Checking in on ZiPS zStats for Pitchers at the Halfway Mark

Kareem Elgazzar/The Enquirer/USA TODAY NETWORK via Imagn Images

Love ’em or hate ’em, the class of “expected” stats has utility when we’re talking about predicting the future. The data certainly inspire mixed feelings among fans, but they perform an important task of linking the things that Statcast and similar non-traditional metrics say to performance on the field. A hard-hit rate of X% or a launch angle of Y degrees doesn’t really mean anything by itself, without the context of what’s happens in baseball games.

I’ve been doing projections now for nearly half (!) my life, so outside of my normal curiosity, I have a vested interest in using this kind of information productively in projections. Like the Statcast estimates (preceded with an “x,” as in xBA, xSLG, etc.), ZiPS has its own version, very creatively using a “z” instead.

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, as in the case of home runs allowed for pitchers. Of the fielding-neutral stats, home runs are easily the most volatile, and home run estimators for pitchers are much more predictive of future home runs allowed than are actual home runs allowed are. Also, the longer a pitcher “underachieves” or “overachieves” in a specific stat, the more ZiPS believes in the actual performance rather than the expected one. More information on accuracy and construction can be found here.

As we did with hitters yesterday, let’s start with a quick look at how last season’s pitching overachievers and underachievers through June performed on the mound over the rest of the season. Again, please note that these aren’t projections themselves, but rather indicators of performance that assist in making projections:

2024 FIP Overachievers Through June 13
Name FIP zFIP zFIP Diff RoS FIP
Luis L. Ortiz 3.49 4.72 -1.23 4.54
Albert Suárez 2.79 3.91 -1.12 4.95
Kevin Gausman 3.39 4.35 -0.95 4.00
Dylan Cease 3.03 3.98 -0.95 3.14
Mitch Spence 3.17 4.09 -0.92 4.74
Cristopher Sánchez 2.58 3.46 -0.88 3.25
Sean Manaea 3.79 4.62 -0.83 3.84
Matt Waldron 3.26 4.06 -0.80 5.13
Jon Gray 2.67 3.41 -0.74 5.27
Logan Webb 2.74 3.48 -0.74 3.10
Cole Irvin 3.64 4.38 -0.73 6.31
Victor Vodnik 3.87 4.53 -0.66 4.49
Cole Ragans 2.31 2.96 -0.65 3.48
Sonny Gray 2.55 3.18 -0.62 3.51
Luis Gil 3.06 3.67 -0.61 5.18
Freddy Peralta 3.52 4.12 -0.60 4.58
Simeon Woods Richardson 3.54 4.14 -0.59 4.46
Ranger Suárez 2.62 3.21 -0.59 4.18

Of the 19 biggest FIP overachievers according to zFIP — I was apparently unable to count to 20 when making the chart — 18 managed at least 30 innings over the remaining 2024 schedule. Trevor Williams, the biggest overachiever, went on the injured list a few weeks later with a flexor strain that ended his season. All 18 had a higher FIP after June 13. The RMSE (root mean squared error) between FIP through June 13 and rest-of-season FIP was 1.46, while for zFIP vs. rest-of-season FIP it was 0.93. In other words, zFIP did about 60% better at projecting FIP for the rest of the season than actual FIP did for the overachievers. Remember, there’s no projection data or regression to the mean built in to “help” zFIP, which is solely derived from the Statcast and similar types of data through a particular date. Let’s look at last year’s FIP underachievers:

2024 FIP Underachievers Through June 13
Name FIP zFIP zFIP Diff RoS FIP
Kyle Hendricks 6.37 4.06 2.31 4.34
Brayan Bello 4.64 3.17 1.47 3.93
Hunter Brown 4.86 3.40 1.46 2.85
Logan Allen 5.50 4.24 1.26 6.56
José Berríos 4.57 3.42 1.15 4.82
Tyler Alexander 5.28 4.16 1.12 4.83
Nathan Eovaldi 3.47 2.49 0.97 4.01
Griffin Canning 5.14 4.19 0.96 5.32
Tobias Myers 5.04 4.09 0.96 3.43
Aaron Civale 4.61 3.69 0.92 4.83
Jared Jones 3.75 2.84 0.91 4.37
Joe Musgrove 5.34 4.45 0.89 2.59
Michael King 4.16 3.27 0.89 2.56
Frankie Montas 4.67 3.80 0.86 4.72
Slade Cecconi 5.99 5.19 0.80 3.69
Justin Verlander 4.97 4.20 0.77 4.43
Kenta Maeda 5.65 4.89 0.76 4.50
Carlos Carrasco 4.98 4.23 0.75 4.86

For the 18 underachievers with at least 30 innings over the rest of the season, zFIP won by a smaller margin, with an RMSE of 1.16 vs. 1.30 for FIP.

zFIP working better with overachievers than underachievers appears to be a feature specific to 2024 rather than a consistent characteristic of the model; with a half-season of data, zFIP is usually 30-40% more accurate than FIP at projecting future FIP.

