
Modeling the Evolution of Distributions Using Ordered Probit Models by Nick Skiera ’24
Wed, March 6th, 2024
1:10 pm - 1:50 pm
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Modeling the Evolution of Distributions Using Ordered Probit Models and Bayesian Techniques: An Application to Winning Percentages of Major League Baseball Teams by Nick Skiera ’24, Wednesday March 6, 1:10 – 1:50pm, NSB 015, Wachenheim, Statistics Colloquium
Abstract: Winning percentages and the corresponding timely trends of teams in Major League Baseball are always a topic of interest. In this talk, I will start by introducing the setup of modeling transition probability matrices over time and across teams. An ordered probit model will be used to model and predict these changes, as well as link other features of individual teams. The methodology will be concluded by Bayesian methods for statistical inference. To finish up, we will take a look at the results of the application to real data from Major League Baseball.
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