How to Build a Hierarchical Bayesian Model with PyMC (and Make a Comeback)

Imagine you're at a packed stadium, heart pounding as your favorite NFL team trails by six points with just ten minutes remaining. The energy is electric, and every play could turn the tide. As a fan, you can't help but wonder: What are the chances of a comeback? If you have already built your hierarchical model of win probability using Stan and R by following last week's article you already know how to find the answer to your question. But maybe you didn't start already because you prefer Python instead? This week is your chance for a comeback!

In our latest exploration, we dive into the world of Bayesian modeling to predict NFL win probabilities using PyMC. We discuss how building hierarchical models with PyMC compares to using Stan, highlighting their structural similarities and unique advantages. The article delves into the pros and cons of scaling continuous variables within Bayesian frameworks and demonstrates how visualization tools like ArviZ can enhance our understanding of the model and its outputs. Ultimately, we showcase how these sophisticated models yield consistent and insightful predictions, reinforcing the power of Bayesian methods in sports analytics.

Curious to see how data science can elevate your sporting experience? Join us as we unravel the complexities of Bayesian hierarchical models using PyMC. Click here to read the full article and discover how cutting-edge analytics are reshaping our understanding of the game.