Archive for Splitter

Zack Wheeler, prior to about two weeks ago, was an enigmatic starting pitcher on whom many fantasy owners had started to lose faith. (Such faith might again be lost after Sunday’s start, which is another issue all together.) After missing two seasons recovering from Tommy John surgery and related setbacks, Wheeler returned to the mound in 2017 only to be sidelined once more and miss almost the entire second half of that season. The prospects of him succeeding in 2018 were, frankly, not great.

Wheeler, however, came out firing, his four-seamer and two-seamer averaging 95.9 and 96.1 mph, respectively, up from 94.8 and 94.3 mph — a moral victory in its own right. The added velocity helped both pitches play up in way completely unseen the year prior, as measured by expected weighted on-base average (xwOBA) allowed:

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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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If you’ve paid any attention to the San Francisco Giants, you’ll know that they stink something awful right now. The parts generally are no greater than the whole. Jeff Samardzija, he of the 5.44 ERA, is not blameless here.

In an alternate universe, though, he could be. Some in(s)ane factoids about Samardzija: Only Chris Sale has as many starts as Samardzija in which he struck out more hitters than he completed innings (6). (In Sale’s first start of the season, he went seven and struck out seven. So close.) Samardzija is also one of only six starters with four-plus starts of eight-plus strikeouts. And among pitchers who have thrown at least 75 innings since August 8, 2016*, Samardzija’s 3.12 xFIP ranks 7th-best, behind only Carlos Carrasco, Noah Syndergaard, Clayton Kershaw, Sale, Michael Pineda and James Paxton. That is elite company.

*Why August 8? I was trying to see who has been better than Ivan Nova since he was traded to the Pirates. Nova shows up 8th on that list above. Seeing Samardzija’s name directly before his floored me.

Samardzija is striking out the world yet has little to show for it. His advanced stats (28.7% K, 5.2% BB) suggest excellence, and his peripherals (11.8% SwStr) affirm them. In short, his 3.43 FIP and 2.87(!!!) xFIP depict a much more effective starting pitcher. It’s his strand rate (LOB%) — a catastrophically bad 58.1% — that has done him in. Normalize it, and he’s sitting pretty with a mid-3.00s ERA.

All that said, I’m here to investigate what changed. Once upon a time, Samardzija was a touted prospect, cracking multiple top-100 lists in 2009. The strikeouts lived up to the hype, yet the results lagged. Then the K’s eroded, and the results eroded further. They K’s are back, and they’re back with a vengeance.

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Jeff Sullivan wrote about Matt Shoemaker last week, being one of the first (to my knowledge) to note that Shoemaker had recently ramped up the usage of his splitter. I’m reluctant to overdo it, but you should read that first. I’d also like to borrow one of the main points he made in order to better establish my narrative. I hope you don’t mind.

Sullivan noted Shoemaker’s increased use of the splitter in all counts. Which is great, because it’s arguably his best pitch. His slider is good, too — both induce an almost-equal percentage of whiffs per swing — but it’s the splitter that has coerced a meager .117 isolated power (ISO) in his Major League career. That’s a big part of it. More splitters means fewer other things, and those other things, as Sullivan noted, have generally been bad.

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Homer Bailey was really coming into his own as we began 2014. He had gotten incrementally better in each of his first seven seasons, bettering his K:BB ratio each season and stringing together five years of ERA improvements from 2008-2013. But then he came out and dropped an ERA north of 5.00 for the first two months of the season. He rebounded in the summer, but then a strained flexor tendon ended his season in early-August. He had surgery a month later (September 5th) and is slated to be ready for Spring Training. In fact, a December 5th report suggested he was actually ahead of schedule.

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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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