If you watch a random pitch from a major league game, there’s a better than even chance you’re going to end up picking out a fastball. The fastball is the core concept upon which pitching is understood, the theme upon which all variations, from changeup to knuckle-curve, are composed. Our society has three great establishments: “establish the fastball” in baseball; “establish the run” in football; and the Establishment Clause of the First Amendment.
Tyler Rogers, easily the owner of the Statcast Era’s lowest vertical release point, throws a rising slider that’s rising even more this season. The positioning of his forearm at release means that a traditional curveball grip puts his thumb on top of the ball and the rest of his fingers underneath; as the ball rolls off of his hand, it creates backspin in addition to the sidespin more typical of a slider:
As you can imagine, the traditional fastball grip places his index and middle finger pointing towards five o’clock or 5:30 rather than a more typical two or three o’clock from a three-quarters arm slot. This results in arm-side sidespin, but also some extra drop, such that his fastball sinks more than his slider:
The Tyler Rogers four-seam fastball has more drop than the average MLB curveball. ? pic.twitter.com/dZIjz9DDI1
Needless to say, I’ve found these two offerings to be among the most unique pitches in the majors this season by a couple of slightly differentmethods. As a result, the Giants were a confounding data point when I used my team-wide pitch-uniqueness model to estimate which pitching staffs roll out the widest array of “looks.” Read the rest of this entry »
If you’ve watched The Brady Bunch, Family Ties, Community, or pretty much any other sitcom, I’m sure you’re familiar with the “two dates to the dance” trope. The premise is exactly what it sounds like, and antics are guaranteed to ensue. It almost always ends in disaster, and the wannabe Lothario learns their lesson. If they had only picked a single date, they might have had a lovely evening. Instead, as Confucius says, “The man who chases two rabbits catches neither.”
It’s not just TV characters who try to pull this off; some of the most prevalent baseball statistics are guilty of double dating, too. In particular, I’m talking about the stats that try to court the analytics crowd and more traditionally-minded fans at the same time. This is an admirable endeavor (unlike two-timing your prom date), but that doesn’t make it any less of a fool’s errand.
OPS+ is the perfect example. It takes a widely understood statistic and revamps it for the modern age, but as a result, it combines all the inaccuracies of OPS with all the complexities of park and league adjustments. It’s too much for most casual fans to wrap their heads around, yet it still undervalues on-base percentage and overvalues extra-base hits — cardinal sins for the hardcore sabermetricians among us. I’ve long thought that isolated power falls in the same category. It’s missing the simplicity and storytelling quality of batting average and total bases, but it also lacks the precision of advanced numbers like wOBA and wRC+. Thus, I’ve never fully understood who the target audience for ISO really is. Read the rest of this entry »
Last year, the sweeper took baseball by storm. Fast forward to this season, and 4.2% of all offerings through the first half have been sweepers, according to Statcast, nearly twice as many as last season. But I have my issues with MLB’s pitch classification system, and it’s been well documented that under their sweeper umbrella there are multiple versions of the pitch; the Yankees’ staff alone threw several different variations last season. Plus, if the number of different names for the pitch (whirly, rising slider, etc.) is any indication, other teams have their own iterations, too. Qualms with MLB’s system aside, if we want to look for the next sweeper, it’s a given that MLB won’t have a classification for it yet anyways.
Why should we look for the next sweeper? The pitch was extremely effective last year, saving pitchers 0.56 runs per 100 tosses. Even this season, as usage has nearly doubled, the sweeper is still saving pitchers 0.18 runs per 100 tosses. But in order to look for the next one, we first have to ask: what makes all sweepers… sweepers? MLB relies on grip and self-reported pitch identifications for their classifications. In the absence of those, we can use velocity, spin rate, spin axis (in three dimensions), and movement (in two) to identify a new pitch.
Even though some teams might throw multiple versions of a pitch, I still think that our best bet to find a new pitch type is by honing in on individual teams. As with the sweeper and its early adopters, teams that discover an effective new pitch will want to teach it to everyone they can, uniformity of pitching looks be damned. In other words, if a team has multiple different pitchers throwing a specific pitch, they must like it so much that their affinity for it outweighs the cost of having pitchers that don’t contrast (which seems to reduce effectiveness). Read the rest of this entry »
As we know, baseball is a bit of an oddball relative to other ball-centric sports for several reasons. Prominent among them, the defense controls the ball at the start of each play, whereas in basketball, football, soccer, and hockey, to be on offense is to be the team with the ball. There exists a mindset difference between playing offense and playing defense, or rather between controlling the ball versus not controlling the ball. One is proactive, the other reactive. As players develop they, whether consciously or not, sort themselves into positional groups partially based on their preferred mindset (alongside their natural skills and physical attributes). Some need the comfort of control, while others thrive on guessing their opponents’ next move.
