#85 A Brief History of Sports Analytics, with Jim Albert
Learning Bayesian Statistics
The Importance of Conceptual Teaching Stats
If you don't know anything about the algorithm that's running in the background, you will just ignore the divergences or the target accept warnings and just use them as it is. You need to understand the algorithm before that makes any sense, right? And there is this amazing small web app that's basically a video demonstration of how MCMC works. So yeah, I put that into the show notes. It's like maybe more of a very broad question, but I'm really curious if you could change one thing in the way stats are taught right now at the level that you were teaching, what would it be? Well, I always thought that the intro level courses that try to be
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In this episode, I am honored to talk with a legend of sports analytics in general, and baseball analytics in particular. I am of course talking about Jim Albert.
Jim grew up in the Philadelphia area and studied statistics at Purdue University. He then spent his entire 41-year academic career at Bowling Green State University, which gave him a wide diversity of classes to teach – from intro statistics through doctoral level.
As you’ll hear, he’s always had a passion for Bayesian education, Bayesian modeling and learning about statistics through sports. I find that passion fascinating about Jim, and I suspect that’s one of the main reasons for his prolific career — really, the list of his writings and teachings is impressive; just go take a look at the show notes.
Now an Emeritus Professor of Bowling Green, Jim is retired, but still an active tennis player and writer on sports analytics — his blog, “Exploring Baseball with R”, is nearing 400 posts!
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
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, Nathaniel Neitzke, 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, Raul Maldonado, 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, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony and Joshua Meehl.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Jim’s website: https://bayesball.github.io/
- Jim’s baseball blog: https://baseballwithr.wordpress.com/
- Jim on GitHub: https://github.com/bayesball
- Jim on Twitter: https://twitter.com/albertbayes
- Jim on Linkedin: https://www.linkedin.com/in/jim-albert-22846b41/
- Jim’s baseball research: https://bayesball.github.io/BLOG/
- Probability and Bayesian Modeling book: https://monika76five.github.io/ProbBayes/
- Curve Ball -- Baseball, Statistics, and the Role of Chance in the Game: https://bayesball.github.io/curveball/curveball.htm
- Visualizing Baseball: https://bayesball.github.io/VB/
- Analyzing Baseball Data with R: https://www.amazon.com/gp/product/0815353510?pf_rd_p=c2945051-950f-485c-b4df-15aac5223b10&pf_rd_r=SFAV7QEGY9A2EDADZTJ5
- Teaching Statistics Using Baseball: https://bayesball.github.io/TSUB2/
- Ordinal Data Modeling: https://link.springer.com/book/10.1007/b98832?changeHeader
- Workshop Statistics (an intro stats course taught from a Bayesian point of view): https://bayesball.github.io/nsf_web/main.htm
- LBS #76, The Past, Present & Future of Stan, with Bob Carpenter: https://learnbayesstats.com/episode/76-past-present-future-of-stan-bob-carpenter/
- MCMC Interactive Gallery: https://chi-feng.github.io/mcmc-demo/app.html?algorithm=HamiltonianMC&target=banana
Abstract
written by Christoph Bamberg
In this episode, Jim Albert, a legend of sports analytics, Emeritus Professor at Bowling Green university, is our guest.
We talk about a range of topics, including his early interest in math and sports, challenges in analysing sports data and his experience teaching statistics.
We trace back the history of baseball sport analytics to the 1960s and discuss how new, advanced ways to collect data change the possibilities of what can be modelled.
There are also statistical approaches to American football, soccer and basketball games. Jim explains why these team sports are more difficult to model than baseball.
The conversation then turns to Jim’s substantial experience teaching statistics ad the challenges he sees in that. Jim worked on several books on sports analytics and has many blog posts on this topic.
We also touch upon the challenges of prior elicitation, a topic that has come up frequently in recent podcasts, how different stakeholders such as coaches and managers think differently about the sport and how to extract priors from their information.
For more tune in to episode 85 with Jim Albert.
Chapters
[00:00:00] Episode Begins
[00:04:04] How did you get into the world of statistics?
[00:11:17] Baseball is more advanced on the analytics path compared to other sports
[00:17:02] How is the data collected?
[00:24:43] Why is sports analytics important and is it turning humans into robots?
[00:32:51] Loss in translation problem between modellers and domain experts...?
[00:41:43] Active learning and learning through workshops
[00:51:08] Principles before methods
[00:52:30] Your favorite sports analytics model
[01:02:07] If you had unlimited time and resources which problem would you try to solve?
Transcript
Please note that this transcript is generated automatically and may contain errors. Feel free to reach out if you are willing to correct them.
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