Archive for pitcher

Using my Pitch Leaderboard, I identified every “new” pitch* thrown during MLB’s glorious first weekend. Then, using my Pitch Comps tool, which uses pitch specs (like velocity, spin rate, movement, and release points) to compare pitches to one another, I wanted to see if I could make any quantifiable declarations about the quality of these pitches in small samples. I can’t write about everyone, so I’ll select the most interesting ones (in my humble opinion).

(*Including existing pitches from rookies for whom we now finally have MLB statcast data as well as existing pitches thrown by players who missed all of 2021 due to injury. It will be interesting to see if the latter group looks measurably different post-injury than they did pre-injury.)

At the end of the season, I’ll revisit to find out these comps were actually indicative (i.e., “predictive,” in a sense) of quality, but it’s also strongly possible the comps will change as samples grow. This is very experimental, but it’s something I’ve wanted to try in the past but hadn’t found the motivation to do.

Why pitch comps? I know I am prone to bias watching a handful of pitches from a pitcher. I can’t scout because I can deceive myself into just about any conclusion (and I think the same can be said for most of us, whether we like it or not).

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Yesterday, I published 15 starting pitcher blurbs, just because. It involved a long prologue behind the what, when, and why of the matter. In short, I spent much of the offseason secluding my digital self from outside noise (fantasy baseball articles, Twitter opinions, etc.), forcing myself to develop opinions on as many relevant players as possible. I isolated my brain from outside analysis and biases, leaving me with only my own. I wanted to document my thoughts, both to have a point of reference during drafts and to have a record of those thoughts for accountability’s sake. If nothing else, be accountable to yourself.

At the end of yesterday’s article, I solicited recommendations from readers for more pitchers to feature. That, my friends, was an enormous mistake. Although I alleged I would pick names at random, instead I combed through 85 comments (and counting) and tallied up the most combined recommendations and up-votes. Those are the ones I’ll present here.

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This offseason I set out on a personal journey. Sentences like that usually precede stories of enlightenment, and I suppose this story features some enlightenment of its own. We won’t know how much enlightenment until after the 2022 season concludes.

I’m not asked this question often, but I have been asked it often enough to have a stock answer for it. It’s some variation of, “What’s the best thing someone can do to improve as a fantasy baseball player?”, to which my answer is some variation of, “Develop an opinion about every player—well, not every player, but you know what I mean.”

This is something that, when I had more time on my hands, I used to do. But in 2020 I became a work-from-home/stay-at-home father navigating a pandemic, and I burned out. Although I managed sporadic success and positive returns on investment in the 2020 and 2021 seasons, I was lucky to escape unscathed—I was all but flying blind. I was, and still am, fortunate to have years and years of watching baseball and playing fantasy baseball to have built an encyclopedic knowledge about most players.

It’s easy, however, to miss the big changes—the breakouts, the fall-offs, the rookies. It’s easy to dismiss them, and easier yet to enter a draft room and ignore them all together. Indeed, you can build a winning team without deeply investigating these types of players, instead relying on existing knowledge about existing players. You can do it, but it’s difficult, and it’s foolish.

So, I dedicated myself to the task of developing opinions about nearly every player.

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Recently I published my Peripheral Prospect hitters for 2021. In a perfect world I would have published my thoughts on a wider array of hitters periodically throughout the season. Alas, this is not a perfect world, so I settled for a year-end catch-all post.

Rinse and repeat for pitchers. The rules: (1) They pitched in the high minors (Double-A or Triple-A) but not the MLB level, and (2) they cannot be featured on any prominent top-100 list. Top-100 updates count (and all due respect to those updated lists, because revising your priors is not a bad thing!).

Brace yourselves: I’m kicking this off with three Cleveland farmhands with whom I implore you must familiarize yourself. Eight Peripheral Prospect pitchers for your fine Wednesday:

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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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I’ve had the question written on my whiteboard for ever: are foul balls good or bad? It’s a glass-half-empty, glass-half-full conundrum. The former group might think a foul ball is simply a barely-missed opportunity at in-play contact. The latter group might view that same event as a positive — that the poor quality of contact on a foul ball is indicative of an ability to induce poor contact quality in general, and it’s not inherently different from a swinging strike.

