Um, and you, with AI now capable of sort of ingesting decades of this granular match data, can a principled probabilistic model still compete with the raw pattern recognition power of a neural network? And are, you know, are we gonna be approaching a threshold where interpretability simply loses accuracy? Do classical probabilistic rating systems still hold their own against neural networks, or are they going to be approaching, uh, obsolescence?

I haven't seen, like, any, uh, any, like, serious machine learning AI attempts at rating competitors in organizations using the kind of data that, uh, is typically used in rating systems.

The data that's usually, um, used by, uh, rating systems is just simply wins and losses and just who the players or, or teams are that are competing.

So the data itself is actually very simple, and at least my experience has been that in such a simple setting, it's actually hard to, like, very seriously improve on methodology that is reasonably classical as opposed to machine learning, uh, because just the, you know, variation in the inputs is so constrained.

And I think what also, uh, makes an extra piece of it even more constrained is that in most of these kinds of settings for analyzing game outcomes, there's an assumption that the team or player abilities are changing over time.

So if there's any role for more high-powered kinds of analytic techniques, it's really gonna be in the tracking of abilities as they change over time.

But fundamentally, all of that's coming from the game outcomes, and the game outcomes is such a simple structure that my sense is that they'll probably, at least for a pretty long while, still be a role for the kinds of rating systems that are currently in use.

I- is there a risk, though, that every franchise optimizing from the same models creates some kind of strategic monoculture? Um, you know, and who in the room, the coach or the algorithm, should have the final word? So I, I, I guess really it's like where's, where's the line between data-informed decision-making and over-automation? And what happens, uh, when every team sort of converges on the same model?