Pitchers and catchers fall in the proactive category, selecting pitch types and locations to best baffle hitters. Position players react both at the plate and in the field. On the basepaths, the roles reverse. Runners make the active decision to advance, leaving pitchers and catchers to react. It’s an abnormal experience for everyone involved.
Season four of Stranger Things hit Netflix on May 27, 2022; around Opening Day of the 2023 major league season, you finally got “Running Up That Hill” by Kate Bush out of your head. (If you don’t watch Stranger Things, just know that the song features prominently throughout the show’s most recent season.) And as the new season dawned, baserunners went wild on the basepaths and all the chatter about running wormed “Running Up That Hill” right back into your brain. Much in the way the show revived a song from the 1980s, changes to MLB’s rules regulating base sizes and pitcher disengagements revived ‘80s-esque stolen base rates. Read the rest of this entry »
Whether there is a luddite rebellion, a scouting counter-revolution, or some other attempt at rolling back the technological advances the game has seen in recent years, last week I detailed why it makes sense for pitchers to adjust to new technology right now. Sure, hurlers could wait for a tech nullification or, in its absence, a new kind of tech to level the playing field, but as things stand, the scales are tipping.
Throughout the pitch-tracking era, hurlers have stood to benefit more than hitters from analytics thanks to highly customizable pitching plans. In recent years, motion-capture systems have helped optimize pitchers’ mechanics in addition to their repertoires. But this year, a significant number of teams have unearthed another use of limb-tracking software: in-game pitch tipping. Essentially, machine learning identifies subtle differences in muscle activation in real-time, typically due to different grips across pitch types, while the pitcher is still holding the ball in his glove. After a quick relay system, that information reaches the field, and the batter can then look to the dugout or a base coach for some indication of what’s coming.
To counteract this, I suggested pitchers mix up their wrist action and finger pressure right before release; by the time the KinaTrax systems pick up what’s going on, it’ll be too late. Sadly, I don’t have access to metrics like breadth of wrist action and finger pressure relative to grip, so I decided to come up with my own way of identifying pitchers who are already doing what I would recommend. I theorized that spin axis could be a proxy for grip, since the point about which a thrown baseball spins is heavily reliant on how the pitcher holds it. The direction and magnitude of movement is also closely related to spin axis, but there isn’t a one-to-one correspondence, so I hypothesized that pitchers with wide variability in movement despite minimal spin axis variability could be adding some extra mustard after separation. Read the rest of this entry »
Last week, Justin Choi wrote a fascinating article about sinkers. You should read it, because Justin’s stuff is great, but I’m going to summarize it here because I want to riff on it a little bit. In essence, Justin pointed out what we all kind of knew but didn’t talk about much: sinkers are much better against same-handed batters. Teams have caught on, and they’re changing usage accordingly.
Here’s a great chart from that article: the percentage of all right-handed sinkers that are thrown to right-handed batters:
That’s pretty straightforward: pitchers are increasingly using sinkers only when they have the platoon advantage. Here’s another way of looking at it: the percentage of sinkers among all pitches thrown by righties to lefties, league-wide:
In plain English, pitchers have stopped throwing sinkers when they’re faced with opposite-handed batters. Meanwhile, they’re throwing right/right sinkers as frequently as ever:
Those two charts hardly look like the same pitch, and in fact they aren’t really. Righty pitchers are actually playing two slightly different games: they’re pitching to same-handed batters and separately pitching to opposite-handed hitters. The object of both games is to get the batter out, so it’s not like the games are that different, but it’s inconceivable that the same pitches would be best against both sides. Read the rest of this entry »
The context behind the phrase “pitch tipping” has grown richer every year. Sure, the basic principle still holds: a pitcher is “tipping” when they’re providing some indication of their upcoming offerings. It’s just that opponents can glean such “tips” through a continuously expanding network of avenues. Previously, the only [clears throat] legal way to do so was when a second-base runner or base coach picked up on a catcher’s signs, or a starting pitcher’s tendency to wind up differently for a fastball or a breaking ball. Then, with the advent of PITCHf/x and later Trackman and Hawkeye, analysts and machine learning algorithms could search for tips to cue their hitters — when Pitcher A throws from a higher release point, there’s usually a fastball coming; when he shortens up his stride, there will probably be a breaking ball.
