#140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko

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Sep 3, 2025

Ron Yurko, an Assistant Teaching Professor and Director of Sports Analytics at Carnegie Mellon University, shares his expertise in Bayesian statistics applied to NFL analytics. He emphasizes the significance of teaching students model-building skills and engaging them in practical projects. The discussion highlights challenges in player performance modeling, the impact of tracking data, and the evolving curriculum in sports analytics education. Ron also advocates for developing a robust sports analytics portfolio to help aspiring analysts thrive in the industry.

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ANECDOTE

From Sports Fan To Statistical Researcher

Ron Yurko's path into statistics began from a sports obsession sparked by Moneyball and fantasy sports.
An internship with the Pittsburgh Pirates charting defensive positions launched his interest in research and modeling.

INSIGHT

Hierarchical Models As The Bayes Gateway

Multilevel (hierarchical) models are the gateway to Bayesian thinking because they reveal pooling and shrinkage.
Bayesian framing forces explicit assumptions and enables prior predictive checks and full uncertainty propagation.

ADVICE

Make Assumptions Explicit Early

Teach explicit modeling and prior predictive checks to force clarity about assumptions.
Make students write and check the data-generating process before fitting models.

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Takeaways:

Teaching students to write out their own models is crucial.
Developing a sports analytics portfolio is essential for aspiring analysts.
Modeling expectations in sports analytics can be misleading.
Tracking data can significantly improve player performance models.
Ron encourages students to engage in active learning through projects.
The importance of understanding the dependency structure in data is vital.
Ron aims to integrate more diverse sports analytics topics into his teaching.

Chapters:

03:51 The Journey into Sports Analytics

15:20 The Evolution of Bayesian Statistics in Sports

26:01 Innovations in NFL WAR Modeling

39:23 Causal Modeling in Sports Analytics

46:29 Defining Replacement Levels in Sports

48:26 The Going Deep Framework and Big Data in Football

52:47 Modeling Expectations in Football Data

55:40 Teaching Statistical Concepts in Sports Analytics

01:01:54 The Importance of Model Building in Education

01:04:46 Statistical Thinking in Sports Analytics

01:10:55 Innovative Research in Player Movement

01:15:47 Exploring Data Needs in American Football

01:18:43 Building a Sports Analytics Portfolio

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli and Adam Tilmar Jakobsen.

Links from the show:

Ron’ website: https://www.stat.cmu.edu/~ryurko/
Ron on LinkedIn: https://www.linkedin.com/in/ron-yurko-stats/
Ron on GitHub: https://github.com/ryurko
Ron’s Substack: https://substack.com/@ronyurko
Ron on Google Scholar: https://scholar.google.com/citations?user=CBT7NWQAAAAJ&hl=en
nflWAR – A Reproducible Method for Offensive Player Evaluation in Football: https://arxiv.org/abs/1802.00998
Going Deep – Models for Continuous-Time Within-Play Valuation of Game Outcomes in American Football with Tracking Data: https://arxiv.org/abs/1906.01760
A Bayesian circular mixed-effects model for explaining variability in directional movement in American football: https://arxiv.org/abs/2507.06122
LBS #42 – How to Teach and Learn Bayesian Stats, with Mine Dogucu: https://learnbayesstats.com/episode/42-teach-bayesian-stats-mine-dogucu
Unveiling True Talent – The Soccer Factor Model for Skill: https://github.com/AlexAndorra/football-modeling/tree/main/01_SFM
LBS #108 – Modeling Sports & Extracting Player Values, with Paul Sabin: https://learnbayesstats.com/episode/108-modeling-sports-extracting-player-values-paul-sabin
CMU Sport Analytics Conference: https://www.stat.cmu.edu/cmsac/conference

Transcript

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