Let’s start the 2025 numbers off with zFIP underachievers and overachievers, based on data through June 29. I’m using 40 innings pitched as a cutoff point here:

2025 FIP Underachievers Through June 29
Name FIP zFIP zFIP Diff
Bowden Francis 6.81 4.48 2.33
Tanner Houck 6.11 3.85 2.26
Keider Montero 5.43 3.63 1.80
Emerson Hancock 5.67 3.92 1.74
Jameson Taillon 5.16 3.66 1.51
Walker Buehler 6.03 4.61 1.41
Osvaldo Bido 6.52 5.11 1.41
Aaron Nola 5.02 3.66 1.36
Zach Eflin 5.72 4.54 1.17
Jack Kochanowicz 5.54 4.38 1.16
Bryse Wilson 6.44 5.32 1.12
Bailey Ober 5.29 4.18 1.12
Ryan Yarbrough 4.69 3.68 1.02
Scott Blewett 5.26 4.25 1.01
Abner Uribe 2.91 1.95 0.96
Hunter Greene 3.40 2.45 0.95
Tyler Phillips 4.77 3.82 0.95
Kyle Hendricks 4.88 4.00 0.87
Jose A. Ferrer 3.36 2.52 0.84
Ben Brown 4.09 3.26 0.84

2025 FIP Overachievers Through June 29
Name FIP zFIP zFIP Diff
Hoby Milner 2.03 4.18 -2.15
Michael King 3.21 4.64 -1.43
Joe Ryan 3.22 4.39 -1.17
Brenan Hanifee 3.21 4.35 -1.14
Brent Suter 3.79 4.82 -1.03
José Buttó 3.34 4.36 -1.02
Max Fried 2.75 3.73 -0.97
Nathan Eovaldi 2.40 3.33 -0.93
Brady Singer 4.23 5.06 -0.83
Garrett Whitlock 2.89 3.72 -0.83
Cade Povich 4.11 4.90 -0.79
Simeon Woods Richardson 4.32 5.08 -0.76
Hunter Brown 2.69 3.44 -0.75
Kodai Senga 3.20 3.94 -0.75
Cole Ragans 2.41 3.15 -0.74
Ranger Suárez 2.88 3.62 -0.74
Nick Pivetta 3.26 4.00 -0.74
MacKenzie Gore 2.92 3.64 -0.71
Garrett Crochet 2.54 3.25 -0.71
Chad Patrick 3.42 4.12 -0.70

zFIP doesn’t completely salvage a poor showing by Bowden Francis, but it brings him to the point of being a moderately useful innings-eater, at least when his shoulder is better. Walker Buehler appearing here is interesting, because I’ve gotten a lot of commentary in my chats over the last month that he looks a lot better than his actual results; it looks like some of you folks were on to something. Zach Eflin being better than his numbers is too little, too late for the Orioles, but at least this might make him fetch more at the trade deadline. Seeing Hunter Greene here is a lot of fun, as he’s actually having a legitimately excellent season already. This suggests that he might be stickier in the Cy Young race going forward.

The estimated numbers take a bite out of some of the league’s best pitchers, but many of them (Nathan Eovaldi, Garrett Crochet, Hunter Brown, MacKenzie Gore) are still seen as excellent contributors, just not quite to the same degree. Emerging less unscathed are Joe Ryan and Michael King. King has been hit harder this season and is getting into a good deal more 1-0 counts. Ryan’s zFIP is less concerning, as he has a history of outperforming his zStats, to the point where ZiPS puts less emphasis on the expected stats when running projections.

