Probabilistic Programming and PyMC3
Peadar Coyle
TL;DR
This paper introduces probabilistic programming using PyMC3, demonstrating its application through a Bayesian hierarchical model for rugby match analysis and prediction, focusing on the 2014 Six Nations tournament.
Contribution
It provides an accessible introduction to PyMC3 and showcases its use in sports analytics with a specific Bayesian model for rugby match outcomes.
Findings
Effective modeling of rugby match outcomes
Accurate predictions of match scores
Demonstration of PyMC3's capabilities in sports analytics
Abstract
In recent years sports analytics has gotten more and more popular. We propose a model for Rugby data - in particular to model the 2014 Six Nations tournament. We propose a Bayesian hierarchical model to estimate the characteristics that bring a team to lose or win a game, and predict the score of particular matches. This is intended to be a brief introduction to Probabilistic Programming in Python and in particular the powerful library called PyMC3.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Formal Methods in Verification · Numerical Methods and Algorithms