Archive for Fastball

Last year, Justin Steele verged upon pitch-tracking-era history:

Justin Steele is 21 four-seamers away from history*.

Most four-seamers thrown w/out giving up a HR in the pitch tracking era:

Heath Bell: 807 in 2010
Justin Steele: 786 in 2022.

— Alex Fast (@AlexFast8) July 22, 2022

If you don’t know how this ends, my exceptionally dim-witted-but-nevertheless-talented colleague and friend Alex Fast jinxed it in spectacular fashion:

This streak by Steele ended on his first pitch of tonight's game. Schwarber slugs a 90.8 mph four-seam fastball from Steele over the RF wall. https://t.co/1cZyei5EHT

— Meghan Montemurro (@M_Montemurro) July 22, 2022

Nevertheless, the streak remains interesting because Steele is up to his same antics.

Only four pitches have been thrown 500 times this year and allowed just one home run. Here they are, in order of total pitches thrown and accompanied by total plate appearances (PA) completed:

1. Steele’s four-seamer (725 thrown, 199 PA)
2. Kevin Gausman’s splitter (622, 160)
3. Jordan Montgomery’s sinker (584, 170)
4. Hunter Greene’s slider (516, 100)

It’s one thing to accomplish this feat at all; it’s another all together to do so with a fastball (kudos to J-Mont, but a sinker ain’t a four-seamer). Here is the top of the list of only fastballs that have allowed one or fewer home runs, ordered by most thrown. The gulf between first and second from a volume standpoint is astounding:

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This month last year, Connor Kurcon of Six Man Rotation set out to quantify the location aspect of command (or “LRP”). By establishing an accounting system that credited and debited pitchers for changes in ball-strike counts based on the attack zone of and hitter’s disposition (take? swing? ball in play?) for every pitch, he effectively created an alternative to Pitch Value (PVal) that rewards optimal movement through ball-strike counts but with much more pitcher and hitter context.

His findings are as you’d expect: Jacob deGrom and Justin Verlander lead the pack, with Gerrit Cole, Max Scherzer, and Clayton Kershaw not far behind. Other budding aces like Jack Flaherty and Mike Clevinger pepper the list, and some pleasant surprises (such as Brendan McKay, Caleb Smith, and, for those still thirsting, Jake Odorizzi) are scattered throughout as well. Out of the bullpen, newly anointed relief ace Nick Anderson led the pack followed by the underrated Emilio Pagán, breakout reliever Giovanny Gallegos, and others.

Near the end of his post, Kurcon includes a subhead dedicated to Kyle Hendricks where he highlights how Hendricks, widely respected as a command artist, fares lukewarmly by measure of LRP. He then reminds us “LRP doesn’t paint the full picture of command.” True that.

Fortunately, Kurcon has left the door open for me to tie up loose ends with find Gs I’ve been meaning to write up for a couple of months now. Never fear, Hendricks is the command artist we know and love — it’s just that he relies heavily on incurring contact in optimal pitch locations. It is a needle very few pitchers can thread, but Hendricks does it masterfully.

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I spend a lot of time bothering Connor Kurcon. He’s a smart dude with a certain intuition about baseball and a certain ability to apply that intuition to produce tangible results that invariably reflect his hypotheses. He devised Predictive Classified Run Average (pCRA), an ERA estimator that outperforms the big three (FIP, xFIP, and SIERA). He also created a dynamic hard-hit rate which, to me, was astoundingly clever and a superior accomplishment to pCRA (although maybe he disagrees).

Anyway, like I said, I bother him a lot, he tolerates me, we bounce ideas off each other. The journey starts there, with my incessant annoyance of him, but also it starts here, with this Tom Tango axiom: exit velocity (EV) is the primary predictive element of hitter performance (as measured by weighted on-base average on contact, aka wOBAcon) — significantly more so than launch angle (LA). Some of the inner machinations of Tango’s mind:

That's not that interesting. What I was more interested is its relationship to NEXT YEAR's wOBA.

Not surprisingly, the performance of the 5% hardest batted balls is what indicates talent in the following year, and by a huge amount. If you've been following my barrels thread.. pic.twitter.com/hvLWr0rCSr

— Tangotiger 🍁 (@tangotiger) February 27, 2020

I won’t speak for Kurcon, but I think this finding helped guide his work on the dynamic hard-hit rate. I also think it inspired his foray into replicating this effort for pitchers or, at the very least, his attempts to determine the most predictive element of pitcher performance. Which leads us to this tweet that (spoiler alert) is actually not stupid at all:

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Back in June, I tried to tackle the age-old question: are foul balls good or bad? I tried to determine the “worth” of a foul ball by grouping plate appearances by their number of foul balls (from zero to four-or-more) and looking at two outcome metrics: strikeout rate (K%) and weighted on-base average (wOBA). Unfortunately, my endeavor turned up mostly duds. There are some interesting nuggets – a pitcher’s wOBA allowed improves by nearly 30 points in two-strike counts if he allows at least one foul ball – but most other splits were meaningless. Similar attempts to quantify the effect of a foul ball on the subsequent pitch were similarly fruitless.

