Episode 80 of the Tennis Abstract Podcast features Martin Ingram (@xenophar), author of a recent academic paper, A point-based Bayesian hierarchical model to predict the outcome of tennis matches.

If you’re interested in learning more about what goes into a forecasting system, this one’s for you. We start with a discussion of the advantages as well as the limitations of the common “iid” assumption, that points are independent and identically distributed. Martin’s model, which relies on the iid assumption, incorporates each player’s serve and return skill, in addition to surface preferences and tournament-specific characteristics. In our conversation, he explains how it works, and why this sort of model is able to provide reasonable forecasts even with limited data.

That’s just the beginning. Martin suggests several possible additions to his model, and we close by considering the importance of domain knowledge in this sort of statistical work.

Thanks for listening!

(Note: this week’s episode is about 65 minutes long; in some browsers the audio player may display a different length. Sorry about that!)

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