Introduction to Football Analytics in R

Course Description

This course provides a comprehensive introduction to football 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 NFL data (including play-by-play information); and develop both linear and logistic models to evaluate teams, inform game strategy, and forecast performance. From visualizing team and player trends, to applying regression concepts for wins, point differentials, or field-goal probabilities, you’ll gain the practical skills to conduct data-driven analysis in a football 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 pass and run tendencies in late-game situations, building a linear model to predict team performance, and applying logistic regression to estimate field goal success—demonstrating your mastery of R-based football analytics. No prior programming or football analytics experience is required to enroll.

What You’ll Learn

• Foundations of Football Analytics

  • Set up R, RStudio, and Tidyverse
    Learn the essentials of installing and configuring R, as well as creating and managing projects in RStudio.
  • Understand how data analytics and modeling shape modern NFL decision-making
    Gain insight into how teams use data to optimize rosters, strategy, and in-game decisions.

• Introduction to Key Metrics

  • Explore essential stats (e.g., passing yards, rushing yards, EPA)
    Become familiar with foundational football metrics that inform player and team performance.
  • See how advanced insights (like play-by-play tendencies) factor into outcomes
    Learn why situational stats—such as clutch pass/run ratios—can reveal hidden strategic edges.

• Data Collection & Analysis Tools

  • Leverage Pro Football Reference, nflfastR, and more
    Retrieve season-level and play-by-play data to power your analyses.
  • Practice data wrangling and compelling visualizations
    Use dplyr, ggplot2, and nflplotR to clean, summarize, and plot football data for impactful insights.

• Team & Player Evaluation Techniques

  • Use linear regression to explore team value and performance
    Model win totals, point differentials, and other key outcomes using statistical techniques.
  • Interpret model outputs to identify over/undervalued teams
    See how regression coefficients can inform roster moves and strategic decisions.

• Game Strategy & Classification

  • Employ logistic regression for binary outcomes (e.g., game wins, field goal success)
    Train models to estimate the probability of a successful outcome and interpret model accuracy.
  • Implement train-test splits and diagnostics
    Evaluate model performance using confusion matrices, ROC curves, and AUC metrics to avoid overfitting.

• Forecasting Future Performance

  • Combine regression methods and advanced metrics to project next-season results
    Use historical data to forecast how teams might perform in upcoming games or seasons.
  • Investigate probabilities for point differentials, playoff qualification, or kicker performance
    Apply predictive models to identify trends and outliers in team and player data.

• Portfolio Projects

  • Pass & Run Tendencies in the Clutch
    Analyze fourth-quarter play-calling and EPA to evaluate late-game decision-making.
  • Predicting Point Differentials for 2024 Playoff Teams
    Build a linear regression model and visualize under/over-performance to inform strategic insights.
  • Predicting Field Goal Success with Logistic Regression
    Estimate the probability of made field goals and identify which kickers exceed expectations.

By the end of this course, you will have a strong foundation in R-based football analytics, from data ingestion and cleaning, to sophisticated regression modeling and strategic interpretation—positioning you to drive data-driven decisions in a professional football context.