Checking Out 2022 zStats for Pitchers After Two Months of Play

Hunter Greene
The Enquirer

As anyone who does a lot of work with projections could likely tell you, one of the most annoying things about modeling future performance is that results themselves are a small sample size. Individual seasons, even full ones over 162 games, still feature results that are not very predictive, such as a hitter or a pitcher with a BABIP low or high enough to be practically unsustainable. For example, if Luis Arraez finishes the season hitting .350, we don’t actually know that a median projection of .350 was the correct projection going into the season. There’s no divine baseball exchequer to swoop in and let you know if he was “actually” a .350 hitter who did what he was supposed to, a .320 hitter who got lucky, or a .380 hitter who suffered misfortune. If you flip heads on a coin eight times out of 10 and have no reason to believe you have a special coin-flipping ability, you’ll eventually see the split approach 50/50 given a sufficiently large number of coin flips. Convergence in probability is a fairly large academic area that we thankfully do not need to go into here. But for most things in baseball, you never actually get enough coin flips to see this happen. The boundaries of a season are quite strict.

What does this have to do with projections? This volatile data becomes the source of future predictions, and one of the things done in projections is to find things that are not only as predictive as the ordinary stats, but also more predictive based on fewer plate appearances or batters faced. Imagine, for example, if body mass index was a wonderful predictor of isolated power. It would be a highly useful one, as changes to it over the course of a season are bound to be rather small. The underlying reasons for performance tend to be more stable than the results, which is why ERA is more volatile than strikeout rate, and why strikeout rate is more volatile than the plate discipline stats that result in strikeout rate.

MLB’s own method comes with an x before the stat, whereas what ZiPS uses internally has a z. (I’ll let you guess what it stands for!) I’ve written more about this stuff in various other places (like here and here), so let’s get right to the data for the first two months of the major league season. We posted the leaderboards for the hitters yesterday, so let’s finish up with the pitchers today, starting with the home run overachievers:

zHR Overachievers
Name HR% HR zHR% zHR zHR% Diff zHR Diff
Jose Quintana 0.9% 2 2.7% 6.2 -1.8% -4.2
Merrill Kelly 1.1% 3 2.5% 6.8 -1.4% -3.8
Kevin Gausman 0.8% 2 2.1% 5.7 -1.4% -3.7
MacKenzie Gore 0.5% 1 2.4% 4.6 -1.9% -3.6
A.J. Minter 0.0% 0 4.0% 3.6 -4.0% -3.6
Alex Colomé 0.0% 0 3.7% 3.3 -3.7% -3.3
Bailey Ober 1.4% 2 3.7% 5.2 -2.3% -3.2
Shane Bieber 1.5% 4 2.8% 7.2 -1.2% -3.2
Patrick Sandoval 0.0% 0 1.5% 3.2 -1.5% -3.2
Joel Payamps 0.0% 0 3.1% 3.1 -3.1% -3.1
Michael Kopech 1.0% 2 2.5% 4.9 -1.5% -2.9
Kyle Wright 1.1% 3 2.2% 5.8 -1.0% -2.8
Zac Gallen 1.8% 4 3.0% 6.6 -1.2% -2.6
Carlos Carrasco 1.1% 3 2.1% 5.6 -1.0% -2.6
Taylor Hearn 3.0% 7 4.1% 9.6 -1.1% -2.6

zHR clearly isn’t buying Jose Quintana only allowing two home runs in his sorta-comeback season for the Pirates. While he does have a solid average exit velocity, he walks a very fine line with his low-90s fastball and makes the occasional mistake. This year, most of those mistakes have stayed in the park. Of the 10 batted balls against him this year with an estimated xSLG above 2.000, only four have been hits, with just one ending up a round-tripper. Those aren’t going to continue to all go to the deepest parts of the park. On a HR-rate basis, A.J. Minter stands out as the biggest overachiever, with a 13.7% barrel rate, netting not a single homer.

