Sports Analytics

(1.5 Credit Hours) · Undergraduate

This course develops essential data analytics and AI skills through the engaging context of sports, teaching students to think probabilistically, make decisions under uncertainty, and extract insights from complex datasets including box score data, play-by-play sequences, Statcast tracking data, and GPS performance metrics. Students will master fundamental analytical techniques, performance quantification through KPIs, ranking systems, simulation modeling, and machine learning approaches, using sports as a data-rich laboratory where analytical decisions have immediate, visible impact. Through hands-on work and strategic use of AI tools, the course covers core business analytics capabilities: cleaning messy data, identifying meaningful patterns, controlling for confounding factors, building predictive models, and communicating data-driven recommendations to diverse audiences. The course culminates in a student-designed analytics project that replicates the complete workflow of a professional sports analytics team, from problem identification and data acquisition through modeling, interpretation, and executive presentation, providing hands-on experience with the end-to-end analytical process used by organizations to drive strategic decisions.