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Activity Number:
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578
- Bayesian Methodologies in Sports Statistics
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Sports
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Abstract #307121
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Title:
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A Bayesian Model for Predicting Point Differentials in Sports Using Ratios
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Author(s):
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Andrew Swift* and Andrew Tew
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Companies:
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University of Nebraska at Omaha and University of Nebraska at Omaha
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Keywords:
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Bayesian Model;
Prediction;
Sports Modeling;
Points Ratio;
Model Comparison
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
