Learning Bayesian Statistics #108 Modeling Sports & Extracting Player Values, with Paul Sabin
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Takeaways
- Convincing non-stats stakeholders in sports analytics can be challenging, but building trust and confirming their prior beliefs can help in gaining acceptance.
- Combining subjective beliefs with objective data in Bayesian analysis leads to more accurate forecasts.
- The availability of massive data sets has revolutionized sports analytics, allowing for more complex and accurate models.
- Sports analytics models should consider factors like rest, travel, and altitude to capture the full picture of team performance.
- The impact of budget on team performance in American sports and the use of plus-minus models in basketball and American football are important considerations in sports analytics.
- The future of sports analytics lies in making analysis more accessible and digestible for everyday fans.
- There is a need for more focus on estimating distributions and variance around estimates in sports analytics.
- AI tools can empower analysts to do their own analysis and make better decisions, but it's important to ensure they understand the assumptions and structure of the data.
- Measuring the value of certain positions, such as midfielders in soccer, is a challenging problem in sports analytics.
- Game theory plays a significant role in sports strategies, and optimal strategies can change over time as the game evolves.
Chapters
00:00 Introduction and Overview
09:27 The Power of Bayesian Analysis in Sports Modeling
16:28 The Revolution of Massive Data Sets in Sports Analytics
31:03 The Impact of Budget in Sports Analytics
39:35 Introduction to Sports Analytics
52:22 Plus-Minus Models in American Football
01:04:11 The Future of Sports Analytics
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, 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, Steven Rowland, 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, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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 and Francesco Madrisotti.
Links from the show:
- LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4
- Paul’s website: https://sabinanalytics.com/
- Paul on GitHub: https://github.com/sabinanalytics
- Paul on Linkedin: https://www.linkedin.com/in/rpaulsabin/
- Paul on Twitter: https://twitter.com/SabinAnalytics
- Paul on Google Scholar: https://scholar.google.com/citations?user=wAezxZ4AAAAJ&hl=en
- Soccer Power Ratings & Projections: https://sabinanalytics.com/ratings/soccer/
- Estimating player value in American football using plus–minus models: https://www.degruyter.com/document/doi/10.1515/jqas-2020-0033/html
- World Football R Package: https://github.com/JaseZiv/worldfootballR
Transcript
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