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

Who This Book Is For

Our book has two target audiences. First, we wrote the book for people who want to learn about football analytics by doing football analytics. We share examples and exercises that help you work through the problems you’d face. Throughout these examples and exercises, we show you how we think about football data and then how to analyze the data. You might be a fan who wants to know more about your team, a fantasy football player, somebody who cares about which teams win each week, or an aspiring football data analyst. Second, we wrote this book for people who want an introduction to data science but do not want to learn from classic datasets such as flower measurements from the 1930s or Titanic survivorship tables from 1912. Even if you will be applying data science to widgets at work, at least you can learn using an enjoyable topic like American football.

We assume you have a high school background in math but are maybe a bit rusty (that is to say, you’ve completed a precalculus course). You might be a high school student or somebody who has not had a math course in 30 years. We’ll explain concepts as we go. We also focus on helping you see how football can supply fun math story problems. Our book will help you understand some of the basic skills used daily by football analysts. For fans, this will likely be enough data science skills. For the aspiring football analyst, we hope that our book serves as a springboard for your dreams and lifelong learning.

To help you learn, this book uses public data. This allows you to re-create all our analyses as well as update the datasets for future seasons. For example, we use only data through the 2022 season because this was the last completed season before we finished writing the book. However, the tools we teach you will let you update our examples to include future years. We also show all the data-wrangling methods so that you can see how we format data. Although somewhat tedious at times, learning how to work with data will ultimately give you more freedom: you will not be dependent on others for clean data.

Who This Book Is Not For

We wrote this book for beginners and have included appendixes for people with minimal-to-no prior programming experience. People who have extensive experience with statistics and programming in R or Python would likely not benefit from this book (other than by seeing the kind of introductory problems that exist in football analytics). Instead, they should move on to more advanced books, such as 'R for Data Science', 2nd edition by Hadley Wickham et al. (O’Reilly, 2023) to learn more about R, or 'Python for Data Analysis', 3rd edition by Wes McKinney (O’Reilly, 2022) to learn more about Python. Or maybe you want to move into more advanced books on topics we touch upon in this book, such as multivariate statistics, regression analysis, or the Posit Shiny application.

We focus on simple examples rather than complex analysis. Likewise, we focus on simpler, easier-to-understand code rather than the most computationally efficient code. We seek to help you get started quickly and connect with real-world data. To use a quote often attributed to Antoine de Saint-Exupéry: If you wish to build a ship, do not divide the men into teams and send them to the forest to cut wood. Instead, teach them to long for the vast and endless sea.

Thus, we seek to quickly connect you to football data, hoping this connection will inspire and encourage you to continue learning tools in greater depth.

Review

"Transforms complex data into accessible wisdom. A must-read to better understand the game."

- John Park, Director of Strategic Football Operations, Dallas Cowboys 

"The case study-drive approach makes it easy to understand how to approach the most common tasks in football analytics. I love how the book is filled with data science and football lore. It's a must read for anyone interested in exploring football data."

- Richie Cotton, Data Evangelist at DataCamp 

"A practical guide for learning, implementing, and generating insight from meaningful analytics within the world of football. Fantastic read for sports enthusiasts and data-drive professionals alike."

- John Oliver, data scientist 

"This is a great reference to learn how data science is applied to football analytics. The examples teach a wide range of visualization, data wrangling, and modeling techniques using real-world football data."

- Ryan Day, advanced data scientist 

"One of the rare books out there written by data science educators that also work in the industry. Football analytics in both Python and R throughout, too! Excellent, thoughtful examples as well."

- Dr. Chester Ismay, educator, data scientist, and consultant 

"I had the pleasure to be a tech reviewer for this book and recommend it to anyone interested in, as the subtitle suggests, learning data science through the lens of sports."

- George Mount, Independent data analyst and data memelord, from a review on Python-Bloggers

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.