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. Football Analytics
- Baseball Has the Three True Outcomes: What Are Football’s?
- Do Running Backs Matter?
- Understanding the Passing Game Through Data
- Can You Beat the Odds?
- Do Teams Know How to Draft?
- Tools for Football Analytics
- First Steps in Python and R
- Example Data: Who Throws Deep?
- Suggested Reading
- 2. Exploring Data Analysis: Stable Versus Unstable Quarterback Statistics
- 3. Simple Linear Regression: Rushing Yards Over Expected
- 4. Multiple regression: Rushing Yards over Expected
- A. Python and R Basics
- B. Summary Statistics and Data Wrangling: Passing the Ball
- About the Authors
- Title: Football Analytics with Python & R
- Release date: November 2023
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492099567
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