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Baseball is not the only sport to use "moneyball." American football fans, teams, and gamblers are increasingly using data to gain an edge against the competition. Professional and college teams use data to help select players and identify team needs. Fans use data to guide fantasy team picks and strategies. Sports bettors and fantasy football players are using data to help inform decision making. This concise book provides a clear introduction to using statistical models to analyze football data.

Whether your goal is to produce a winning team, dominate your fantasy football league, qualify for an entry-level football analyst position, or simply learn R and Python using fun example cases, this book is your starting place. You'll learn how to:

  • Apply basic statistical concepts to football datasets
  • Describe football data with quantitative methods
  • Create efficient workflows that offer reproducible results
  • Use data science skills such as web scraping, manipulating data, and plotting data
  • Implement statistical models for football data
  • Link data summaries and model outputs to create reports or presentations using tools such as R Markdown and R Shiny
  • And more

Table of Contents

  1. Preface
  2. 1. Football Analytics
  3. 2. Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
  4. 3. Simple Linear Regression: Rushing Yards Over Expected
  5. 4. Multiple Regression: Rushing Yards Over Expected
  6. 5. Generalized Linear Models: Completion Percentage over Expected
  7. 6. Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
  8. 7. Web Scraping: Obtaining and Analyzing Draft Picks
  9. 8. Principal Component Analysis and Clustering: Player Attributes
  10. 9. Advanced Tools and Next Steps
  11. A. Python and R Basics
  12. B. Summary Statistics and Data Wrangling: Passing the Ball
  13. C. Data-Wrangling Fundamentals
  14. Glossary
  15. Index
  16. About the Authors
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