Turning our attention to home runs:

2025 HR Underachievers Through June 29
Name HR zHR zHR Diff
Jameson Taillon 22 13.6 8.4
Emerson Hancock 15 7.0 8.0
Bowden Francis 19 11.5 7.5
Zach Eflin 16 9.9 6.1
Zack Littell 23 17.5 5.5
JP Sears 18 12.5 5.5
Ryan Yarbrough 10 4.7 5.3
Tanner Houck 10 5.2 4.8
Bailey Ober 21 16.4 4.6
Walker Buehler 15 10.4 4.6
Tanner Bibee 15 10.7 4.3
Aaron Nola 11 6.8 4.2
Jackson Rutledge 8 3.8 4.2
Jack Kochanowicz 15 11.0 4.0
Kyle Hendricks 15 11.3 3.7
Michael Lorenzen 16 12.3 3.7
Keider Montero 11 7.3 3.7
Tomoyuki Sugano 17 13.4 3.6
Kyle Hart 8 4.4 3.6
Tyler Holton 8 4.4 3.6

2025 HR Overachievers Through June 29
Name HR zHR zHR Diff
Kris Bubic 5 10.6 -5.6
Miles Mikolas 9 13.9 -4.9
Hoby Milner 0 4.6 -4.6
Brady Singer 10 14.6 -4.6
Joe Ryan 10 14.5 -4.5
Garrett Whitlock 2 6.2 -4.2
José Buttó 2 6.2 -4.2
Tyler Mahle 4 8.2 -4.2
Matthew Liberatore 7 11.1 -4.1
Nathan Eovaldi 4 8.1 -4.1
Erick Fedde 9 12.7 -3.7
José Soriano 4 7.6 -3.6
Jack Leiter 9 12.4 -3.4
Yusei Kikuchi 11 14.4 -3.4
Drew Pomeranz 0 3.3 -3.3
Shane Smith 5 8.2 -3.2
Martín Pérez 1 4.2 -3.2
Nick Pivetta 11 14.2 -3.2
Framber Valdez 6 9.1 -3.1
AJ Smith-Shawver 4 7.0 -3.0

Of the three FIP components, home runs are easily where zStats for pitchers are the most valuable. Unlike with hitters, home runs for pitchers tend to be an absolutely dreadful stat from a predictive standpoint, and many of the long-term failures to evaluate pitchers have come from taking very high or very low numbers for home runs allowed too seriously. Indeed, home runs allowed being such an abysmal stat for pitchers is why xFIP is more predictive despite it making the assumption that pitchers exert no influence over whether a pitch becomes a home run, which is a ludicrous notion. Home run suppression is far better measured by things like exit velocity data, so practically any estimate that uses this data will do a superior job predicting future home runs allowed than either home run tally or xFIP.

Jameson Taillon is a good example here. His barrel rate isn’t good and his hard-hit rate is ordinary, but neither number is so inflated as to justify a roughly 70% increase in his home run allowed rate, nor is he suddenly missing velocity. He’s allowed more pulled fly balls, which is a bad thing, but it only accounts for about four additional home runs.

On to walks:

2025 Walk Underachievers Through June 29
Name BB zBB zBB Diff
Grant Holmes 42 26.6 15.4
Luis L. Ortiz 42 27.2 14.8
Mitchell Parker 36 22.3 13.7
Ben Brown 27 14.1 12.9
Jake Irvin 30 17.3 12.7
Jack Flaherty 35 23.4 11.6
Sandy Alcantara 34 22.7 11.3
Framber Valdez 39 27.8 11.2
Zac Gallen 42 31.8 10.2
Corbin Burnes 26 15.9 10.1
Keider Montero 20 10.2 9.8
Luis García 16 6.2 9.8
José Berríos 37 27.4 9.6
Clay Holmes 35 25.4 9.6
Bowden Francis 27 17.6 9.4
Jack Kochanowicz 40 30.6 9.4
Shane Baz 35 26.0 9.0
Keegan Akin 18 9.0 9.0
Cionel Pérez 18 9.2 8.8
Luis Castillo 32 23.5 8.5

2025 Walk Overachievers Through June 29
Name BB zBB zBB Diff
Max Fried 21 39.0 -18.0
Hunter Brown 28 45.7 -17.7
Eduardo Rodriguez 27 41.5 -14.5
Joe Ryan 20 32.9 -12.9
Nick Martinez 21 32.3 -11.3
Zack Wheeler 25 36.1 -11.1
Ben Casparius 12 22.8 -10.8
Brandon Eisert 8 18.4 -10.4
MacKenzie Gore 30 40.2 -10.2
Sonny Gray 17 26.9 -9.9
Pablo López 14 23.9 -9.9
JP Sears 22 31.9 -9.9
Trevor Williams 20 29.4 -9.4
Michael King 17 26.1 -9.1
Brent Suter 9 18.0 -9.0
Tomoyuki Sugano 18 27.0 -9.0
Bryan Woo 17 25.8 -8.8
Edwin Díaz 12 20.6 -8.6
Chris Bassitt 25 33.5 -8.5
Freddy Peralta 34 42.5 -8.5

Unlike home runs allowed, walks allowed (and strikeouts) are good stats for pitchers, so zStats don’t dominate the real numbers here. zBB is still more predictive than actual walks, primarily because it includes two plate discipline stats that are important leading indicators of future walk rate: out-of-zone swing percentage and first-pitch strike percentage.

