Sports Analytics: Turning Numbers into Game-Changing Insights.

Catalog description

Sports are no longer driven by intuition alone—analytics now shapes everything from player evaluation to game strategy. This course introduces students to the fascinating world of sports analytics, blending mathematics, probability, and decision theory with real-world applications across professional leagues. We begin with an Introduction to Sports Analytics, exploring how data revolutionized sports through examples like Moneyball’s impact on MLB and the rise of expected goals (xG) in soccer. Students will learn how rating systems and matrices underpin rankings such as college football power ratings and Elo scores in chess and esports. Next, we tackle Strength of Schedule and the debate between Wins vs. Points, using the NFL playoff race and NBA seeding controversies to illustrate why raw wins don’t always tell the full story. Through matrix-based models, students will see how advanced metrics provide deeper insights into team performance. The course then shifts to Voting Systems, where we analyze how MVP awards and Hall of Fame selections are determined. Students will explore majority voting, the Condorcet cycle, and preference lists, uncovering why award voting can lead to paradoxes. We dive into fairness and the Arrow Impossibility Theorem, asking: Can any voting system truly be fair? Real examples include NBA MVP debates and Heisman Trophy controversies. From there, we introduce PageRank and MVP Passing, applying algorithms originally designed for Google search to rank players based on passing networks—think quarterback influence in the NFL or assist networks in the NBA. Our Sports Statistics modules cover the Pythagorean expectation (used by MLB teams to predict wins from runs scored and allowed), the difference between rates vs. raw numbers, and concepts like persistence and reliability in player performance. We examine plus-minus ratings in basketball, park factors in baseball, and the tension between evaluation vs. prediction—why a player’s past performance doesn’t always forecast future success. The course also addresses Randomness in Sports, introducing probability basics and standard deviation through examples like coin-flip overtime in the NFL and shootout luck in hockey. We explore whether slumps are disasters or statistical noise, and whether hot streaks reflect luck or skill, using case studies from MLB batting streaks and NBA shooting variance. Finally, students will bring everything together in a capstone project, applying analytics to a real-world sports question—such as predicting playoff outcomes, ranking players, or evaluating team strategies. By the end of this course, students will not only understand the math behind sports analytics but also gain hands-on experience applying these concepts to real data, preparing them for careers in sports management, data science, or beyond. This course is only open to high school students.