Modeling and Visualizing In-Game Win Probability in NCAA Men's Basketball
My fellowship project examined how statistical models can be used to estimate and visualize a team's chances of winning throughout a game. Using play-by-play data from NCAA men's basketball games, I collected, cleaned, and analyzed data to build several win probability models. These included frequentist and Bayesian logistic regression models as well as maximum likelihood estimation and Dynamic Bayes approaches.