36-660 Special Topics: Sports Analytics

9 units, Carnegie Mellon University

This course introduces students to fundamental topics in sports analytics and the relevant statistical methods for tackling problems in this growing area. The first half of the course will cover foundational topics in sports analytics including models for the expected value of game states, win probability, team ratings, and hierarchical models for player evaluation. The second half of the course will focus on spatio-temporal methods appropriate for modeling complex player-tracking data. The focus is on understanding the foundations of the considered methods and introducing software for implementation. Students will develop their own sports analytics project using techniques covered in the course for their final assessment.

Prerequisites: none listed

Offered: Spring 2027, Spring 2026

Courseweave needs JavaScript to show sections, workloads and planning tools.