Our guest is Paul Sabin, a sports data scientist and an analytics writer at ESPN. Paul has worked on predictive and descriptive models for sports performance including ESPN's proprietary metrics such as BPI, FPI, and Strength of Record (SOR). Paul explains how he ended up working for ESPN, why he is Bayesian instead of a frequentist, and how a Bayesian approach to the real world can make you more informed and better off. We also discuss the applications of sports analytics in the major US sports leagues including the NFL and the NBA. Paul references a book for those who want to learn more. The book is called The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy.