In my heart of hearts, it makes more sense to me that a foul ball is closer to in-play contact than not. Considering the diameter of both a bat and a ball, and the nearly physically impossible feat of connecting the two in motion, a foul tip has a margin of error of mere inches, whereas a swinging strike, fully sans contact, can have a margin of error measured in feet. Yes, it seems like getting a piece of the ball suggests, from the pitcher standpoint, makes the glass appear more half-empty than otherwise.

I wanted to finally tackle the subject, but I didn’t really know how. I first looked at the outcome of the pitch directly following foul and non-foul pitches, but it was a bit noisy (although, to be fair, I may have missed clear patterns in that noise). I imagine the effects spawning from a foul ball are not exclusive to the next pitch; rather, they may manifest two or three or even four pitches deeper into the plate appearance. In other words, a pitch-sequencing analysis might be prohibitively difficult, at least for someone like me who lacks the brainpower or mental stamina to pull it off.

Instead, I opted for something a little easier yet arguably just as telling. Read the rest of this entry »

Last week, I recommended an improvement for expected fielding independent pitching (xFIP) without dismantling the original FIP framework upon which it was built. FIP describes the relationship between ERA and strikeouts, walks, and home runs allowed; xFIP does the same but attempts to remove the luck component from home runs by multiplying the number of fly balls a pitcher allows by the league-average rate of home runs to fly balls (HR/FB) — the rationale being HR/FB is notoriously fickle to project year to year.

The recommendation: change HR/FB to include line drives (LDs) and exclude infield fly balls (IFFBs, aka pop-ups). It’s worth noting our dark overlord David Appelman once explained how removing pop-ups from aggregate fly balls insignificantly affects xFIP. Additionally, less than 1% of line drives result in home runs. The recommendation, then, seems like the merging of two separate but equally fruitless endeavors, given the facts.

Yet changing the HR/FB component in xFIP to be “HR/(oFB + LD)” substantially improved the metric’s correlation with same-year ERA. Adjusted r², which measure the strength of relationship from 0 to 1, increased from 0.42 to 0.55 using Statcast data (0.44 to 0.53 using FanGraphs data). I hypothesize that, when added to fly balls, line drives (despite resulting in very few home runs) give a more holistic indication of the average contact quality and launch angle a pitcher allows.

Today’s recommendation: account for start/relief splits.

Although I thought of this independently, the idea itself is far from an original one. Read the rest of this entry »

Rob Silver, the 2016 National Fantasy Baseball Championship (NFBC) Main Event winner and high-stakes fantasy baseball extraordinaire, messaged me on Twitter a few days ago to ask a question: Which source of pitching statistics are most accurate? I’m paraphrasing. Also, I could paraphrase the question any number of ways: Which source should we be using? Which most reliably correlates with pitcher performance?

It was a question for which I had no answer. Admittedly, I use a variety of sources, none of which align with one another — something I have noticed before but about which I can do nothing but shrug and accept it as a quirk of being a sabermetrician who bears the struggle of dealing with publicly available data.

The sources cryptically mentioned above include the following:

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If something about Madison Bumgarner’s first eight starts of 2018 have seemed odd to you, it’s because they have been. No matter the fielding independent pitching statistic to which you subscribe — FIP, xFIP, SIERA (although, frankly, it should be SIERA) — Bumgarner’s 2018 has not inspired confidence. Despite a dazzling (and quintessentially Bumgarnerian) 2.90 ERA, his baserunner suppression skills (i.e. strikeouts and walks) have lagged this year, and the various FIPs all portend severe bumps in the road. Granted, Bumgarner has outperformed his FIPs the last three years and throughout his career. I’m here to argue not that we should dismiss our concerns because of this but, instead, that such overperformance has insulated us from what should be potentially serious concerns about MadBum’s long-term health and success.

The problems with Bumgarner’s 2018 season — or at least the peripherals that underpin his 2018 season — thus far stem back not to his broken finger but, rather, something both farther back and much more dire. You may or may not recall Bumgarner fell off a dirt bike last year and injured his throwing shoulder. He returned from that injury almost exactly a year ago and promptly underwhelmed us. Sure, he posted a 3.43 ERA through September and has a 3.23 ERA in the calendar year since his return. It’s not vintage Bumgarner, but it’s not awful. But the peripherals, oh, the peripherals: his strikeout rate (K%) has caved dramatically, falling more than 6 percentage points (27.1% from April 2015 through April 2017; 20.9% from July 2017 onward).

It’s his fastball. Bumgarner’s fastball, once elite (relative to other four-seamers), is broken, and it has been broken for a year.

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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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