Statistical Methods in Sports Analytics

Course Overview

This course teaches statistical thinking in the context of sports analytics, with a focus on building, evaluating, and interpreting statistical models. Core subject matter includes regression, shrinkage, confounding, uncertainty quantification, and related methods for prediction, inference, and decision-making with real sports data.

Course Schedule

Select a year to view the day-by-day lecture and lab schedule.

Expectations

• Attend all sessions, lectures, lab, and research in person
• No other coursework or internships during the program, and communicate all absents and conflicts
• Complete each assignment; it is essential to understanding the material. Ask for help when needed!
• Attend Moneyball Academy in person according to the TA schedule and engage positively with students
• Make consistent research progress with the expectation of publishing during the 2026-2027 academic year.
• Prepare a 15-minute presentation of your research project for WSABI staff on the final day of the program

Core References

• Statistical Models: Theory and Practice (Freedman)
• An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)
• Regression and Other Stories (Gelman, Hill, Vehtari)
• Selected papers from sports analytics literature