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Football Analytics with Python & R: Learning Data Science Through the Lens of Sports
Baseball is not the only sport to use "moneyball." American football teams, fantasy football players, fans, and gamblers are increasingly using data to gain an edge on the competition. Professional and college teams use data to help identify team needs and select players to fill those needs. Fantasy football players and fans use data to try to defeat their friends, while sports bettors use data in an attempt to defeat the sportsbooks.

In this concise book, Eric Eager and Richard Erickson provide a clear introduction to using statistical models to analyze football data using both Python and R. Whether your goal is to qualify for an entry-level football analyst position, dominate your fantasy football league, or simply learn R and Python with fun example cases, this book is your starting place.

Through case studies in both Python and R, you'll learn to:

  • Obtain NFL data from Python and R packages and web scraping
  • Visualize and explore data
  • Apply regression models to play-by-play data
  • Extend regression models to classification problems in football
  • Apply data science to sports betting with individual player props
  • Understand player athletic attributes using multivariate statistics
1143408951
Football Analytics with Python & R: Learning Data Science Through the Lens of Sports
Baseball is not the only sport to use "moneyball." American football teams, fantasy football players, fans, and gamblers are increasingly using data to gain an edge on the competition. Professional and college teams use data to help identify team needs and select players to fill those needs. Fantasy football players and fans use data to try to defeat their friends, while sports bettors use data in an attempt to defeat the sportsbooks.

In this concise book, Eric Eager and Richard Erickson provide a clear introduction to using statistical models to analyze football data using both Python and R. Whether your goal is to qualify for an entry-level football analyst position, dominate your fantasy football league, or simply learn R and Python with fun example cases, this book is your starting place.

Through case studies in both Python and R, you'll learn to:

  • Obtain NFL data from Python and R packages and web scraping
  • Visualize and explore data
  • Apply regression models to play-by-play data
  • Extend regression models to classification problems in football
  • Apply data science to sports betting with individual player props
  • Understand player athletic attributes using multivariate statistics
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Football Analytics with Python & R: Learning Data Science Through the Lens of Sports

Football Analytics with Python & R: Learning Data Science Through the Lens of Sports

Football Analytics with Python & R: Learning Data Science Through the Lens of Sports
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Football Analytics with Python & R: Learning Data Science Through the Lens of Sports

Football Analytics with Python & R: Learning Data Science Through the Lens of Sports

Overview

Baseball is not the only sport to use "moneyball." American football teams, fantasy football players, fans, and gamblers are increasingly using data to gain an edge on the competition. Professional and college teams use data to help identify team needs and select players to fill those needs. Fantasy football players and fans use data to try to defeat their friends, while sports bettors use data in an attempt to defeat the sportsbooks.

In this concise book, Eric Eager and Richard Erickson provide a clear introduction to using statistical models to analyze football data using both Python and R. Whether your goal is to qualify for an entry-level football analyst position, dominate your fantasy football league, or simply learn R and Python with fun example cases, this book is your starting place.

Through case studies in both Python and R, you'll learn to:

  • Obtain NFL data from Python and R packages and web scraping
  • Visualize and explore data
  • Apply regression models to play-by-play data
  • Extend regression models to classification problems in football
  • Apply data science to sports betting with individual player props
  • Understand player athletic attributes using multivariate statistics

Product Details

ISBN-13: 9781492099628
Publisher: O'Reilly Media, Incorporated
Publication date: 09/19/2023
Pages: 349
Product dimensions: 7.00(w) x 9.19(h) x (d)

About the Author

Eric A Eager is the Head of Research, Development and Innovation at Pro Football Focus (PFF), where he uses his training as an applied mathematician to produce solutions to quantitative problems for 32 National Football League clients, over 105 NCAA Football clients and numerous media clients and contacts. He also co-hosts the PFF Forecast Podcast, which can be found on PodcastOne and iTunes and is the most popular football analytics podcast in the world since 2018. Additionally, Eager supplies odds used by Steve Kornacki on Football Night in America, the Today Show, and other programs since 2020.

He studied applied mathematics and mathematical biology at the University of Nebraska, where he wrote his PhD thesis on how stochasticity and nonlinear processes affect population dynamics. Eager spent his first six years thereafter as a professor at the University of Wisconsin - La Crosse, before transitioning to PFF full-time in 2018. He has since taught statistics and mathematics to over 10,000 students through college-level courses, the Wharton Sports Analytics and Business Initiative’s Moneyball Academy, as well as an online course, “Linear Algebra for Data Science in R” with DataCamp.

Eager has been interviewed by nfl.com’s Ian Rappoport about Cowboys in-game decision making and The Washington Post for commentary about sports analytics. He joined the legendary Peter King’s podcast about fourth-down decisions and is a frequent guest on Cris Collinsworth’s podcast.

Richard A Erickson helps people use mathematics and statistics to understand our world as well as make decisions with this data. He is a lifelong Green Bay Packer fan, and, like thousands of other cheeseheads, a team owner. He has taught over 25,000 students statistics through graduate-level courses, workshops, and his DataCamp courses on Generalized Linear Models in R and Hierarchical Models in R. He also uses Python on a regular basis to model scientific problems.

Erickson received his PhD in Environmental Toxicology with an applied math minor from Texas Tech where he wrote his dissertation on modeling population-level effects of pesticides. He has modeled and analyzed diverse datasets including topics such as soil productivity for the USDA, impacts of climate change on disease dynamics, and improving rural healthcare. Erickson currently works as a research scientist and has over 70 peer-reviewed publications. Besides teaching Eric about R and Python, he also taught Eric to like cheese curds.

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