What about the underachievers?

zHR Underachievers
Name HR% HR zHR% zHR zHR% Diff zHR Diff
Elieser Hernandez 8.3% 18 5.4% 11.8 2.8% 6.2
Hunter Greene 6.2% 15 3.7% 9.0 2.5% 6.0
Nathan Eovaldi 5.7% 16 3.7% 10.3 2.1% 5.7
Germán Márquez 4.3% 12 2.6% 7.2 1.7% 4.8
Shane McClanahan 3.2% 8 1.5% 3.8 1.7% 4.2
Matt Bush 5.3% 5 1.3% 1.3 4.0% 3.7
Caleb Smith 7.0% 8 3.8% 4.4 3.2% 3.6
Beau Brieske 6.7% 12 4.8% 8.6 1.9% 3.4
Marcus Stroman 4.0% 8 2.4% 4.8 1.6% 3.2
Kyle Bradish 6.0% 10 4.1% 6.8 1.9% 3.2
Aaron Sanchez 4.3% 6 2.1% 3.0 2.2% 3.0
Zack Littell 5.6% 5 2.3% 2.1 3.3% 2.9
Robbie Ray 4.7% 14 3.7% 11.1 1.0% 2.9
Marco Gonzales 4.4% 11 3.3% 8.1 1.2% 2.9
Lucas Giolito 4.9% 10 3.5% 7.1 1.4% 2.9

I have to wonder if Hunter Greene’s presence here is a by-product of his lack of experience in the high minors, the result of a lot of missed time due to injury. Fifteen homers is quite a lot for a pitcher with his stuff, and he’s not hit particularly hard overall. If anything, these numbers resemble those of the biggest HR underachiever of 2019, Corbin Burnes, who really befuddled zHR with his 17 homers in just 49 innings. ZiPS didn’t buy those numbers when it came to Burnes, and when it comes to future projections, ZiPS is going to be pretty forgiving with Greene.

Shane McClanahan here feels almost greedy given that he’s fifth in baseball for pitcher WAR. Like Luis Castillo last year at a similar point in the season, Germán Márquez has greatly underperformed his recent history while the zStats for him see little actual change in how he’s pitching. Coors is always a problem, of course, but I’d definitely be interested in seeing if someone’s willing to sell Márquez low in my fantasy league.

Now let’s look at the walk rate over and underachievers:

zBB Overachievers
Name BB% BB zBB% zBB zBB% Diff zBB Diff
Corbin Burnes 4.9% 13 7.9% 21.1 -3.0% -8.1
Antonio Senzatela 4.8% 9 8.4% 15.9 -3.7% -6.9
George Kirby 2.3% 3 7.3% 9.5 -5.0% -6.5
Jameson Taillon 2.4% 6 4.9% 12.4 -2.5% -6.4
Daulton Jefferies 4.7% 8 7.9% 13.6 -3.3% -5.6
Paul Blackburn 6.0% 14 8.3% 19.4 -2.3% -5.4
Aaron Nola 3.4% 10 5.3% 15.4 -1.9% -5.4
Craig Stammen 1.2% 1 7.5% 6.3 -6.3% -5.3
Yimi Garcia 5.7% 5 11.6% 10.3 -6.0% -5.3
Evan Phillips 6.7% 6 12.4% 11.1 -5.7% -5.1
Bryan Baker 7.8% 8 12.6% 13.0 -4.8% -5.0
Seth Lugo 5.0% 5 9.7% 9.8 -4.8% -4.8
Cristian Javier 8.7% 16 11.3% 20.8 -2.6% -4.8
Hunter Greene 10.0% 24 11.9% 28.6 -1.9% -4.6
Taylor Clarke 1.1% 1 6.0% 5.6 -4.9% -4.6

