Brad Penner-Imagn Photos
Few issues tickle my fancy like a baseball participant as much as no good. Blue Jays reliever Brendon Little presently holds this distinction. Take a gander:
That’s Ramón Laureano swinging at Little’s knuckle-curve, which Little bounced off the entrance fringe of house plate. Right here’s one other (pardon Matt Olson’s cameo):
That’s Sean Murphy rapidly resigning himself to failure in opposition to one other curve within the grime, this one picked by Alejandro Kirk. Yet one more for the experience house:
That’s Junior Caminero emulating a mangled corkscrew in opposition to yet one more bouncing curve, one thing for which Little ought to contemplate getting a patent.
As Ryley Delaney famous at Blue Jays Nation a few weeks in the past, the pitch is a superlative providing. It generated 30 whiffs in Might, tied for many by any pitch thrown by a reliever. It has allowed the bottom contact price on the season (min. 200 thrown), fueling Little’s ascent up the swinging strike leaderboard, the place he stands tall with the majors’ fourth-highest swinging strike price (min. 20 innings). The pitch additionally boasts each the lowest zone price (Zone%) and highest price of waste of any particular person providing. Lastly — and I’ve to say I like this about him and for him — Little throws the pitch 83% of the time in two-strike counts. Eighty-three %. 5 occasions out of six! (All stats are as of June 8.)
Opposing hitters ought to know what they’re getting from Little once they’re down two strikes. It’s going to be a knuckle-curve, as a result of the pitch is silly good, if not haunted. It’s not going to be within the strike zone, as a result of it doesn’t should be. The hitter will swing anyway, typically within the type of a noncommittal oooooops swing, as a result of there’s two strikes and naturally he’s swinging. It’s all scripted upfront; even when the e-book have been out on Little, which it must be, it wouldn’t actually matter. This, in any case, is a person who is aware of it’s completely nice to make use of the identical trusty hammer each time he’s offered with a nail. It is a man who needn’t heed sport idea and its intricacies as a result of he supersedes it. It is a man engaged in shenanigans. It is a man as much as no good.
What concerning the pitch has made it so efficient? I’ve lengthy been a proponent of the concept that pitches don’t must be conventionally “good” to achieve success. They will merely be distinctive, unfamiliar. Little’s knuckle-curve actually falls below this latter classification. Its solely fairly shut comp is Kyle Freeland’s curve — when it comes to whole induced vertical and horizontal motion, they’re fairly related — however Little throws his curve 4 ticks more durable with 500 fewer revolutions per minute from the next arm slot. The result’s a pitch that appears a lot firmer out of hand, presumably strike zone-bound, just for the underside to fall out, leaving the opposing hitter to mirror on all the choices he made in his life that introduced him to the completely ghastly hack he simply took at a wobbly curve that didn’t attain house plate.
…
Statcast’s new bat monitoring information are as thrilling as they’re plentiful. The obvious use case for these new measurements is the higher analysis of hitters. However I used to be extra instantly compelled by what these information can inform us concerning the participant on the opposite aspect of the ball (i.e., the pitcher), and I wasn’t alone. Whereas extra apparent functions embody offering higher context for present outcomes like exit velocity, launch angle, and spray angle, Stephen Sutton-Brownkeeper of and mastermind behind Baseball Prospectus’ StuffPro metrics and associated pitch modeling effortsrapidly recognized a really intelligent use of swing path tilt:
Swing tilt relative to a batter’s common tilt on middle-middle four-seams tells you the place they anticipated a pitch to cross the plate ==> modeling anticipated plate location given relative swing tilt provides us a solution to validate anticipated pitch possibilities
— Stephen Sutton-Brown (@srbrown70.bsky.social) 2025-05-21T02: 00: 34.952z
Sutton-Brown’s sabermetric pursuits and achievements routinely clock in above my pay grade. I don’t know the best way to mannequin anticipated pitch possibilities! However I used to be instantly struck by his ingenuity, particularly the premise of estimating the hitter’s anticipated pitch location primarily based on the noticed tilt of his swing. Taking it a step additional, I believe we will assume a hitter sometimes reveals mechanical continuity from pitch to pitch. As such, his swing path ought to function constant tilt at any given pitch location. This assumption permits us to make use of swing path tilt in opposition to any pitch to “reverse-engineer” the hitter’s expectation of the pitch’s location. Compute the distinction between the anticipated and precise places, and also you’re left with what I’ll name the implied miss distance, or IMD.
It’s an imperfect idea. For one factor, the belief that hitters are mechanically constant from swing to swing might be principally right, however not at all times. Not all swings are good swings (as evidenced above), and even among the good swings are totally different from the opposite good swings. For now, this assumption is what it’s. These inconsistencies have an effect on each hitter, so at the least the inconsistencies are constant.
