CNET 5442 — Sports Analytics Through Data and Networks
4 semester hoursGraduateLecture
Explores quantitative and computational methods for analyzing sports data. Students model in-game events as time series, networks, probability distributions, and decision processes while working with real datasets. Core topics include regression, hypothesis testing, Bayesian inference, classification, and causal inference. Concepts extend to complex-systems approaches such as passing networks, player embeddings, tournament forecasting, and diffusion of tactics. Culminates in a group project in which students apply analytical methods to a dataset of their choice, preferably a novel dataset collected during the term. Highlights how analytics reshapes modern sports and develops transferable skills for analyzing complex systems in any domain.