Brian Fluharty-Imagn Images

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 hitter “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 usual, let’s start with a quick look at how last season’s midseason update fared. All 20 of the OPS overachievers last year received at least 150 plate appearances over the rest of the season (RoS). All but one of them had a RoS OPS worse than their actual OPS up to that point, with their cumulative second halves 106 points of OPS lower than in the first half.

The underachievers got fewer plate appearances the rest of the way than the overachievers did, an unsurprising result since underachievers tend to lose playing time. All but one of the 20 biggest underachievers still got at least another 100 plate appearances (LaMonte Wade Jr. was the exception), and 16 of the 20 improved, with a collective second-half OPS that was 66 points better than in the first half.

But let’s get to the 2026 numbers, updated through Tuesday morning.

Mickey Moniak has a 136 wRC+ for the Rockies, and the zStats aren’t really having any of that with him. Even though the numbers take into account that he plays in Coors Field, ZiPS thinks that he’s outperforming in BABIP by about 40 points, and wants to lop off about a third of his home runs. That he’s pulling a lot of balls in the air is very good, but he hasn’t hit a lot of line drives, and his hard-hit data isn’t impressive for a player with his homer total. Otto Lopez is having MVP-type numbers, which I don’t think anyone expected, and ZiPS thinks BABIP is a big culprit here, as well. Juan Soto, Pete Crow-Armstrong, and Junior Caminero all make this list, but ZiPS sees it as a disagreement into the exact degree of their excellence, rather than a dispute of their excellence. All three easily remain stars if you take a bit of helium out of their numbers.

If you’re confused by the season of Jackson Merrill, ZiPS is just as befuddled. His contract and track record ensure that he’ll get quite a lot of opportunity to bounce back. Despite real improvements in his bat speed and hard-hit rate, without worsening his plate discipline, Merrill’s stats have fallen through the floor this season. Otherwise, there are no big surprises here, except perhaps Brandon Nimmo. His stats don’t look all that different from last year’s, and he’s at an age where decline is likely at any time, but ZiPS, like Statcast, thinks that he ought to be in the middle of a pretty good comeback season, not a league-averageish one.

You didn’t really think that Garrett Mitchell was going to stay a .400 BABIP hitter, did you? Riley Greene’s back on track after an over-aggressive year at the plate in 2025, but he still has a fairly low contact rate for a hitter hanging onto a batting average in the .290s.

The thing about very low BABIPs is that there’s a real floor to them. Pitchers, when hitting and swinging away (so excluding bunts) from 2010-2019, collectively had a .232 BABIP. So realistically, it’s hard for half-competent MLB hitters to be that far below that mark for long periods of time, even if they’d struggle to beat a glacier in a foot race. Austin Wells shouldn’t be having a good year for the Yankees, with his overall zStats at a .206/.274/.323 triple-slash line, but it’s hard to believe that the balls he hits are easier to field than those hit by Randy Johnson, whose form at the plate looks like Jaws (the Bond villain, not the shark) with a mullet and a couple herniated discs. Dansby Swanson gets some batting average back as well, though as you’ll see in a minute, when ZiPS giveth, it sometimes also taketh.

Sorry, Dansby, ZiPS is going to take away some of your home runs in return for the singles and doubles it’s giving you. While ZiPS is prepared to think that Luis García Jr. is having a massive breakout season, with a zStats triple-slash line of .297/.337/.509, it’s not yet prepared to see him as someone who will fight to finish with 40 homers. ZiPS also casts a bit of shade on the home run totals of Kyle Schwarber and Junior Caminero, but only a bit, with their zHR still ranking third and sixth in the majors, respectively. Now, I’d be pretty happy to see Schwarber hit 60 homers, personally, as I think his Fun Above Replacement value has always outpaced his WAR, and because by old-timey baseball conventional wisdom, he’s the exact opposite of the platonic ideal of a two-hole hitter. What’s interesting is that James Wood doesn’t appear here at all; ZiPS thinks he should have two more home runs than his actual total of 24.

As mentioned above, ZiPS thinks Nimmo ought to be having a much better season than he actually is, with a profile that should have led to a lot more homers this year. Maybe I need to start watching more Rangers games? While Vladimir Guerrero Jr. and Fernando Tatis Jr. shouldn’t be having good homer years, ZiPS thinks both are well below where they should be given the tracking data. Vladito’s performance this season by his zStats triple-slash of .294/.366/.444 is a lot less disappointing than his actual line of .263/.347/.346, and the same is true for Tatis, whose .286/.355/.444 zStats line is much better than his real-life line of .282/.343/.382. I should note that Guerrero has dropped just below 400 career homers in his long-term ZiPS projection for the first time in several years.

While projections that use zBB and zSO for hitters are better than projections that do not, these numbers are far less crucial than they are for pitchers, so they’re mostly here for informational purposes. The r^2 for zBB% vs. BB% is just under 0.7, and for zSO% vs. SO%, a hair under 0.9, but the actual numbers are also very predictive in their own right, which reduces the relative value of these expected numbers in determining future performance. But they’re at least interesting, and though I’m not yet satisfied the model is ready for primetime, swing data is actually moderately helpful in improving these models.

Up next, the pitchers, where zBB and zSO really get their time to shine.