And let’s contemplate a nonetheless picture of a swing the place the bat is proven flush with house plate, having both simply linked with or swung previous a pitch. In case you draw a line from the knob out by way of the pinnacle of the bat like a lightsaber, the swing path tilt intersects an array of places with extremely diverse vertical and horizontal coordinates. Let’s use Bryce Harper for instance:
The pink, inexperienced, and blue circles would all be legitimate pitch places primarily based on Harper’s swing path tilt. I’m not competent sufficient to find out, statistically, whether or not Harper anticipated that pitch to be up on his palms or down and away, each of which lie alongside the swing path tilt conveyed by his lightsaber. To avoid this, I calculated a hitter’s common swing path tilt primarily based completely on pitch peak. IMD then turns into an estimation, with appreciable error bars, of the typical implied miss distance alongside the vertical airplane (i.e., with respect to a pitch’s peak). By decreasing this difficulty from two dimensions to 1, we simplify the query: “How far over or below the pitch did the hitter swing?”
Which brings us to the actual cause we’re gathered right here at present: Little’s knuckle-curve, along with all its different honors and recognitions, boasts the biggest common IMD (in absolute phrases) of any pitch thrown at the least 200 occasions this yr. It’s the one pitch with an implied miss distance better than eight inches on common, not to mention 9. And this isn’t simply on whiffs! Hitters miss his knuckle-curve by a mean of three-quarters of a foot on each swing. They simply swing proper excessive of that sucker:
2025 Implied Miss Distance Leaders/Laggards
Information present as of June 1
Min. 200 pitches thrown
(Two different fast issues. First, this isn’t a Kodai Senga publish, however Senga having three pitches featured right here — two leaders and one laggard — is fascinating. Additionally, as a result of IMD is based upon vertical motion, we will see its bias towards pitches with vertical motion traits in comparison with these with lateral motion traits. Whiffs do are inclined to favor vertical motion, nevertheless, so this bias will not be unfounded. Anyway.)
The magnitude and course of a pitch’s IMD can inform us rather a lot about its effectiveness. For instance, pitches with bigger optimistic IMDs (which means misses excessive of the pitch) induce shallower launch angles and extra groundballs, whereas pitches with bigger adverse IMDs (below the pitch) induce steeper launch angles and extra popups. (“Launch angle affect” beneath controls for pitch peak and opposing hitters’ particular person tendencies.) The road doesn’t move completely by way of the origin, however it’s fairly shut, and it might be gently affected by survivorship bias:
Absolutely the magnitude of the IMD additionally correlates with decrease exit velocities. (“Exit velocity affect” controls for pitch velocity and opposing hitters’ particular person tendencies.) It’s handy to my idea and my fragile ego that, like launch angle affect above, hitters certainly maximize their exit velocities in opposition to pitches the place the implied miss distance is successfully zero…
…as a result of if the IMD is zero, the hitter has extra probably than not squared up that pitch:
From the color-coding in every graph, you may see from just about each vantage level how anticipated contact high quality (vis-à-vis weighted on-base common on contact, a.okay.a. xwOBAcon) is maximized the place IMD is minimized, and the way pitchers’ talents to exert their affect over outputs — by suppressing exit velocities, by skewing launch angles, by stopping flush contact — could be externally validated. Little’s knuckle-curve numbers: -2.1 mph exit velocity affect, -10.4 levels launch angle affect, 13.3% squared-up% (league common is 31%), .171 xwOBAcon (in comparison with .347 precise wOBAcon). All of it tracks.
Thus, I see IMD nearly as a measure of a pitch’s swing (and miss) high quality. It not solely helps corroborate one thing like swinging strike price but in addition lends veracity to a pitcher’s contact administration expertise and the diploma to which they manifest. Pitch location by itself already goes a good distance towards explaining these; pairing location with IMD provides much more readability. IMD probably wants additional stress-testing — for instance, swing path tilt doesn’t improve linearly with pitch peak, so IMD could also be overstated for pitches with low targets (like changeups or Little’s knuckle-curve) in comparison with pitches with excessive targets, like four-seamers — however this rudimentary model appears sufficiently illuminating to me.
On the finish of the day, IMD is only a descriptive metric. It tells us what occurs (or at the least provides us its personal model of the story about what occurred), and it might have some predictive worth to it, however it doesn’t clarify why Little’s knuckle-curve is so good, why it elicits such woeful swings, simply that it does. That, I believe, is a job for Sutton-Brown and like-minded nerds (complimentary) whose work already relentlessly pursues these solutions by way of capital-‘s’ Stuff modeling, arsenal interplay results by way of form mimicry, tunneling, and sequencing, and so forth. These subjects can invariably present an evidence for Little’s dominance. (Talking of which, Stuff+ loves the pitch in query.)
Nonetheless, so far as descriptive metrics go, this one appears fairly nifty, particularly if it validates my Little king, for whom I’ve rapidly developed an excellent fondness. Little’s knuckle-curve makes hitters appear to be absolute buffoons, and IMD confirms this. (The clips firstly of this publish? I selected them by wanting up the three greatest IMDs elicited by Little’s knuckle-curve. They didn’t disappoint.) He spams the pitch relentlessly in two-strike counts; that it’s nearly by no means positioned competitively by the point it reaches house plate provides insult to damage. He can’t preserve getting away with it. IMD suggests he most likely will.
