Archive for four-seamer

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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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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Some good pitchers, despite being good pitchers, throw bad pitches. And there are bad pitchers, too, who throw good pitches. Both are true, and one could make an argument a Venn Diagram of the two groups may overlap significantly, and that overlapping area is the group of pitchers toeing the line between breaking out and being unusable for fantasy purposes.

It stands to reason, then, that good and bad pitchers could benefit from easing off or completely abandoning their bad pitches. It’s one thing to evaluate a pitch based on its underlying metrics — its swinging strike rate (SwStr%), its ground ball rate (GB%), its velocity, and so on. It’s another thing to evaluate the pitch objectively by looking at its weighted on-base average (wOBA) allowed, which, I hope, in an adequately large sample, can indicate a pitch’s quality regardless of its peripherals. In theory, the larger the sample size, the greater the probability a pitch’s outcomes will converge with its inputs, such that the caveat “regardless of its peripherals” doesn’t actually mean anything. Given enough pitches thrown, the aforementioned underlying metrics will adequately inform the wOBA allowed.

Using PITCHf/x data from the last two years, I looked for (1) good pitchers who throws pitches that allow (2a) extremely bad wOBAs with (2b) unusually low BABIPs. Incurring high wOBAs on low BABIPs is less than ideal; if BABIP is subject to high variance and generally converges on the league average, then a bad pitch being “lucky” by BABIP suggests things will only get worse.

This post was going to be about several pitchers, each with their own problematic pitches, but I became too passionate about this single case. This is about Dylan Bundy, his abhorrently bad four-seamer, his fantastic slider, and how much his pitch selection is suffocating his potential. Ultimately, it’s about adding by subtracting.

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