programming Books to deepen understanding.

Mini Glossary

R is a powerful tool for sports analysis—stick with small, repeatable steps and your skills will compound quickly. 🏈🏀⚽