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New jobs in sports analytics! Ron Yurko Special, someone worth listening to!
Two talks from Ron Yurko, one of the most important researchers to follow in sports analytics.

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Who to follow and listen in Sports Analytics? Ron Yurko is one of them

Looking for good and authentic content is becoming increasingly difficult so I believe having a curated lists of websites and persons you trust is really valuable.
Among those, I have Ron Yurko in my list: Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University, and the Director of the Carnegie Mellon Sports Analytics Center (CMSAC).
I have already shared content from him in older editions but in the past month he gave 2 talks I wanted to point out to you in case you missed it.
1) NFL Analytics & Teaching Bayesian Stats, with Ron Yurko
This is from the great Learning Bayesian Statistics podcast with Alex Andorra (who works for the Miami Marlins!). They talk about papers, analytics work, education in sports analytics and much more. Fun and easy going episode with technical knowledge.
Some 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.
2) College Football Volatility: A Bayesian State-Space Model of the Transfer Portal and NIL Impact
Main Topics
Transformation of College Football:
Overview of how the sport changed since 2014 with the playoff era, the transfer portal, and especially NIL (Name, Image, Likeness) rules that let players profit and move freely between schools.Statistical Challenge:
Modeling how roster volatility (due to transfers) affects team performance from one season to the next.Methodological Core:
Adaptation of the Glickman–Stern Bayesian state-space model (originally used for rating teams) to allow non-constant, time-varying variance, capturing how unpredictable a team becomes depending on roster changes.Extension:
Adds team-level and year-level variance depending on:
NIL era indicator (post-2021)
Number of incoming transfers
Later includes positional breakdowns (QB, DB, OL, etc.) to see which positions affect team volatility most.
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