36-460 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 building models for the expected value of game states and multilevel modeling for player and team evaluation. The second half of the course focuses on Bayesian thinking with hierarchical models to estimate and quantify the uncertainty around player / team ratings across multiple sports, including static and dynamic techniques. Remaining time of the course will introduce students to working with complex player-tracking data and relevant spatio-temporal methods. All methods in the course are motivated by real sports problems that a statistician / data scientist working in sports analytics encounters. 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: 36-401 (min C)
Offered: Spring 2027, Spring 2026
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