Don’t be alarmed by Burnes checking in at the top of the list. Yes, his walk rate is much lower than you’d expect from the various plate discipline stats, especially his extremely underwhelming first-strike percentage, which is a strong leading indicator of future walk rate. But that doesn’t mean there’s an actual problem here, as Burnes has a history of bettering his expected walk rate, something that ZiPS knows when it drools over his numbers when making a projection. It’s at least interesting that he’s been demonstrating a repeatable skill of rarely allowing walks despite more 1–0 counts than the average pitcher. Among the plate discipline stats, out-of-zone swing percentage is also a leading indicator, and a few of the names here, such as Bryan Baker and Seth Lugo, are also at the bottom of the league in that number. I’m especially concerned about Lugo, as this is a significant shift.

Now the underachievers:

zBB Underachievers
Name BB% BB zBB% zBB zBB% Diff zBB Diff
Merrill Kelly 9.9% 27 6.5% 17.8 3.4% 9.2
Joan Adon 13.2% 35 10.0% 26.5 3.2% 8.5
Sean Manaea 8.7% 24 6.0% 16.5 2.7% 7.5
Nick Martinez 10.5% 22 7.0% 14.7 3.5% 7.3
Framber Valdez 8.9% 25 6.4% 17.9 2.6% 7.1
Taylor Hearn 10.8% 25 7.7% 17.9 3.0% 7.1
Adam Wainwright 8.2% 22 5.6% 15.0 2.6% 7.0
Hunter Strickland 16.5% 17 9.9% 10.2 6.6% 6.8
Yusei Kikuchi 13.5% 28 10.5% 21.6 3.1% 6.4
Dillon Peters 15.4% 16 9.3% 9.7 6.1% 6.3
Spencer Strider 13.5% 18 8.9% 11.8 4.6% 6.2
Cal Quantrill 8.1% 20 5.7% 14.1 2.4% 5.9
Dylan Cease 12.0% 30 9.7% 24.2 2.3% 5.8
Daniel Lynch 9.9% 22 7.4% 16.5 2.5% 5.5
Aaron Ashby 11.2% 22 8.4% 16.6 2.7% 5.4

Dylan Cease has been a bit walk-riffic lately, giving out 10 free passes in June. Now, he was the victim of one of the most abysmal calls you’ll ever see, but he can’t blame all of his walk rate on that! Given his velocity, contact rates, and the rate at which hitters mistakenly assume it’s a good idea to chase his knuckle-curve, I wouldn’t be worried, at least not yet. Merrill Kelly appears both here and on the home run overachievers list to the extent that they just about cancel each other out. ZiPS certainly hopes that the Braves don’t use Spencer Strider’s walk rate as a reason to move him into shorter stints; despite a rather low first-strike percentage, Strider’s contact/swing numbers have convinced the computer that several more plate appearances should have been resolved before ball four.

And now for the strikeout rate over and underachievers.

zSO Overachievers
Name SO% SO zSO% zSO zSO% Diff zSO Diff
Nestor Cortes 28.6% 71 21.8% 54.1 6.8% 16.9
Rony García 30.0% 33 17.8% 19.6 12.2% 13.4
Cristian Javier 30.4% 56 23.7% 43.6 6.8% 12.4
Frankie Montas 27.9% 78 23.5% 65.7 4.4% 12.3
Austin Gomber 17.9% 40 12.6% 28.1 5.3% 11.9
Aaron Nola 29.3% 85 25.2% 73.1 4.1% 11.9
Justin Verlander 27.0% 73 22.7% 61.4 4.3% 11.6
MacKenzie Gore 30.0% 57 24.5% 46.6 5.5% 10.4
Eric Lauer 27.7% 65 23.4% 55.0 4.2% 10.0
Joan Adon 16.5% 44 13.1% 34.9 3.4% 9.1
Yusei Kikuchi 25.1% 52 20.8% 43.0 4.3% 9.0
Carlos Rodón 30.2% 75 26.6% 66.1 3.6% 8.9
Eli Morgan 35.1% 34 25.9% 25.2 9.1% 8.8
Robert Suarez 30.9% 29 21.5% 20.2 9.4% 8.8
Emmanuel Clase 29.7% 27 20.2% 18.3 9.5% 8.7

