Saturday, November 23, 2024
Layered wOBAcon, for Pitchers
Having layered out the process and results for batters, we can now turn our attention to pitchers.
Now, the news is not going to be great. It'll be good, in that this is an advance. But any expectation that we'd uncover anything big would have been highly misguided.
First, we start with our two baselines. First, how well does wOBAcon (year T) predict wOBAcon (year T+1)? The year to year correlation (minimum 100 batted balls, average of 264) is an r=.08. This sets the ballast (regression toward the mean amount) to a whoppping 3000 batted balls. In other words, once a pitcher accumulates 3000 batted balls (which is 5 or 6 full seasons for a starting pitcher), his observed wOBAcon represents about half the pitcher talent and half Random Variation. This is what we are up against when it comes to batted balls. This is why DIPS exists, and this is why FIP (which is a measure that ignores most batted balls, only keeping HR) has staying power.
How does xwOBAcon do, which you can already see on the Savant player pages and in Search? Remember that xwOBAcon is an estimate of the PLAY, looking at the combination of launch angle and speed, with combination being the key word. In that case, the r=.11, which is a slight gain over just using wOBAcon. The ballast is 2000 batted balls. That's actually pretty good, as now we're down to needing 3 or 4 full seasons for a starting pitcher to find half of their true talent.
Let's now go to each layer, starting with layer of Launch Speed. Now, remember, we are not looking at Launch Speed itself, but the translation of Launch Speed into wOBA. It is not 1:1. Low launch speeds, those under 88 mph, are all equivalent. And launch speeds over 100 mph get progressively more impactful (up to a point). All that work behind the scenes gives us a wOBA_layer based strictly on Launch Speed. And what is the year to year correlation of Lauch Speed Layer (year T) to wOBAcon (year T+1)? That is ALSO an r=.11. That's the same as good ole xwOBAcon. Remember, xwOBAcon uses both launch speed and launch angle, in unison, to describe the PLAY. And it obviously does that better than the Launch Speed Layer on its own. But to describe the PLAYER? Well, as we can see by the results, the Launch Angle is just about entirely Random Variation (when used in combination as xwOBAcon does).
When we use ALL the layers, our correlation jumps to r=.16, and our ballast drops all the way down to 1400 PA, meaning we need fewer than 3 full seasons for a starting pitcher to find half their talent. The half-full glass view is that we've drastically improved from needing 5 to 6 seasons to now only needing half as many. The half-empty view is that we still need almost 3 seasons, when we'd really like to have only 1.
Let's go through it layer by layer. Layer for Launch Angle is half the impact of that of Launch Speed, and is basically what causes it to go from r=.11 to r=.16. The difference between the layered approach and the xwOBAcon combo approach is that we are better able to isolate launch angle from launch speed.
As for the other layers, all their p-values are quite high, making them all pretty meaningless. But let's go through them anyway. The batter run speed actually has a negative correlation, but with a p-value of .36, it really could just as well be 0. The fielding aligment has a p-value of .48, and a slightly positive correlation. So again here, the fielding alignment doesn't really carry over in predictability. The spray angle has a slightly negative correlation and an even higher p-value (.62). The Fielder Performance layer is slightly positive, but at a p-value of .93, well, we can easily ignore it. And the Carry Layer at a p-value of .94, and a coefficient of almost 0, it's as meaningless as it gets.
So there you have it, after all is said and done, the two things we care about, Launch Angle and Speed are the two things already in xwOBAcon since its inception. The paradigm shift is to layer them to better isolate them, rather than combine them from the outset. And it gives us a half-glass impact in evaluating pitchers.
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