Date of Graduation

5-2025

Document Type

Thesis

Degree Name

Master of Science in Statistics and Analytics (MS)

Degree Level

Graduate

Department

Statistics and Analytics

Advisor/Mentor

Plummer, Sean

Committee Member

Chakraborty, Avishek

Second Committee Member

Zhang, Qingyang

Keywords

Bayesian; Sports Betting; Statistics

Abstract

This thesis explores the use of latent factor models to uncover hidden structures in pair wise outcomes derived from Over/Under betting markets in sports betting. Specifically, we implement and evaluate the Eigen model, a latent space model that represents dyadic data using node-specific vectors whose inner product govern edge probabilities. By modeling relationships between teams as adjacency matrices of binary outcomes, we investigate the extent to which the Eigen model captures both homophily, the tendency of similar teams to yield consistent betting results, and stochastic equivalence, where different teams exhibit indistinguishable patterns of Over/Under outcomes. A Bayesian formulation of the model allows for posterior inference on team-level latent traits and model parameters, while poste rior predictive checks are used to assess model fit. We further discuss the potential for such models to detect systematic biases in bookmaker lines, highlighting how latent structures in match-ups may inform profitable betting strategies under market inefficiencies. Simulated and real-world betting data are used to illustrate the model’s capabilities and limitations.

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