ZiPS is clearly not fully buying the Nestor Cortes story quite yet. For a pitcher who doesn’t throw hard at all and is very ordinary at getting batters to swing through pitches, he has a lot of strikeouts. That said, ZiPS would have taken awhile to believe Tom Glavine, too, so there’s certainly a lot of hope here. Rony García is baseball’s biggest strikeout rate overachiever, a result of mediocre contact rates. Thirteen “extra” strikeouts in 28 innings is an enormous number. While I think there’s a real shot that Cortes can continue to outperform his expected strikeouts, García’s history of being able to maintain good strikeout rates is much shorter; he has a lower career strikeout rate in the minors than in the majors.

And finally, the underachievers:

zSO Underachievers
Name SO% SO zSO% zSO zSO% Diff zSO Diff
Jordan Montgomery 19.4% 46 25.9% 61.4 -6.5% -15.4
Yu Darvish 20.1% 54 25.8% 69.3 -5.7% -15.3
Carlos Hernandez 10.7% 16 19.7% 29.4 -9.0% -13.4
Tyler Wells 15.3% 29 22.1% 42.0 -6.8% -13.0
Noah Syndergaard 15.4% 30 21.6% 42.1 -6.2% -12.1
Chad Kuhl 18.2% 41 22.8% 51.4 -4.6% -10.4
Zack Greinke 11.2% 25 15.8% 35.2 -4.6% -10.2
Kyle Hendricks 14.8% 37 18.8% 47.0 -4.0% -10.0
Paul Blackburn 18.9% 44 23.0% 53.5 -4.1% -9.5
Dany Jiménez 24.2% 22 34.4% 31.3 -10.2% -9.3
Spenser Watkins 10.0% 14 16.5% 23.0 -6.5% -9.0
Taijuan Walker 12.9% 25 16.9% 32.7 -4.0% -7.7
Griffin Jax 26.1% 30 32.5% 37.4 -6.4% -7.4
Steven Wilson 22.6% 19 31.3% 26.3 -8.7% -7.3
Domingo Acevedo 20.6% 22 27.2% 29.1 -6.6% -7.1

It’s interesting to see Noah Syndergaard on the underachievers list. He’s looked relatively unimpressive by his standards when I’ve seen him pitch this year, but it’s hard to eliminate all bias when you know that he’s missing a lot of his normal velocity and actually has a very low strikeout rate. I wonder if he’s still trying to figure out how to finish off batters with a lot of his explosiveness gone. Batters are hitting .262 against him in 0–2 counts, and he’s struck out barely a quarter of the batters against whom he’s gone ahead 0–2 (11 out of 43, or 25.6%). Just to put that 25.6% into context, he was at 51.2% coming into this season. Dany Jiménez may be the most interesting inclusion on this list. His contact rate of about 65% is elite, thanks to hitters continually getting fooled by his curve. Obviously, you wouldn’t expect him to quite match his nearly 15 K/9 from the minors last year, but ZiPS sees a lot more strikeouts here.





Dan Szymborski is a senior writer for FanGraphs and the developer of the ZiPS projection system. He was a writer for ESPN.com from 2010-2018, a regular guest on a number of radio shows and podcasts, and a voting BBWAA member. He also maintains a terrible Twitter account at @DSzymborski.

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jbgocubsmember
5 months ago

hypothetically, if a pitcher had showed up on all three overachiever lists, what percent of you would think he’s a lucky boy v. what percent of you would think there’s something the model is flat out missing with this guy?

jfree
5 months ago
Reply to  jbgocubs

That depends if he’s on your team or a team that Z hates (and therefore deliberately screws up the model in order to mess with fellow fans).