ZiPS zStats for Pitchers at the Midpoint

The emergence of Statcast (and similar types of tracking data) over the last decade-plus has revolutionized many parts of baseball analysis. A big category that didn’t really exist prior was the notion of “expected” stats. Up until then, numbers were all tallies of results, and proto-expected metrics, like Bill James’ Component ERA, were derived from the classical array of stats. But tracking data opened up new opportunities in this area, allowing us to more closely look at home runs and strikeouts, and see the underlying processes and skills that made those results. While the past is always the past, expected stats are useful when talking about the future.
As someone who made the odd decision to work with baseball projections for half his life, I have a vested interest in finding the best use of this kind of information when predicting the future. Like the Statcast estimates (preceded with an x, as in xBA, xSLG, etc.), ZiPS has its own version, very creatively using a z instead. zStats do have some correlation with xStats, but not a perfect one, as ZiPS uses things like spray data, sprint speed, and plate discipline metrics in its estimates.
It’s important to remember these aren’t predictions in themselves. ZiPS certainly doesn’t just look at a pitcher’s zSO from the last year and say, “Cool, brah, we’ll just go with that.” But the data contextualize how events come to pass and are more stable than the actual stats are for individual players. That allows the model to shade the projections in one direction or the other. Sometimes that’s extremely important, such as in the case of homers allowed for pitchers. Of the fielding-neutral stats, homers are easily the most volatile, and home run estimators for pitchers are much more predictive of future homers than are actual homers allowed. Also, the longer a player “underachieves” or “overachieves” in a specific stat, the more ZiPS believes the actual performance rather than the expected one. Call this the Rule of Isaac Paredes, in honor of a player who constantly stymies zHR. In some ways, we’re projecting how cruel regression toward the mean will be.
More information on accuracy and construction can be found here.
As with the hitters, the best place to start is checking in on some of last year’s overachievers and underachievers. Read the rest of this entry »






