Hi! It has been made to clear to me that my use of f = m * a as a narrative device herein was quite distracting, chiefly because in this context it’s incorrect. I apologize in advance to renowned baseball physicist Prof. Alan Nathan, should he ever read this; to all other physics enthusiasts who have remarked on the mistake; and to me, for embarrassing me. Ideally you will see past it and appreciate the meat and potatoes of the post for what they are: that there is possibly declining marginal utility to bat acceleration in a way we don’t seem to witness for bat speed. Thanks, and sorry again!

I can’t possibly begin to cover all the excellent work concerning Statcast’s new bat tracking data. Now as much as ever, it’s important to support your local Baseball Prospectuses, PitcherLists, Baseball Americas, FanGraphses and freelance Substack writers. We move quickly in these parts. There’s so much analysis to consume, all of it superb.

When confronted with this new data, one of my first instincts was to see which metrics from other areas of sabermetric analysis could be replicated within the bat tracking framework. Enter 90th-percentile exit velocity (90EV); it’s a powerful shorthand metric that distills a lot of information about the top end of a hitter’s exit velocity distribution into a single number. It’s not perfect, and other metrics outperform it, but it’s easy to see how it has become popular in contemporary analysis, especially in prospecting and scouting circles. Read the rest of this entry »

Release angles comprise the vertical and horizontal angles at which a pitcher releases a pitch. They are the natural counterpart to approach angles, except they capture the initial angle of a pitch’s trajectory rather than its final angle upon crossing home plate. Release angles can tell us a lot — namely, where a pitch is headed (or, at least, intended to go). However, we already have plenty of data to describe a pitch’s flight path. We have its short-form movement (i.e., total inches of break), as well as its acceleration and velocity vectors in all three dimensions, not to mention its final location coordinates. We can pretty much map the entire trajectory without release angles. Like the last unrevealed letter in Wheel of Fortune, you theoretically need it to solve the puzzle, but you can probably infer the word or phrase just fine without it. What are release angles, then, if not just a different way to describe a pitch’s movement in space? What information do release angles add? (Michael Rosen adeptly provided an answer to that question here.)

When a pitcher throws a pitch, the pitch reaches home plate in a fraction of a second. The opposing hitter, then, has a fraction of a fraction of a second to discern a great many things about the pitch: its velocity, its shape, its probable final location, all to then ascertain whether or not he should swing. Given the impossibly small window of time in which to make a swing decision, much of a hitter’s behavior is influenced by the untold thousands of pitches he’s seen before, like a mental library of pitch shapes. One of the very first visual cues a hitter receives, aside from the pitcher’s release point, is the angle at which a pitch leaves the pitcher’s hand. This particular visual cue ought to enable a hitter to determine out-of-hand a prohibitively bad pitch — one that, on most occasions, will not find the zone. He can potentially make a snap decision with a fairly high degree of confidence that the pitch will miss the zone. Read the rest of this entry »

The proliferation of Stuff models has invariably pitted capital-S Stuff against command in terms of their respective importance to a pitcher’s success. If you had to choose one, is it better to locate well, or is it better to be filthy?

The answer to this question is why baseball is beautiful and delightful but also occasionally horribly frustrating. From Vicente Iglesias and Scott Powers:

Importance, in layman’s terms, means how relevant a variable is to explaining an observed outcome; reliability is how much we can expect a variable to repeat or be repeated. In baseball, we often talk about these ideas in terms of “descriptiveness” and “predictiveness.” This dichotomy illustrates the Catch-22 that forever propels baseball forward in all its uncertain glory: pitch location plays an outsized role in determining the outcomes we witness, yet we cannot expect to rely on it year over year. Meanwhile, Stuff is much more reliable next year — i.e., changes to a pitcher’s pitches in terms of velocity, movement, and release point tend to vary to a much smaller degree over time — but it plays a significantly undersized role in influencing actual outcomes this year. Brutal stuff. Why do we even bother? Read the rest of this entry »

I’ll lead with this: I’m not certain the Launch Angle Revolution^TM was ever really a thing — or at least, it wasn’t a thing in the way we thought it was. In 2019, we were faced with an onslaught of home runs that needed an explanation, a genesis. It made sense to turn to launch angles: all else equal, if you hit balls higher, they tend to travel farther. As we’d later learn, juiced balls were much more a culprit than anything else. I wish I could find the sound byte for it – my squishy memory may have manufactured it – but I swear I recall Christian Yelich, perhaps the juiced ball’s most prominent (though, to be clear, not necessarily its biggest) beneficiary, scoffing at the concept of a “launch angle swing.” (Edit: It’s here! Thanks, Mike Petriello!) Although Yelich’s fly ball rate jumped 13.4 percentage points in 2019, he (arguably rightly) denounced the very idea of what everyone assumed had fueled his success.

