STATISTICAL FOUNDATIONS IN SPORT ANALYTICS

Credit Hours: 3 · Graduate Corequisite: COMP 680

This course covers a few classes of statistical models that get used extensively in the profession of sport analytics, divided into three units: Measuring Team Strength, Estimating Player Talent, and Valuing Event Outcomes. For each model, we discuss its mathematical underpinnings and how to interpret the results, bridging the gap from model land to real life. We implement all models in R using current real-world data from a wide variety of sports, and students complete assignments to practice generalizing concepts to other sports. A final project encourages students to dive deeply into a model they've learned in our outside of class by applying it to a problem about which they are passionate. Topics include the Bradley-Terry model, regression to the mean, mixed-effects models, Regularized Adjusted Plus-Minus, Markov chains, and Markov decision processes. Graduate/Undergraduate Equivalency: SMGT 430 . Mutually Exclusive: Cannot register for SMGT 530 if student has credit for SMGT 430 .