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

by Eric A. Eager, Richard A. Erickson

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

Apply basic statistical concepts to football datasetsDescribe football data with quantitative methodsCreate efficient workflows that offer reproducible resultsUse data science skills such as web scraping, manipulating data, and plotting dataImplement statistical models for football dataLink data summaries and model outputs to create reports or presentations using tools such as R Markdown and R ShinyAnd more

578 pages, Kindle Edition

Published August 15, 2023

Reviews

[Enrique · Rating 4 out of 5]

Great, but flawed

1. Good intro to DS concepts. Helped augment my Coursera course on Advanced Data Analytics
2. I appreciate how they flip between languages, and explain the deltas.
3. Quick read, make sure to use Jupyter notebooks for python code.

Now for the bad part:
1. Too superficial in explanation
2. Some examples are in R only (only two that I remember)
3. A lot of links to external books and articles to expand, but difficult to digest in Kindle format.