Football Analytics with Python & R

✍ Scribed by Eric A. Eager; Richard A. Erickson

• Publisher: O'Reilly Media, Inc.
• Year: 2023
• Tongue: English
• Leaves: 300
• Category: Library

✦ Synopsis

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

Preface

Who This Book Is For
Who This Book Is Not For
How We Think About Data and How to Use This Book
A Football Example
What You Will Learn from Our Book
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments

1. Football Analytics

   Baseball Has the Three True Outcomes: Does Football?
   Do Running Backs Matter?
   How Data Can Help Us Contextualize Passing Statistics
   Can You Beat the Odds?
   Do Teams Beat the Draft?
   Tools for Football Analytics
   First Steps in Python and R
   Example Data: Who Throws Deep?
   nflfastR in R
   nfl_data_py in Python
   Data Science Tools Used in This Chapter
   Suggested Readings

2. Exploring Data Analysis: Stable Versus Unstable Quarterback Statistics

   Defining Questions
   Obtaining and Filtering Data
   Summarizing Data
   Plotting Data
   Histograms
   Boxplots
   Player-Level Stability of Passing Yards per Attempt
   Deep Passes vs Short Passes
   So, What Should We Do with This Insight?
   Data Science Tools Used in This Chapter
   Exercises with Your Data
   Suggested Readings

3. Simple Linear Regression: Rushing Yards Over Expected

   Exploratory Data Analysis
   Simple Linear Regression
   Who Was the Best in RYOE?
   Is RYOE a Better Metric?
   Data Science Tools Used in This Chapter
   Exercises
   Suggested Readings

4. Multiple Regression: Rushing Yards Over Expected

   Definition of Multiple Linear Regression
   Exploratory Data Analysis
   Applying Multiple Linear Regression
   Analyzing RYOE
   So, Do Running Backs Matter?
   Assumption of Linearity
   Data Science Tools Used in This Chapter
   Exercises
   Suggested Readings

5. Generalized Linear Models: Completion Percentage Over Expected

   General Linear Models
   Building a GLM
   GLM Application to Completion Percentage
   Is CPOE More Stable Than Completion Percentage?
   A Question About Residual Metrics
   A Brief Primer on Odds Ratios
   Data Science Tools Used in This Chapter
   Exercises
   Suggested Readings

6. Using Data Science for Sports Betting: Poisson Regression and Passing

   The Main Markets in Football
   Application of Poisson Regression: Prop Markets
   The Poisson Distribution
   Individual Player Markets and Modeling
   Understanding Poisson Regression Coefficients
   Closing Thoughts on GLMS
   Data Science Tools Used in This Chapter
   Exercises
   Suggested Readings

7. Webscraping: Obtaining and Analyzing Draft Picks

   for Loops
   Web Scraping with Python
   Webscraping in R
   Analyzing the NFL Draft
   The Jets/Colts 2018 Trade Evaluated
   Are Some Teams Better at Drafting Players than Others?
   Data Science Tools Used in This Chapter
   Suggested Reading
   Exercises

8. Principal Component Analysis and Clustering: Player Attributes

   Web Scrapping and Visualizing NFL Combine Data
   Introduction to PCA
   PCA on All Data
   Clustering Combine Data
   Clustering Combine Data in Python
   Clustering Combine Data in R
   Closing Thoughts on Clustering
   Data Science Tools Used in This Chapter
   Exercises
   Suggested Reading

9. Advanced Tools and Next Steps

   Advanced Modeling Tools
   Time Series Analysis
   Multivariate Statistics Beyond PCA
   Quantile Regression
   Bayesian Statistics and Hierarchical Models
   Survival Analysis/Time-to-event
   Bayesian Networks/Structural Equation Modeling
   Machine Learning
   Command Line Tools
   Bash Example
   Suggested Reading for bash
   Version Control
   Git
   GitHub and GitLab
   GitHub Webpages and Resumes
   Suggested Reading for Git
   Style Guides and Linting
   Packages
   Suggested Reading for Packages
   Computer Environments
   Interactives and Report Tools to Share Data
   Artificial Intelligence Tools
   Conclusion

A. Python and R Basics

Obtaining Python and R
    Local Installation
    Cloud-based Options
Scripts
Packages in Python and R
nlffastR and nfl_py_data Tips
Integrated Development Environments
Basic Python Data Types

B. Summary Statistics and Data Wrangling: Passing the Ball

Basic Statistics
    Averages
    Variability and Distribution
    Uncertainty Around Estimates
Filtering and Selecting Columns
Calculating Summary Statistics with Python and R
A Note about Presenting Summary Statistics
Improving your presentation
Exercises
Future readings

C. Data Wrangling Fundamentals

Logic Operators
Filtering and Sorting Data
Cleaning
Piping in R
Checking and Cleaning Data for Outliers
Merging Multiple Datasets

Glossary
About the Authors