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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 #307121
Title: A Bayesian Model for Predicting Point Differentials in Sports Using Ratios
Author(s): Andrew Swift* and Andrew Tew
Companies: University of Nebraska at Omaha and University of Nebraska at Omaha
Keywords: Bayesian Model; Prediction; Sports Modeling; Points Ratio; Model Comparison

The application of statistical models has been used extensively to predict the outcomes of sporting competitions. However, for predicting point differential, the existence of a single model that is easily transferable across multiple sports is lacking. A primary reason for this is because each sport is defined by a unique set of characteristics. However in most sports, we can express on team's score as a fraction of the total score. We propose a family of Bayesian models based on that idea which can be easily transferable from sport to sport. We compare our model to other common models used for score prediction.

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

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