Introduction to Basketball Analytics in R

Course Description

This course provides a comprehensive introduction to basketball analytics in R, organized around key pillars of modern sports data science. You will learn to install and configure R, RStudio, and the Tidyverse; ingest and wrangle detailed NBA data (including shot-level information); and develop both linear and logistic models to evaluate players, inform game strategy, and forecast performance. From visualizing team and player trends, to applying regression concepts for salary vs. production or shot-make probabilities, you’ll gain the practical skills to conduct data-driven analysis in a basketball front-office context.

Each lesson concludes with a brief quiz to reinforce important concepts. You will also complete three real-world projects—such as analyzing over/under-performing players via logistic regression on shot data, building a linear model to predict total team wins, and applying machine learning principles to predict playoff probabilities—demonstrating your mastery of R-based basketball analytics. No prior programming or basketball analytics experience is required to enroll.

What You’ll Learn?

• Foundations of Basketball Analytics

  • Set up R, RStudio, and Tidyverse
  • Understand how data analytics and modeling shape modern NBA decision-making

• Introduction to Key Metrics

  • Learn essential stats (e.g., usage rates, shooting splits) as baseline references
  • Examine how advanced insights (like shot distance, shot type) factor into performance

• Data Collection & Analysis Tools

  • Explore HoopR and nbaStatR for retrieving season-level and shot-level data
  • Practice wrangling raw files and APIs, then create compelling visualizations

• Player Evaluation Techniques

  • Use linear regression to explore player value (e.g., points, minutes, salary)
  • Interpret model outputs to identify over/undervalued players and inform roster decisions

• Game Strategy & Classification

  • Employ logistic regression for binary outcomes (e.g., made/missed shots, playoffs)
  • Implement train-test splits to avoid overfitting and better assess model accuracy

• Forecasting Future Performance

  • Combine regression methods and advanced metrics to project next-season breakouts
  • Investigate probabilities for team wins, playoff qualification, or individual shot success

• Portfolio Projects

  • Complete three hands-on projects demonstrating real-world basketball analytics scenarios