Sunday, December 04, 2022
Spray Angle overfits xwOBA
I'm always interested in saber research, constantly seeking it out. The latest one I found is on reddit called 3D wOBA. First off: what a great name! I wish I would have thought of it.
Anyway, the researcher notes that xwOBA is principally driven by speed and angle, and notes the lack of use of the Spray Angle. Which is true. And intentional.
As I've said many times in the past: it's a question of describing the PLAY or the PLAYER. Why is FIP popular? Because it describes the PLAYER. Doesn't giving up 0 hits in a game mean it's a great game? Yes. But, does it mean it's a great PITCHER? No, not necessarily. That's because hits that stay in the park are subject to a great deal of random variation having nothing to do with the pitcher himself. You have the fielders, the fielding alignment, and the park. Not to mention most pitchers are similar in BABIP talent that it requires a GREAT deal of batted balls to find the signal amongst all that noise.
Anyway, back to the matter at hand. The research very (very) helpfully provided the data. And so, it took just a couple of minutes for me to do the test I needed to do: compare wOBAcon, xwOBAcon, and 3DwOBAcon to NEXT SEASON'S wOBAcon. Why do we want to do that? Because that data is unbiased. It's describing the PLAYER. And that is what I care about. And really, when you think about it, most of the time, that's what you care about too.
Anyway, here are the correlations. A straight wOBAcon to wOBAcon correlation is an r=0.55 (the sample had an average of 359 batted balls). This gives us a ballast value (the regression amount) of 293 batted balls.
How about the xwOBAcon from Savant (as shown in their spreadsheet anyway)? That's a correlation of r=0.56. We learn a little bit more, but not much more, than just using their actual performance. But at least, directionally, it's where we want it.
Now, finally the superbly named 3DwOBAcon, how did it do? Correlation of r=0.52. Wait, it's LOWER? Yes, it is. And this is very typical when you overfit your data. You are so focused on trying to explain THAT PLAY that you ignore what really matters here: the players themselves.
When you run a correlation to same-plays, the Savant xwOBAcon has a correlation of r=0.83 while 3DwOBAcon is slightly higher at r=0.85. The good thing here is that their model, as they've wanted to tune it, works fine. The extra dimension, the spray angle, does in fact better help describe the play in question.
But, from the PLAYER perspective, the spray angle is mostly noise. And so when you use that information as a critical component to describe the player TALENT, and so, be able to predict next season, you are introduce noise for that prediction. It's like trying to use ERA to explain ERA next season instead of FIP. Or use win% to predict next season's win% instead of using ERA to predict next season's win%.
And this is why, by and large, we don't use BABIP to evaluate pitchers. And this is why, by and large, we don't use spray angles to evaluate batters.
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