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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 and Mike Loncaric.
Takeaways:
- The evolution of sports modeling is tied to the availability of high-frequency data.
- Bayesian methods are valuable in handling messy, hierarchical data.
- Communication between data scientists and decision-makers is crucial for effective model use.
- Models are often wrong, and learning from mistakes is part of the process.
- Simplicity in models can sometimes yield better results than complexity.
- The integration of analytics in sports is still developing, with opportunities in various sports.
- Transparency in research and development teams enhances decision-making.
- Understanding uncertainty in models is essential for informed decisions.
- The balance between point estimates and full distributions is a challenge.
- Iterative model development is key to improving analytics in sports.
- It's important to avoid falling in love with a single model.
- Data simulation can validate model structures before real data is used.
- Gaussian processes offer flexibility in modeling without strict functional forms.
- Structural time series help separate projection from observation noise.
- Transitioning from sports analytics to consulting opens new opportunities.
- Continuous learning is essential in the field of statistics.
- The demand for Bayesian methods is growing across various industries.
- Community-driven projects can lead to innovative solutions.
Chapters:
03:07 The Evolution of Modeling in Sports Analytics
06:03 Transitioning from Academia to Sports Modeling
08:56 The Role of Bayesian Methods in Sports Analytics
11:49 Communicating Models and Insights to Decision Makers
15:12 Learning from Mistakes in Model Development
18:06 The Importance of Model Flexibility and Iteration
21:02 Utilizing Simulation for Model Validation
23:50 Choosing the Right Model Structure for Data
27:04 Starting with Simple Models and Building Complexity
29:29 Advancements in Gaussian Processes and PyMC
31:54 Exploring Structural Time Series and GPs
37:34 Transitioning to PyMC Labs and New Opportunities
42:40 Innovations in Variational Inference Methods
48:50 Future Vision for PyMC and Community Engagement
50:43 Surprises in Bayesian Methods Adoption
54:08 Reflections on Problem Solving and Influential Figures
Links from the show:
Transcript
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Learning Bayesian Statistics