Next, the Trajekt pitching robots made it so that not only could coaches convey these cues to their hitters, but they could demonstrate how to use them to their advantage in real time. Integrating near-perfect trajectory replication with video of each pitcher’s windup, a pitcher facsimile completes their follow through at a mobile slot — adjustable in three dimensions for different release points and extensions — from which a batting practice baseball is launched. Still, pitchers can make in-game adjustments and at least avoid falling prey to the Trajekt machine for one start at a time, and the use of PitchCom makes it harder for runners and coaches to become privy to signs in-game. Maybe all of that can at least spare the pitcher an inning?
Now, I’m not so sure. Last week, Sports Illustrated’s Tom Verducci described team executives and coaches who are spending more time combatting their hurlers’ tipping than ever before. That’s because of markerless motion capture systems installed in as many as 15 big league ballparks. There are supposedly safeguards against using these KinaTrax systems for sign stealing, safeguards that dovetail with PitchCom’s effects, but the cameras go far beyond their intended purpose of preventing injury and sharpening up mechanics. Verducci describes an example, relayed to him from a front office executive: pitch grip influences which forearm muscles activate and how much they activate, even while the ball is still in the pitcher’s glove; once analysts or machine learning algorithms match each flexion pattern to a particular pitch type, that information goes straight to the dugout, and then to the hitters. Read the rest of this entry »
As a measure to improve baseball for the average fan — or even the decidedly non-average fans who frequent our pages — I think the pitch clock has been a resounding success. Trimming almost half an hour from the length of games hasn’t diminished baseball itself, with the cutting room floor mainly littered with the things that take place in between the action. Now, you can argue that we’ve also eliminated some of the dramatic tension from crucial situations in important games. But for every high-stakes matchup between two great players in a big moment, there were a multitude of unimportant ones stretched out endlessly by a parade of uniform readjustments and crotch reconfigurations. I enjoy having a leisurely Campari and soda with a friend while waiting for dinner, but I certainly don’t want to do that for every meal, and if I could chop down cocktail hour to get my food more quickly, I’d happily find other moments for social bonding.
Of course, game length isn’t the only consideration when assessing the pitch clock. I’m frequently asked in my chats if I think a given pitcher’s underperformance relative to expectation can be attributed to the clock. It can’t feel great to do a job for a number of years and suddenly experience such a monumental change in how you go about executing it. Steve Trachsel ain’t punching no time clock!
Another big question is whether the pitch clock, which can result in mechanical changes, could have an effect on injuries, a subject Will Sammon, Brittany Ghiroli and Eno Sarris explored for The Athletic after a high injury rate in April. While we obviously don’t have enough data to reach a verdict on the long-term effects of the clock (and things like Tommy John surgery count are still going to involve relatively small samples), as we near the halfway point of the season, we do have enough information to look at how the data are shaking out and arrive at some kind of preliminary conclusion about what’s going on. Read the rest of this entry »
One of the strange things about projecting baseball players is that even results themselves are small sample sizes. Full seasons result in specific numbers that have minimal predictive value, such as BABIP for pitchers. The predictive value isn’t literally zero — individual seasons form much of the basis of projections, whether math-y ones like ZiPS or simply our personal opinions on how good a player is — but we have to develop tools that improve our ability to explain some of these stats. It’s not enough to know that the number of home runs allowed by a pitcher is volatile; we need to know how and why pitchers allow homers beyond a general sense of pitching poorly or being Jordan Lyles.
Data like that which StatCast provides gives us the ability to get at what’s more elemental, such as exit velocities and launch angles and the like — things that are in themselves more predictive than their end products (the number of homers). StatCast has its own implementation of this kind of exercise in its various “x” stats. ZiPS uses slightly different models with a similar purpose, which I’ve dubbed zStats. (I’m going to make you guess what the z stands for!) The differences in the models can be significant. For example, when talking about grounders, balls hit directly toward the second base bag became singles 48.7% of the time from 2012 to 2019, with 51.0% outs and 0.2% doubles. But grounders hit 16 degrees to the “left” of the bag only became hits 10.6% of the time over the same stretch, while toward the second base side, it was 9.8%. ZiPS uses data like sprint speed when calculating hitter BABIP, because how fast a player is has an effect on BABIP and extra-base hits. Read the rest of this entry »
