Acknowledgments

The creation of a comprehensive textbook is never a solitary endeavor. This work stands on the shoulders of countless contributors to the field of sports analytics, and we are grateful to acknowledge their influence.

Data Providers and Tool Creators

First and foremost, we acknowledge the developers and maintainers of the College Football Data API (CFBD). Their commitment to making college football data accessible has transformed what is possible in sports analytics education. Without free, high-quality data, teaching these concepts would be immeasurably harder.

We also thank the teams behind the essential Python libraries used throughout this text:

  • pandas and NumPy teams for the foundational data manipulation tools
  • matplotlib and seaborn developers for visualization capabilities
  • scikit-learn contributors for accessible machine learning implementations
  • statsmodels maintainers for statistical modeling tools

The creators of nflfastR and cfbfastR deserve special mention. While their tools focus on R, their methodological contributions—particularly in expected points and win probability modeling—inform approaches used throughout this textbook.

The Analytics Community

The sports analytics community is characterized by an unusual generosity of knowledge. Many concepts in this textbook were first shared freely on social media, blogs, and forums by practitioners who believe in advancing the field collectively.

We acknowledge:

  • Football Outsiders and their pioneering work in football analytics methodology
  • Pro Football Focus for demonstrating the value of detailed play-level analysis
  • ESPN's Stats & Information Group for advancing public understanding of football analytics
  • The Athletic's analytics writers for making sophisticated analysis accessible
  • Academic researchers in sports analytics whose peer-reviewed work grounds many techniques presented here

The Twitter/X analytics community deserves collective thanks. The daily exchange of ideas, methods, and constructive criticism among practitioners creates an ongoing seminar that benefits everyone who participates.

Educational Influences

This textbook draws on pedagogical approaches developed by educators in statistics, data science, and sports analytics:

  • The Carnegie Mellon Sports Analytics Club and similar student organizations that demonstrate what motivated learners can achieve
  • Sloan Sports Analytics Conference organizers and participants who showcase cutting-edge applications
  • MITSportsAnalytics and other academic programs that have pioneered formal sports analytics education
  • Countless professors who have shared syllabi, assignments, and teaching approaches that informed our pedagogical choices

The Game Itself

We acknowledge college football's place in American culture and the passion it inspires. The coaches, players, and staff who create the games we analyze deserve recognition. Behind every data point is human effort, strategic thinking, and athletic excellence.

We particularly recognize the student-athletes whose performance generates the data we study. Their dedication to their sport and education reminds us that analytics serves the game, not the other way around.

Open Source Philosophy

This textbook embodies the open source ethos that characterizes the best of sports analytics. We commit to:

  • Sharing methods openly so others can learn and improve upon them
  • Acknowledging sources and giving credit for ideas
  • Contributing back to the community that has given us so much
  • Welcoming feedback and corrections that improve this work

Future Contributors

Finally, we acknowledge you—the students and practitioners who will use this textbook. Your questions will reveal where explanations need improvement. Your applications will extend these methods in directions we haven't imagined. Your contributions will advance the field.

Sports analytics is a collaborative endeavor. We look forward to what you will build.

Thank you all.