There is, however, unquestionably another revolution afoot: the Pulled Fly Ball Revolution^TM. Inherently, it’s its own kind of launch angle revolution. But it’s also a spray angle revolution, and a pitch selection revolution, and a swing decision revolution. It is multifaceted and sprawling, and it is much more clearly defined than its predecessor. Here’s the percentage of batted ball events (BBE) that were pulled fly balls (PFBs, for short) by year:

Read the rest of this entry »

It’s been almost two years since I authored a visual primer on vertical approach angle (VAA) and almost three years since I first discussed VAA here at FanGraphs. The topic directly adjacent to VAA — horizontal approach angle (HAA) — has been marinating in my mind (and in my drafts) ever since.

In an auspicious turn of fate, while revisiting this draft, our beloved Eno Sarris reached out to ask if I had ever published anything about HAA. He had questions about Brandon Pfaadt — specifically, Pfaadt’s sweeper. Much was made about Pfaadt’s success this postseason, which can be attributed, in no small part, to his sweeper and how he weaponized it. (More on that later.) I was spurred into action. Thanks, Eno! Read the rest of this entry »

This time last year, I investigated where vertical approach angle (VAA) seems to matter most. The short answer: at the top of the strike zone for four-seam fastballs and at the bottom of the zone for sinkers and two-seam fastballs. This piece, which is adapted from a presentation I did as part of the 2022 PitcherList PitchCon, will provide much-needed additional context, like benchmarking and watermelon-colored heat-map-style graphics.

For the uninitiated — which could be many of you — VAA is the angle at which a pitch approaches home plate… vertically. Despite its usefulness, the concept has experienced slow uptake in the public sphere. I think that’s largely due to a lack of data, which, for nerds like me too entrenched in baseball Twitter, has shrouded the metric in mystery. Why are scouts and college baseball R&D departments valuing VAA so highly, why have I barely heard of it, and how can I find it?

To answer the last question: Statcast is granular enough that, fortunately, we can calculate VAA using physics. So, let’s calculate it! Thanks to Baseball Prospectus‘ Harry Pavlidis (who credits baseball’s renowned physicists), here are the equations: Read the rest of this entry »

Last year, Connor Kurcon created dynamic hard-hit rate (DHH%) to add dimension to our typical understanding of Statcast’s hard-hit rate (HardHit%). Whereas HardHit% uses a fixed minimum exit velocity (EV) threshold of 95 mph to determine a hard hit, DHH% uses a — you guessed it — dynamic threshold that changes according to launch angle of the batted ball event (BBE). Kurcon found this orientation of hard-hit rate to be more powerful than its original in terms of describing same-year contact quality (per weighted on-base average on contact, or wOBAcon), predicting next-year contact quality, and predicting itself (year-over-year “stickiness”).

Inspired by a Yermín Mercedes home run off a Willians Astudillo eephus, I borrowed the premise of DHH% and applied it to pitch velocity — that is, the dynamic threshold was based on pitch speed rather than launch angle. Although not as powerful as the original, Pitch DHH% also proved itself superior to HardHit%.

Ever since Kurcon unveiled DHH% in 2020, though, I’ve been thinking about how the premise might apply to spray angle (horizontal angle, lateral angle, whatever you want to call it). It seemed intuitive to me that a hitter would generate more power to his pull side and less to the opposite field. I suspect if you were prompted to guess, you might have said the same. Read the rest of this entry »

On May 17, Chicago White Sox legend Yermín Mercedes hit the sixth home run of his stellar, albeit wilting, nevertheless unlikely, rookie campaign. A mammoth blast over the center field wall of Target Field, the home run sparked — in equal parts, seemingly — awe and controversy.

? THE YERMINATOR JUST HOMERED OFF OF LA TORTUGA! ? pic.twitter.com/pHacUxXtdF

— MLB (@MLB) May 18, 2021

The controversy? Mercedes teed off on a 3-0 count with one out to spare in a 15-4 blowout, off a beloved Position Player Pitching^TM no less. He did so in the home park of a sputtering rival, one expected to compete for their division’s title but, at the time, had instead won half as many games (13) as it had lost (26). Naturally, a lengthy and unpleasant discourse about the game’s unwritten rules ensued. Retribution, however juvenile, was had.