Ben Brown is interesting here because of the great strides he’s made in his walk rate in the majors, with zBB suggesting that he could get even better. His improvement in the first pitch of an at-bat has been quite spectacular; he went from 46% strikes in the minors in 2024 to 69% in the majors this year. Alas, he’s currently bedeviled by a .362 BABIP, so the Cubs are trying to “reset” him a bit in the minors. zBB is less alarmed about Sandy Alcantara than you might expect from his numbers this year, especially early on (and he has in fact improved in recent weeks). He may very well end up being the most valuable trade candidate in July after all.

Now let’s look at strikeouts:

2025 Strikeout Underachievers Through June 29
Name SO zSO zSO Diff
Bailey Ober 74 91.7 -17.7
Jameson Taillon 74 88.6 -14.6
Antonio Senzatela 42 55.9 -13.9
Jeffrey Springs 75 88.5 -13.5
Tyler Phillips 23 35.9 -12.9
Justin Verlander 55 67.3 -12.3
Emmanuel Clase 38 49.8 -11.8
Hunter Dobbins 43 54.3 -11.3
Cade Horton 33 44.2 -11.2
Osvaldo Bido 34 45.1 -11.1
Angel Chivilli 24 35.0 -11.0
Randy Vásquez 48 58.8 -10.8
Mitchell Parker 62 72.7 -10.7
Keider Montero 39 49.6 -10.6
Spencer Schwellenbach 108 118.0 -10.0
Chase Shugart 20 30.0 -10.0
Kolby Allard 14 23.7 -9.7
Orion Kerkering 32 41.3 -9.3
Jimmy Herget 30 39.0 -9.0
Ben Casparius 54 62.6 -8.6

2025 Strikeout Overachievers Through June 29
Name SO zSO zSO Diff
Zack Wheeler 126 101.9 24.1
Garrett Crochet 135 114.7 20.3
Hunter Brown 118 98.5 19.5
MacKenzie Gore 129 111.6 17.4
Chad Patrick 93 75.7 17.3
Joe Ryan 104 86.9 17.1
Grant Holmes 103 88.0 15.0
Yoshinobu Yamamoto 101 87.4 13.6
Max Fried 104 90.6 13.4
Félix Bautista 41 28.6 12.4
Merrill Kelly 100 87.7 12.3
Seth Lugo 76 64.1 11.9
Jack Flaherty 100 88.2 11.8
Ranger Suárez 67 55.4 11.6
Will Warren 103 91.7 11.3
Cole Ragans 76 64.8 11.2
Chris Sale 114 102.9 11.1
Chris Bassitt 93 82.0 11.0
Drew Rasmussen 72 61.1 10.9
Nick Pivetta 101 90.2 10.8

zSO is only slightly more predictive than actual strikeouts, but the projections work best when they have access to both numbers. zSO’s strongest ability is identifying players whose contact rate is a bit out of whack with their strikeout rate.

One thing you might notice is that there tend to be more veterans among the overachievers than the underachievers. There’s actually something to that! It wasn’t my original intention, but the relationship between plate discipline and strikeouts appears to be capturing some kind of ability, whether you call it “veteran moxie” or “pitchability” or whatever, that isn’t measured well by the data. The zSO model actually improves significantly if you include service time as one of the inputs, but I excluded it here simply because I’m trying to only utilize performance rather than these “extra” characteristics. When ZiPS interprets this data in a projection, it believes overachieving a bit more for younger pitchers and underachieving a bit less for older pitchers. This is a work in progress; I’ve been exploring the interaction of repertoire, sequencing data, and strikeouts, which appears to have promise. For now, don’t get too excited or panicky about this data, even though it remains useful!





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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opifijiklMember since 2024
6 hours ago

Thanks for publishing this, it’s cool!

Lots of Red Sox and former Red Sox here! I’m glad to see there may be some regression for Houck and Buehler. Hopefully the Sox haven’t stacked too many losses before that happens to lose a playoff spot. It also looks like the positive regression has a chance to balance the negative regression.