Acknowledgments

Pioneers of Basketball Analytics

This textbook stands on the shoulders of giants who developed the field of basketball analytics. We gratefully acknowledge:

Dean Oliver, whose book Basketball on Paper (2004) established the foundational framework for possession-based analysis and remains essential reading for anyone in the field. His Four Factors of Basketball Success continue to guide team evaluation.

John Hollinger, who brought advanced statistics to mainstream basketball coverage through his work at ESPN, creating widely-used metrics like Player Efficiency Rating (PER) and Game Score.

The Basketball Reference team, particularly Sean Forman, whose comprehensive database has been an invaluable resource for analysts, researchers, and fans worldwide.

Dan Rosenbaum, whose early work on Adjusted Plus-Minus opened new avenues for measuring player impact beyond box score statistics.

Seth Partnow and the team at Nylon Calculus (now The Athletic), who elevated basketball analytics writing and made sophisticated concepts accessible to broader audiences.

Kirk Goldsberry, whose innovative shot charts and spatial visualizations revolutionized how we see and understand shooting in basketball.

Academic Contributors

The academic community has provided rigorous foundations for basketball analytics methods:

David Berri and colleagues, whose Wins Produced metric and research on player valuation challenged conventional wisdom about basketball performance.

Research teams at MIT Sloan Sports Analytics Conference, which has served as the premier venue for sharing new methods and ideas in sports analytics.

Statisticians and computer scientists who developed the machine learning and statistical methods we apply to basketball data.

Open Source Community

Modern basketball analytics is built on open-source tools, and we thank:

The Python community, particularly developers of NumPy, pandas, matplotlib, seaborn, and scikit-learn, whose libraries make data analysis accessible.

Swar Patel and contributors to the nba_api package, which provides reliable access to official NBA statistics.

The Jupyter project, whose notebooks enable reproducible, interactive analysis and have become the standard for data science education.

Data Providers

We acknowledge the data sources that make this work possible:

NBA Stats and the official league statistics portal Basketball Reference and Sports Reference LLC Second Spectrum and player tracking technology providers Kaggle and community-contributed datasets

Reviewers and Educators

Special thanks to the educators and reviewers who provided feedback:

  • Professors who tested draft chapters in their courses
  • Students whose questions improved our explanations
  • Industry professionals who reviewed technical content
  • Copy editors who improved clarity and consistency

Personal Acknowledgments

Finally, we thank our families, friends, and colleagues who supported this multi-year project. Writing a comprehensive textbook requires countless hours, and their patience and encouragement made it possible.

Any errors that remain are solely our responsibility.