I stepped back from the research to let it breathe. Intuitively, I knew there should be value here – I just wasn’t sure how it would present itself. Then, one day (specifically, June 27), inspiration struck in the form of Bryse Wilson’s third career start, during which he incurred nine swinging strikes but also 20 (twenty!) foul balls on 56 four-seam fastballs, amounting to a 16% swinging strike rate but also an absurd 36% foul ball rate (Foul%). The coincidence of many whiffs and also many fouls struck me as fascinating and extremely relevant to my previous research. It encouraged me to reframe the question at hand:

How does foul ball rate correlate with other measurements of success by pitch type?

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You may or may not have heard that Yu Darvish is back. By any conventional measure, his latest back-to-back starts of six shutout innings, two hits, and seven-plus strikeouts rank among his best in a long time. By measure of “Game Score v2,” which FanGraphs includes in a pitcher’s Game Log, Darvish’s scores of 78 and 79 are his two best starts since 2017. Because some of his better Game Score starts went more than six innings: these are his two best six-inning starts, period. They happened very recently, consecutively, and he didn’t labor through them, either, throwing just 94 and 83 pitches, respectively.

The last two starts were a gift to those who took a leap of faith. Darvish, who walked at least three batters in seven of his first eight starts (33 walks in 36ish innings!) and 10 of his first 13 (44 walks in 66ish innings!), was an absolute mess. He had compiled a 4.88 ERA, 5.19 FIP, 4.49 xFIP, and 4.96 SIERA in 13 starts, the cherry on top being a walk rate (BB/9) of six. Six! Six batters per nine innings. As my 14-year-old self quoting Ron Burgundy might say: “I’m not even mad — just impressed.”

However, from June 10 to July 3 — a five-start window sandwiched between his early-season futility and his recent wizardry — Darvish struck out 33 and walked just five in roughly 31 innings, compiling a 3.68 xFIP and 3.64 SIERA. Read the rest of this entry »

In a fairly surprising turn of events, the Rockies demoted Jon Gray Saturday. Gray has arguably been baseball’s most enigmatic pitcher this year, posting a career-worst 5.77 ERA supported by career-best peripherals — e.g., a 13.4% swinging strike rate (SwStr%) underpinning a 28.9% strikeout rate (K%), and fielding independent metrics of 2.78 xFIP, 3.08 FIP, and 3.15 SIERA. Given our most basic sabermetric understandings of baseball, Gray should be a very good pitcher, even if he pitches half his starts at hitters’ paradise Coors Field.

I have written about how a common-breed Rockies pitcher’s peripherals might be penalized for calling Coors Field home (Gray inspired this bit of research as well). FIP metrics generally underestimate ERA by anywhere from 0.8 to 1.3 runs for home starts (compared to 0.0 to 0.2 runs for road starts), suggesting that Rockies pitchers may underperform (a) their FIPs by 0.35 runs or (b) their SIERAs by 0.65 runs — given error bars, maybe more.

Still, that doesn’t explain why Gray’s ERA is nearly 6 right now. I shed light on the ridiculousness of the move; his strand rate (LOB%) is suppressed and his batting average on balls in play (BABIP) is elevated, even compared to his uniquely bad baselines. I’m not sure there’s much more to it.

Nick Mariano of RotoBaller noted here that Gray’s fastball has been incredibly hittable since his debut and especially this year. Despite my thoughts on the inevitability of regression in Gray’s favor, I wanted to pursue Mariano’s train of thought a little further. Gray’s fastball is bad, but how bad? And why?

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I already wrote about Aaron Nola this year. So, too, have Eno Sarris, August Fagerstrom and Jeff Sullivan. He’s been a big deal.

When I wrote about Nola — barely a month ago, at this point — he kind of took the backseat to rotation-mate Vincent Velasquez, who had recently struck out 16 hitters in a dominant complete game. However, since VV’s first two starts, during which he struck out a combined 25 hitters, he hasn’t struck out more than six hitters in a single game and has shown lapses in command.

Meanwhile, Nola has, somewhat quietly, turned in one of the season’s best first six weeks. He ranks third in pitcher WAR (wins above replacement) behind Clayton Kershaw and the underrated Jose Quintana. But WAR is partly a function of playing time, so this might be an unfair comparison for, say, Kyle Hendricks, who has started two fewer games than Nola.

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Any day now, Zach Sanders (@zvsanders) will come out with his end-of-season FAVRz/Fantasy Rankings. Look out for them.

For this post, I will provide three sets of rankings using that same approach (summed up z-scores) for our end-of-season “Bullpen Report: Expected Fantasy Rankings”. The Bullpen Report team should follow up with role reports for each division in the coming weeks as well.

The first set of rankings you will find almost anywhere: on the fantasy sites that you use, via player-raters, etc. It’s the standard 5×5 fantasy value (Wins, ERA, WHIP, SO and Saves). The second grid will be for 6×6 leagues (addition of Holds). The last grid will be for 5×5 and 6×6 leagues, but instead of standard ERA and WHIP, we’ll look at rankings if you were to use expected ERA (via SIERA) and adjusted (adj)WHIP through BABIP differential: I will explain below.

1) 5×5 Rankings (Wins, ERA, WHIP, SO and Saves) – actual 5×5 value in column 4; expected 5×5 value in column 5:

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