Name: Developing Hierarchical Models for Sports Analytics using PyMC
Start: 2023-06-02T18:00:00+01:00
End: 2023-06-02T20:00:00+01:00
Location: Bayes Business School

It is a great pleasure to welcome Chris Fonnesbeck back to the Bayesian Mixer.

Thanks again to Bayes Business School for hosting us. Please register in time for the event.

Chris' talk is titled: **Developing Hierarchical Models for Sports Analytics using PyMC**

**Abstract:** Decision-making in sports has become increasingly data-driven with GPS, cameras, and other sensors providing streams of information at high spatial and temporal resolution. While machine learning is a popular approach for turning these data streams into actionable information, Bayesian statistical methods offer a robust alternative. They allow for the combining of multiple data sources, a natural means for imputing missing data, as well as full accounting for various system uncertainties.

In particular, hierarchical models provide a means for integrating information at multiple scales and adjusting for biases associated with small sample sizes. I will demonstrate a Bayesian workflow for model development using PyMC version 5, from data preparation through to the summarization of estimates and predictions, using baseball data.

Markus Gesmann

Bayesian Mixer London

Technology

Statistical Computing

Bayesian Statistics

Applied Statistics

Statistical Science

Statistical Modeling

Chris is the Principal Quantitative Analyst in Baseball Research & Development for the Philadelphia Phillies. He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia.​

Chris Fonnesbeck

Developing Hierarchical Models for Sports Analytics using PyMC

Bayes Business School

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Bayesian Mixer London

Developing Hierarchical Models for Sports Analytics using PyMC

Bayesian Mixer London

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