Archive for Swinging-strike%

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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Pitch modeling isn’t anything particularly unique or groundbreaking. It’s the kind of thing Harry Pavlidis and Jonathan Judge (of Baseball Prospectus) and our once-editor Eno Sarris (now of The Athletic) have investigated for years. I won’t claim to break new ground here. I’m just a nerd who likes testing hypotheses for himself.

Last year, I used velocity and movement, courtesy of PITCHf/x, to model swinging strike and ground ball rates for pitchers. That post was not my best work (easy to say in hindsight), primarily because of limitations with the data. The data, from Baseball Prospectus, was aggregated, such that I couldn’t isolate any single pitch thrown by a pitcher. The advent of Statcast has enabled us to do exactly that, providing publicly accessible hyper-granular pitch-level data and changing how the public sphere of sabermetricians nerd out.

Something I have wanted to do for a long time is refresh my previously-linked analysis, but with (1) Statcast data and (2) a different modeling approach — namely, the use of a probit model rather than a multiple regression model. For most of you, this means nothing. It’s gibberish. I don’t intend to wade too deeply into the weeds of the modeling, lest I disorient or alienate. Mostly, I just want to communicate I think it’s an exciting and different way to answer the everlasting question: how does a pitch’s velocity, movement, and spin rate affect its outcome?

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At the beginning of last season, I contextualized the swinging strike rate (SwStr%) (and refreshed those numbers after the season concluded). I had seen other analysts call certain pitches “above-average,” “below-average,” “elite,” etc. using the league-average whiff rate as a baseline. This is neither a criticism nor a judgment, as I absolutely did this before I had my statistically-driven epiphany. But understanding the average four-seamer’s or slider’s or cutter’s whiff rate lends additional context to any assertion one might make about the “elite-ness” of a pitch.

More recently, I wanted to convert discrete outcomes by pitch type into fielding independent pitching (FIP) statistics — namely, FIP and xFIP (expected FIP, which substitutes a pitcher’s rate of home runs per fly ball for the league-average rate). Let me warn you now: the results are very imperfect. It took some brute force on my part to get there, but I got there. I would wager that the the extreme (lowest and highest) values are probably a bit exaggerated. Regardless, it’s an interesting table to ingest:

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This year, I’ve been caught up on pitching. I investigated the nuance inherent to swinging strikes, indirectly made a case for completely abandoning the sinker with this piece comparing pitch type outcomes, and (maybe) identified the keys to unlocking pitcher BABIP and HR/FB.

Here, I’ve modeled swinging strike and ground ball rates using only pitch velocity movement. Surely, this work can be improved; my quantitative tool set, while fairly robust compared to the layman, is meager compared to the professional or even hobbyist statistician. Regardless, I think it’s pretty cool, and I hope it adds to the conversation constructively.

Mostly, this serves to satiate my own curiosity. Unfortunately, it may be denser than I expected — few answers are ever quite as simple as you hope them to be, I guess.

Existing Research

I linked to several of my own pieces above. Dan Lependorf wrote about estimating ground ball rates in 2013 at the Hardball Times, although its conclusions have an anecdotal slant. (It thinks about velocity and movement but doesn’t take the requisite steps to bridge the logic.)
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As a Twitter dork, I’m exposed to a lot of discussion about swinging strike rates (SwStr%), so much so that it almost feels like it has supplanted xFIP (or other comparable metrics) as a catch-all way to evaluate pitchers. Dude has a 12.5% whiff rate! Sweet. It’s not for naught — swinging strike rate bears a strong correlation to strikeout rate (K%), which comprises substantial portions of the regression equations that underpin the aforementioned xFIP and its counterparts. Swinging strike rate’s correlation to the following metrics (using data from the last five years of 714 pitchers who threw at least 100 innings in a given season):

• K%: r = 0.83
• SIERA*: r = 0.61
• xFIP*: r = 0.55
• FIP: r = 0.50
• ERA: r = 0.40

(*See footnote.)

It also correlates strongly year over year (among 392 player-seasons during the same timeframe in which the pitcher threw 100 innings in the current and subsequent seasons):

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I planned to include Rhys Hoskins in my blind résumés post from Monday, but I couldn’t find any realistic comps for him. Part of the problem is no one does for a full season what Hoskins did for 50 games. Part of the problem, also, is no one does for a full season what Hoskins would be expected to do for a full season, based on his peripherals. It’s a fairly unique skill set (although let’s not conflate “unique” with “the best” or any kind of superlative like that… yet).

Hoskins had himself a real, real nice debut. This isn’t the first time you’ve read about him in the last couple of months and it will be far from the last. Andrew Perpetua, for all intents and purposes, regressed his batted balls from 2017 and he still would’ve had an awesome season. In Eno Sarris’ heart, as well as mine, Hoskins was the runner-up National League Rookie of the Year to Cody Bellinger.

Hoskins had himself a real, real conveniently sized debut as well. His playing exactly 50 games prevents me from arbitrarily choosing a cutoff and having to justify it. A cutoff for what, you ask? Well, Hoskins, in exactly 50 games, posted a .359 isolated power (ISO) while swinging and missing only 7.1% of the time. He struck out a fair deal, but he also walked a ton. Take this snapshot of a season and, as aforementioned, you’ll be hard-pressed to find comps.

Which is exactly why I set out on a very pseudo-scientific quest to find any of Hoskins’ contemporaries who have done this — this, being the aforementioned 50 games of a .350-ish ISO and a 7%-ish swinging strike rate (SwStr%) — at any point in their careers (or within windows of their careers that I’ve curated). I’m winging it here, plucking names from my brain who have elite power and at least above-average plate discipline (assuming Hoskins might, but it’s not a foregone conclusion) and scouring their careers for similar streaks. Any omitted hitters are a product of my lack of memory or imagination, not of malice. Except for Giancarlo Stanton.

Mike Trout

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On 7/18, Madison Bumgarner changed his positioning on the mound. Eno noted it and Madison Bumgarner confirmed it. Bumgarner has worked to make his pitches (and I presume his release point) very similar through video and in front of a mirror “making sure he sets up in in the right places.”

Eno summed it up: “Bumgarner is ready to make the most of his old playbook. Throw lots of fastballs, cutters, and curves, all from the same release point, all with similar spin, and all exploding out of a slow, deliberate delivery.”

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