Pitcher zStats Entering the Homestretch, Part 2 (The Stats!)

One of the strange things about projecting baseball players is that even results themselves are small samples. Full seasons result in specific numbers that have minimal predictive value, such as BABIP for pitchers. The predictive value isn’t literally zero; individual seasons form much of the basis of projections, whether math-y ones like ZiPS or simply our personal opinions on how good a player is. But we have to develop tools that improve our ability to explain some of these stats. It’s not enough to know that the number of home runs allowed by a pitcher is volatile; we need to know how and why pitchers allow homers beyond a general sense of pitching poorly or being Jordan Lyles.

Data like that which StatCast provides gives us the ability to get at what’s more elemental, such as exit velocities and launch angles and the like — things that are in themselves more predictive than their end products (the number of homers). StatCast has its own implementation of this kind of exercise in its various “x” stats. ZiPS uses slightly different models with a similar purpose, which I’ve dubbed zStats. (I’m going to make you guess what the z stands for!) The differences in the models can be significant. For example, when talking about grounders, balls hit directly toward the second base bag became singles 48.7% of the time from 2012 to ’19, with 51.0% outs and 0.2% doubles. But grounders hit 16 degrees to the “left” of the bag only became hits 10.6% of the time over the same stretch, and toward the second base side, it was 9.8%. ZiPS uses data like sprint speed when calculating hitter BABIP, because how fast a player is has an effect on BABIP and extra-base hits.

And why is this important and not just number-spinning? Knowing that changes in walk rates, home run rates, and strikeout rates stabilized far quicker than other stats was an important step forward in player valuation. That’s something that’s useful whether you work for a front office, are a hardcore fan, want to make some fantasy league moves, or even just a regular fan who is rooting for your faves. If we improve our knowledge of the basic molecular structure of a walk or a strikeout, then we can find players who are improving or struggling even more quickly, and provide better answers on why a walk rate or a strikeout rate has changed. This is useful data for me in particular because I obviously do a lot of work with projections, but I’m hoping this type of information is interesting to readers beyond that.

Yesterday, I went over how pitchers zStats for the first two months of the season performed over the last two months. Today, we’ll look at the updated data, through the games on August 10.

Sonny Gray still tops this list, as he did in June, but the rate of “overperformance” has come down a bit. While that discrepancy looks like an alarm siren, especially since he doesn’t have a history of defying the data, he’s been so good that even all those extra homers simply put him in the same neighborhood as last year’s performance, which is just fine.

Zac Gallen’s and Dane Dunning’s corrections have slid them down this list a bit, and Bailey Ober has surfed right off the bottom of it. Justin Steele has been hit a lot harder the last couple of months (84 mph through the end of May, 89 mph since) with results to match, also kicking him off the list, but his stats remain such that he’s still a plausible Cy Young candidate if he has a strong stretch drive. I doubt many would be disgruntled if his true ability were an ERA in the 3.00–3.50 range rather than somewhere around 2.00, which was always unlikely to be sustainable.

Where zStats take away from the Twins, they give back. When there’s a pitcher like Joe Ryan who breaks out and then suddenly struggles, the general inclination of the public is that it was the breakout that was the outlier. This is at least one data point that the good Ryan is closer to real than the one who has allowed nine homers in the last 19 innings. The fact that he had a groin injury he apparently didn’t tell the team about also lends credence to the idea that he’s better than this.

Chris Flexen’s bad run led him first to be a throw-in to the Mets in the Trevor Gott trade and then outright released just a few days later. It’s not a bad notion for a team like the Rockies to take a chance on him, but I’m not sure that’s the ideal offensive environment to fix things. I have to wonder if the Dodgers saw something similar in Lance Lynn to pick him up; they’ve previously acquired a number of players who are on the zHR underachieving lists, most notably Andrew Heaney when most people were calling him toast.

Zack Wheeler has demonstrated a long-term ability to walk fewer batters than you’d expect, so there’s almost no impact on his long-term projections. ZiPS is less convinced about Joel Payamps, as he doesn’t have the same history that Wheeler does. I have to wonder if the Astros have an approach that other teams haven’t figured out yet; they have three pitchers who rank very highly here despite unimpressive first-strike percentages. Usually, this number is a leading indicator of walk rates, but J.P. France, Cristian Javier, and Framber Valdez don’t fit the usual pattern. That’s something I might explore further at the end of the season.

Alek Manoah certainly hasn’t been good since returning to the majors, but at least the walks are more in line with what you’d expect. I still think something is broken, because his collapse has been so thorough — his brief stint in the minors was brutal — and so quick. I don’t think he should be in a major league rotation right now. Shane McClanahan was on this list last time, too, so it’s not like any injury problems were foretold by his presence here. It does mean that ZiPS thinks his walk rate should have been more like last year’s than this year’s still-solid rate, but obviously with a serious injury, we’re not going to see him again this year. Spencer Strider is still here because of that blip back in May.

zStats were pretty confident that Mitch Keller would have a correction, but it’s been way more severe than the model expected, as he shed more than a third of his strikeout rate over the last two months. I feel quite annoyed that I developed a model that keeps being so mean to Kevin Gausman! While his plate discipline-against numbers are fine, they’re not “11 strikeouts per game” good. Amusingly, Gausman has actually increased his strikeout rate since June. Gallen has fallen completely off this list, so there’s not a lot of hot air left in his numbers to ventilate.

It’s surprising to see McClanahan keep turning up in these underperforming lists considering he was already having an excellent season. zSO definitely sees some upside remaining for Patrick Sandoval, who has extremely unimpressive strikeout numbers for a pitcher with his peripheral numbers. Shane Bieber’s strikeout rate did in fact jump to acceptable levels in June, but the progress he was making in this department was cut off by elbow injury which has kept him out for the last month. He’s still an interesting target for a trade this offseason, but coming off an injury, Cleveland may be less willing to risk selling low.

I hate to keep coming back to Gausman, but he keeps showing up at the top of the list. What especially intrigues me is that early in his career, he was a habitual underperformer of these metrics (and FIP):

The good news here generally is that only a handful of these numbers really change how you would evaluate a pitcher. You don’t get to a zFIP in the fours until you get to Lucas Erceg, and even then, with an ERA over six, zStats are basically just telling you not to trust the FIP as much as you might otherwise.

Graham Ashcraft has pitched better of late; what’s confused ZiPS is his struggles to finish off batters for that third strike. He has improved somewhat in that department recently and only needed 5.1 innings a few starts ago to set his seasonal whiff high at eight. ZiPS still think there’s upside here. The model here is convinced that Sandy Alcantara has gone full circle and may now be underrated, and it still holds out hope that Aaron Nola is a lot better than he’s shown this season.

Hopefully, you find all this data useful. If you have any questions or comments about different types of things you might want to see in these reports, please let me know. They’re fairly difficult to automate, but if people beyond me find them interesting and/or useful, I’d like to get at least monthly updates into the leaderboards somewhere rather than the less efficient method of including them in articles.