# #108 Modeling Sports & Extracting Player Values, with Paul Sabin

Page: https://stenobird.com/podcast/learning-bayesian-statistics/108-modeling-sports-extracting-player-values-with-paul-sabin
Text version: https://stenobird.com/podcast/learning-bayesian-statistics/108-modeling-sports-extracting-player-values-with-paul-sabin.md
Podcast: [Learning Bayesian Statistics](https://stenobird.com/podcast/learning-bayesian-statistics)
Published: 2024-06-14T11:00:00+00:00
Episode link: https://learnbayesstats.com/all-episodes/108-modeling-sports-extracting-player-values-paul-sabin
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Duration seconds: 4684

## Resource

Proudly sponsored by PyMC Labs , the Bayesian Consultancy. Book a call , or get in touch ! My Intuitive Bayes Online Courses 1:1 Mentorship with me Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Visit our Patreon page to unlock exclusive Bayesian swag ;) 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 Spor…

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