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Activity Number: 578 - Bayesian Methodologies in Sports Statistics
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Sports
Abstract #304922
Title: Model Based Estimation of Baseball Batting Metrics
Author(s): Lahiru Wickramasinghe* and Alexandre Leblanc and Saman Muthukumarana
Companies: University of Manitoba and University of Manitoba and University of Manitoba
Keywords: Weighted Likelihood; MAMSE weights; Baseball; Multinomial; Sparse data; Dirichlet Process

We introduce an approach to model the batting outcomes of baseball batters based on the weighted likelihood approach and make use of our methodology to estimate commonly used baseball batting metrics. The weighted likelihood allows the other batters to contribute to the inference so that the relevant information they contain is not lost and the weights are determined based on their dissimilarities with the target batter. MAMSE weights are used as the likelihood weights. For comparison, we implemented a semi-parametric Bayesian approach based on Dirichlet process which enables the borrowing information across batters while providing natural clustering mechanism. We demonstrate and compare these approaches using 2018 Major League Baseball (MLB) batters data.

Authors who are presenting talks have a * after their name.

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