Sports Analytics

Catalog description

This course introduces statistical thinking and data analysis in the context of sports. Students learn to explore, visualize, and model sports data to evaluate performance, predict outcomes, and support strategic decisions. Emphasis is placed on the distinctive features of sports data, including player metrics, event outcomes, and temporal or spatial patterns, as well as on applying statistical and computational tools to real-world problems. Topics include exploratory data analysis, simulation, confidence intervals, hypothesis testing, resampling methods, regression, and introductory machine learning techniques such as decision trees, random forests, and clustering. By the end of the course, students will be able to interpret models, assess performance, and communicate data-driven insights in the sports domain. Students are expected to have proficiency in at least one computer language or software package capable of statistical analysis (e.g., R, Python, Stata, or MATLAB). A working understanding of basic probability, statistics, and a familiarity with linear regression, properties of the normal distribution, and common types of errors.