Basketball has become one of the most data-driven sports in the world. From the NBA to NCAA programs, teams now rely heavily on advanced metrics to make smarter decisions on the court. With the rise of open data sources, it’s easier than ever for analysts, students, and even fans to explore the game using modern tools.
One of the most exciting resources for R users is the hoopR package. This package provides direct access to NBA and NCAA data, including play-by-play logs, box scores, team stats, and even shot locations. Instead of scraping or manually collecting information, hoopR allows you to work with clean, ready-to-use data straight from ESPN and official APIs.
Why hoopR Matters
- Comprehensive coverage: NBA and NCAA play-by-play and box score data in a tidy format.
- Integration with tidyverse: Perfectly suited for data wrangling with packages like
dplyrandtidyr. - Flexibility: Explore player performance, team efficiency, lineups, and even win probability.
- Open source: Built by the sports analytics community and constantly updated.
With hoopR, you can answer questions like:
- Which players are most efficient in clutch situations?
- How do different lineups affect a team’s scoring margin?
- What patterns emerge from shot location data across an entire season?
All of this can be done directly in R with reproducible code.
Getting Started
# install.packages("devtools")
devtools::install_github("sportsdataverse/hoopR")
library(hoopR)
library(dplyr)
# Load play-by-play data for the 2023 NBA season
pbp_data <- load_nba_pbp(seasons = 2023)
# Quick check
pbp_data %>%
select(game_id, period, clock_display_value, type_text, home_score, away_score) %>%
head()
From here, you can extend the analysis to calculate efficiency metrics, identify top scorers, or even model win probability over time.
Why R is the Perfect Fit
R is not only powerful for statistical analysis, but it also excels in reproducibility. By combining hoopR with packages like ggplot2 and BasketballAnalyzeR, analysts can move seamlessly from raw data to advanced models and publish-ready reports.
Whether you are a coach looking for tactical insights, a student working on a sports analytics project, or a hobbyist exploring your favorite team’s performance, R provides the tools you need.
Recommended Resource
If you’re serious about learning how to use hoopR and R for basketball analytics, I recommend checking out this book:
👉 Basketball Analytics with R: From hoopR Data to Winning Insights
This professional guide walks you through reproducible code, player and team analysis, efficiency metrics, and much more. It’s designed for analysts, students, coaches, and data enthusiasts who want to take their basketball analytics to the next level.