Andrew Gelman: Bayesian Sports Statistics

Macrobius

Macrobius

Megaphoron
predicting sports outcomes is one of the toughest problems out there for stats...

Modelling the World Cup outcomes


(STAN is https://mc-stan.org/ )

Maurits Evers writes:

Inspired by your posts on using Stan for analysing football World Cup data here and here, as well as the follow-up here, I had some fun using your model in Stan to predict outcomes for this year’s football WC in Qatar. Here’s the summary on Netlify. Links to the code repo on Bitbucket are given on the website.
Your readers might be interested in comparing model/data/assumptions/results with those from Leonardo Egidi’s recent posts here and here.
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Enjoy, soccerheads!

P.S. See comments below. Evers’s model makes some highly implausible predictions and on its face seems like it should not be taken seriously. From the statistical perspective, the challenge is to follow the trail of breadcrumbs and figure out where the problems in the model came from. Are they from bad data? A bug in the code? Or perhaps a flaw in the model so that the data were not used in the way that were intended? One of the great things about generative models is that they can be used to make lots and lots of predictions, and this can help us learn where we have gone wrong. I’ve added a parenthetical to the title of this post to emphasize this point. Also good to be reminded that just cos a method uses Bayesian inference, that doesn’t mean that its predictions make any sense! The output is only as good as its input and how that input is processed.
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BLAH BLAH BLAH just show me the predictions: https://football-wc-predictions-2022.netlify.app/

Screenshot 2022-11-26 9.36.58 PM.png
Screenshot 2022-11-26 9.38.54 PM.png

More Whimsical - an IRL 'Ultimate Showdown of Ultimate Destiny' - only one can live:


(I think the setup is - which pair of seminar/motivational speakers would you prefer, or something like that)
 
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