Acknowledgments

A book of this scope is, by its nature, a collaborative undertaking, and we owe debts of gratitude to many individuals and communities whose contributions—direct and indirect—made it possible.

Our first thanks go to the academic researchers whose work forms the intellectual foundation of this textbook. The scholars who have studied sports betting markets with the same rigor applied to financial markets—publishing in journals of statistics, econometrics, operations research, and computer science—have elevated the field from folklore to science. We are particularly indebted to the researchers in market microstructure whose frameworks for understanding price formation, information asymmetry, and market efficiency translated so naturally to the study of betting lines. The growing body of peer-reviewed literature on prediction markets, rating systems, and machine learning for sports forecasting gave us a solid base on which to build. We have cited these contributions throughout the text, but a citation can only partially express the intellectual debt we owe.

We are equally grateful to the sports analytics community at large. The annual MIT Sloan Sports Analytics Conference and similar gatherings have created a culture of openness and cross-pollination that benefits everyone in the field. The practitioners who share analyses on blogs, in podcasts, on social media, and in online forums—often at the cost of eroding their own competitive advantage—have created a public knowledge base of extraordinary depth. The communities on sports analytics Twitter, Discord servers, and specialized forums provided a steady stream of ideas, critiques, and reality checks that sharpened our thinking at every stage.

The open-source software community deserves special recognition. This book's code is built entirely on freely available tools: Python and its scientific ecosystem, including NumPy, pandas, scikit-learn, statsmodels, PyTorch, XGBoost, and many others. The developers and maintainers of these projects—most of them volunteers or modestly funded—have democratized quantitative work in ways that would have been unimaginable a generation ago. We also acknowledge the creators of sports data APIs and open datasets who have made the raw material of sports analytics accessible to independent researchers and hobbyists. Without open data, the exercises and case studies in this book could not exist in their current form.

Our beta readers gave generously of their time and expertise. Early drafts of this manuscript were reviewed by practitioners working at sportsbooks, independent bettors with years of profitable track records, data scientists in adjacent fields, graduate students in statistics, and complete beginners who represented our broadest target audience. Their feedback was invaluable. They caught errors, flagged unclear explanations, challenged assumptions, suggested additional examples, and told us—sometimes bluntly—when a chapter failed to deliver on its promise. The book is immeasurably better for their honesty. Any remaining errors or shortcomings are, of course, entirely our own.

We thank the editorial and production team at Quantitative Sports Press for their patience, professionalism, and commitment to quality. Producing a textbook of this length and technical complexity—with hundreds of equations, code listings, figures, and tables—is a monumental logistical challenge, and they met it with skill and good humor.

Several individuals merit specific mention. We are grateful to the colleagues and mentors who read individual chapters in their areas of expertise and offered detailed technical feedback. We thank those who contributed datasets, pointed us to obscure but important papers, or simply engaged us in the kind of extended, probing conversations that clarify one's thinking in ways that solitary work cannot. We are thankful to our families and partners, who endured the long weekends, late nights, and occasional distraction that accompany a project of this magnitude.

Finally, we acknowledge the inherent tension in writing a textbook about a competitive domain. Every insight we formalize and publish is, in some sense, an edge shared and therefore diminished. We have proceeded with the conviction that the long-term health of the field depends on raising the general level of understanding, that education is not a zero-sum game, and that the problems ahead are interesting enough to sustain progress for those willing to do the work. We hope our readers agree.