Recreations in Randomness: From Glicko Rating to World Cup

Thank you, Mark and Steph, for joining this podcast.

And Mark, I want to first thank you for creating and editing this HDR columns on recreations randomness.

If I remember correctly, the name actually you created, uh, which is a really wonderful name.

It's a column on the data science for the leisure activities and sports is part of it, but there are a lot more, more than that.

And Steph, I want to welcome you as the, uh, new co-editor working with Mark.

But I also want to thank you for your wonderful article back in twenty twenty-one for the column on why the tennis is still not ready to play the Moneyball, because it's really about the availability of the data, which is something that I've been emphasizing, and we all emphasize, no matter how the AI technologies, you know, go forward, the most important thing is to have data, have good quality data.

Without it, this technology cannot do much.

So speaking of, of technology, that the first question I have with both of you is that in the world of the sports analytics, what are the one most transformative methodologies or, or technologies, and are we using them, using it wisely? Mark, maybe you go first.

Sure.

I guess off the top of my head, there are two that really spring to mind.

Uh, so one of them is just the notion of using, uh, detailed tracking data to be able to characterize very, uh, detailed kinds of movements on the pitch or court or, or field that gives just a lot more richness to the kinds of ways of describing how players and teams carry out their, uh, their tactics at a, a fairly precise level.

And then the second is having these very specific models that basically estimate the probability of a team winning at any particular point in the game based on situational factors.

So there are a lot of different procedures and applications to particular sports where you can look at, for example, say in basketball, that, you know, the game, it has fifteen minutes left, and one team is up by a certain margin, and they're playing on the home field.

Having just this very, uh, specific situational aspect of the game and being able to then have a calculation that says, "Here's the probability that the game is gonna be a win." And the reason that's important is that you can use that information, for example, to start evaluating players by who's actually on the court and when they get substituted in.

So you can bas- basically say relative to a player being on the court and then play for a while and then come off the court, you can see how the win probability has changed over that period, and that gives you the basis for being able to evaluate, like how good players are, how valuable they are to their teams.

Steph?

Yeah.

I, I would really echo Mark's points.

I think one of the things that really fascinates me is how the kinds of methodology and, and questions that researchers ask is really driven by what the state-of-the-art is in data.

You'll see that in any field, but within sports analytics it's, it's quite striking when the typical arc in sports analytics has been, you know, beginning with maybe box scores and then being able to do more play-by-play data and then tracking data.

That's, that's sort of the era that we're in, although it's-- we're starting to see some movement to even newer types of data beyond that.

But one of the things that came out, uh, really immediately once More of that data became open was methods like expected possession value.

That was one of the, the kind of big ideas that originally came out of the, the XY Hoops group.

That would have never been possible without having access to that positional data.

But what's also interesting is, is where you still see the fundamentals that preceded it within it, because although EPV requires spatially driven models, it does kind of sit with on what is fundamentally a Markov model.