Intro

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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, 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 and Suyog Chandramouli.

Takeaways:

• Player tracking data revolutionized sports analytics.
• Decision-making in sports involves managing uncertainty and budget constraints.
• Luke emphasizes the importance of portfolio optimization in team management.
• Clubs with high budgets can afford inefficiencies in player acquisition.
• Statistical methods provide a probabilistic approach to player value.
• Removing human bias is crucial in sports decision-making.
• Understanding player performance distributions aids in contract decisions.
• The goal is to maximize performance value per dollar spent.
• Model validation in sports requires focusing on edge cases.
• Generative models help account for uncertainty in player performance.
• Computational efficiency is key in handling large datasets.
• A diverse skill set enhances problem-solving in sports analytics.
• Broader knowledge in data science leads to innovative solutions. 
• Integrating software engineering with statistics is crucial in sports analytics.
• Model validation often requires more work than model fitting itself.
• Understanding the context of data is essential for accurate predictions.
• Continuous learning and adaptation are essential in analytics.

Chapters:

11:58 Transition from Academia to Sports Analytics

20:44 Evolution of Sports Analytics and Data Sources

23:53 Modeling Uncertainty in Decision Making

32:05 The Role of Statistical Models in Player Evaluation

39:20 Generative Models and Bayesian Framework in Sports

46:54 Hacking Bayesian Models for Better Performance

49:55 Understanding Computational Challenges in Bayesian Inference

52:44 Exploring Different Approaches to Model Fitting

56:30 Building a Comprehensive Statistical Toolbox

01:00:37 The Importance of Data Management in Modeling

01:03:21 Iterative Model Validation and Diagnostics

01:06:53 Uncovering Insights from Sports Data

01:16:47 Emerging Trends in Sports Analytics

01:21:30 Future Directions and Personal Aspirations

Links from the show:

• Luke’s website: http://www.lukebornn.com/ 
• Luke on Linkedin: https://www.linkedin.com/in/lukebornn/
• Luke on Wharton Moneyball: https://knowledge.wharton.upenn.edu/podcast/moneyball-highlights/luke-bornn-part-owner-of-ac-milan/
• LBS #108 Modeling Sports & Extracting Player Values, with Paul Sabin: https://learnbayesstats.com/episode/108-modeling-sports-extracting-player-values-paul-sabin

Transcript

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