At the time, the sheer amount of baggage on the home run did not register with me. My brain is so moldy and soggy that I reacted somewhat primitively. Good lord, Yermín Mercedes absolutely mashed possibly the slowest pitch I’ve ever seen.

Indeed, Mercedes’ home run is the hardest-hit batted ball (109.3 mph) against a pitch 60 mph or slower (47.1 mph) in the Statcast era. Only Christian Walker (seen here) and Ryan McMahon (seen here) come close, and their batted balls came against pitches thrown more than 53 mph. That’s, like, light speed in comparison. Read the rest of this entry »

You know it’s almost time for baseball season when all of the major projection systems forecast Kyle Hendricks‘ ERA one run per nine innings too high.

As much as this sounds like a knock on those who develop projections, it’s not. What Jared Cross (Steamer), Dan Szymborski (ZiPS), Derek Carty (THE BAT), and the folks at Baseball Prospectus (PECOTA) do is no small feat. If I weren’t too cowardly to even try to create my own projection system, I would be too stupid to design one that is half as effective as theirs. Glass houses and all that.

That said, I am just smart enough to know that projected ERAs ranging from 3.84 to 4.42 for Hendricks, who boasts a career ERA of 3.12 and has never finished a season with an ERA above 3.46 (except that dastardly 3.95 ERA in 2015), are too high. It’s easy to poke holes in the obvious outliers, but projections succeed by describing and then predicting the talents of most pitchers, not the ones whose talents deviate dramatically from expectation. Hendricks is every projection system’s known blind spot.

It’s not just projections that struggle with Hendricks, either. We, the sabermetric community, frequently use ERA estimators as shorthand to characterize a pitcher’s talent level. If you frequent FanGraphs, you’re familiar with Fielding Independent Pitching (FIP), expected FIP (xFIP), and Skill-Interactive ERA (SIERA). By virtue of how they’re constructed, each metric makes assumptions about the skills a pitcher theoretically “owns”:

• FIP: strikeouts, walks, and home runs allowed
• xFIP: strikeouts, walks, and fly balls induced
• SIERA: a complicated combination of strikeouts, walks, net groundballs (groundballs minus fly balls), and their squared terms and interactions with one another

While each estimator features a batted ball component, they focus on trajectory (launch angle), not on authority (exit velocity). This is a fair assumption, frankly. I have illustrated how a pitcher can influence hitter launch angle, operating under the assumption they bear little to no influence over hitter exit velocity. It’s not quite that bleak; certified baseball genius Rob Arthur found that the average pitcher’s effect on a baseball’s exit velocity: roughly five parts hitter, one part pitcher. Read the rest of this entry »

A couple of weeks ago, I was chatting with PitcherList’s Alex Fast about four-seam fastballs swinging strike rates (SwStr%) and their relationship to pitch height — or, perhaps more specifically, their lack of relationship. At the pitcher-season level (e.g., “2020 Clayton Kershaw“), the correlation between SwStr% and pitch height appeared weak at best. When you consider that no fastball is created equal and then introduce small-sample variance to the equation, the relationship could, understandably, become blurred at the pitcher level.

As a retort, I sent him the following graph, which shows SwStr% by pitch height for the three broad pitch classes as defined by Statcast, the source of the data. For reference, I’ve added black lines to indicate the average bottom, heart, and top of the strike zone:

If we zoom out and consider the question at the macro level, independent of context (what’s the average swinging strike rate for all fastballs by pitch height?), we can see that fastballs generate more swinging strikes up in the zone, a phenomenon our own Jeff Zimmerman touched upon here. This finding is mildly interesting in and of itself. But as I considered the matter further, the importance of swing frequency (Swing%) to SwStr% became clear (both use all pitches as a denominator). Regardless of efficacy, more swings will afford more chances for swinging strikes. As such, I anticipated that fastballs probably induce more swinging strikes up high than down low simply because hitters swing more frequently at high fastballs. Similarly (but inversely), non-fastballs would generate more swinging strikes down low instead of up high. The next graph all but affirmed my intuition:

Although the peaks of the bell curves cluster near the heart of the zone, we can see distinct differences in swing rate by pitch class at the thresholds of the strike zone. At its bottom edge, hitters are half as likely to swing at fastballs as they are at non-fastballs; at its top edge, twice as likely. Read the rest of this entry »