Acknowledgments

A textbook of this scope is necessarily a collaborative effort, drawing on the contributions of many individuals and communities.

The Football Analytics Community

We are deeply grateful to the broader football analytics community, whose open sharing of ideas, methods, and code has made this field accessible to newcomers. Special recognition goes to:

The nflfastR and nfl_data_py Teams: Ben Baldwin, Sebastian Carl, and the many contributors who have built and maintained the data infrastructure that makes modern football analytics possible. Without free, accessible play-by-play data, this textbook could not exist.

Football Outsiders: Aaron Schatz and the Football Outsiders team pioneered many of the concepts covered in this book, including DVOA and success rate. Their work demonstrated that rigorous analysis could coexist with engaging football writing.

The Athletic and ESPN Analytics Teams: For bringing sophisticated analysis to mainstream sports media and demonstrating the appetite for data-driven football content.

Academic Researchers: The sports analytics research community, including faculty and students at MIT Sloan Sports Analytics Conference and similar venues, has provided the theoretical foundations for many practical applications.

Open Source Software

This book relies heavily on open source software, and we acknowledge the developers of:

  • Python and the Python Software Foundation
  • Pandas, NumPy, and SciPy for data manipulation and scientific computing
  • Matplotlib, Seaborn, and Plotly for visualization
  • Scikit-learn and Statsmodels for statistical modeling
  • PyTorch and TensorFlow for deep learning
  • Jupyter for interactive computing
  • Jupyter Book for publishing this textbook

Beta Readers and Reviewers

We thank the many students and practitioners who reviewed early drafts, tested code examples, and provided feedback:

  • The students of our pilot courses, whose questions and confusion helped us identify areas needing clarification
  • Professional analysts who verified that our coverage aligns with industry practice
  • Technical reviewers who caught errors and suggested improvements

Any remaining errors are entirely our own.

Data Providers

We acknowledge the following organizations for making data publicly available:

  • The National Football League for play-by-play data
  • Pro Football Reference for historical statistics
  • ESPN for various metrics and projections
  • The NFL Big Data Bowl for player tracking data samples

Personal Acknowledgments

We thank our families for their patience during the writing process, our colleagues for stimulating discussions, and our students for their enthusiasm and curiosity.

You, the Reader

Finally, we thank you for choosing to learn football analytics. The field grows stronger with each new analyst who brings fresh perspectives and rigorous thinking. We hope this book serves you well on your journey.

If we have seen further, it is by standing on the shoulders of giants. — Adapted from Isaac Newton