Football Analytics with Python & R

Description

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 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