The Dodgers took two out of three from the Royals this weekend in Los Angeles, but they suffered a pair of losses that can’t help but prove costly, as injuries felled two of the game’s best players. On Saturday, Yoshinobu Yamamoto left his start after just two innings due to what was initially described as triceps tightness but was later diagnosed as a rotator cuff strain. On Sunday, Mookie Betts suffered a fracture after being hit on the left hand by a 98-mph fastball. Neither injury is season-ending, but both players figure to be out for several weeks.

Yamamoto’s problems are traceable to his June 7 start against the Yankees. He was brilliant in that outing, shutting out the Bronx Bombers on two hits and two walks while striking out seven in a game that remained scoreless until the 11th inning, when Teoscar Hernández’s two-run double proved decisive. Perhaps owing to the adrenaline that comes with pitching in a playoff-like atmosphere, the 25-year-old righty’s four-seam fastball averaged 97.0 mph that night, 1.5 mph above his average in his first season since coming over from Japan after signing a 12-year, $325 million deal last December. He threw his 17 fastest four-seamers and eight fastest sliders while throwing a season-high 106 pitches; it was his fourth straight outing of at least 100 pitches after topping out at 99 in his first nine turns.

Because Yamamoto experienced soreness in his triceps in the wake of that start, the Dodgers pushed back his next outing from Thursday to Saturday; instead, he threw a bullpen on Thursday but did not experience any additional soreness. On Saturday, he did experience some discomfort while warming up, but “it was not that serious at that point,” as he later said through a translator according to the Los Angeles Times‘ Mike DiGiovanna. He told pitching coach Mark Prior after his warmup, “I don’t feel 100%. I don’t feel frisky, but I feel fine.” Read the rest of this entry »

Rick Kranitz has seen a lot of good changeups over the years. A minor league pitcher in the Milwaukee Brewers system for five seasons beginning in 1979, he joined the coaching ranks in 1984 and has been tutoring hurlers ever since. As noted when I talked pitching with him for FanGraphs three years ago, “Kranny” has served as the pitching coach for multiple big league teams, including the one he joined in 2019, the Atlanta Braves.

Unlike our 2021 interview, which covered a variety of pitching topics, this one focuses exclusively on one offering. I sat down with Kranitz to talk changeups when the Braves visited Boston earlier this month.

———

David Laurila: I want to ask you about a pitcher you were with 40-plus years ago, a guy who had a great changeup.

Rick Kranitz: “You must be talking about Greg Maddux.” Read the rest of this entry »

Of all 193 relief pitchers with at least 20 innings pitched this season, exactly three have thrown four distinct types of fastballs a minimum of 20 times each: Reed Garrett, Chris Martin, and Cole Sands. They all have one non-fastball offering, but none of them throw it more than a quarter of the time. Justin Choi wrote recently about the strategic options available to pitchers with more than one fastball, but four? Four whole fastballs? These guys feel like doomsday preppers getting ready for some apocalyptic scenario where money is now worthless and fastballs are the new currency.

But anytime a new strategy pops up in baseball, it’s worth checking to see if the outliers are onto something others should attempt, or if their “one weird trick” to pitching works only for them. Shoot, maybe it doesn’t even work for them all that well. Regardless, we’re gonna get to the bottom of what’s going on with these pitchers and all the fastballs they’re hoarding.

Reed Garrett

Garrett has thrown 34.2 innings for the Mets so far this season, posting a 3.12 ERA and a 3.17 FIP. He’s struck out 37% of the batters he’s faced and walked 12%. His performance this year has earned him an ERA- of 81, firmly better than average. What the averages aren’t telling you is that Garrett started the season with a 0.57 ERA in March and April, a ridiculous run that earned him a full breakdown on his evolution from last season by Ben Clemens on April 23. But that April ERA had to buy new pants after swelling to 6.08 in May. His performance has regressed somewhat in June, settling somewhere between those extremes. The current version of Garrett is probably more representative of what the Mets should expect from him moving forward.

The table below shows a breakdown of Garrett’s pitch repertoire with the usage and a few metrics for evaluating each offering (run value per 100 pitches thrown, xwOBA, Stuff+, and Location+). The two most common fastball types (four-seamers, sinkers) that most pitchers feature at the center of their arsenals are the pitches he throws the least. But the metrics linked to Garrett’s outcomes — either actual outcomes (RV100) or expectations based on the characteristics of the outcomes (xwOBA) — agree with his decision to de-emphasizing those pitches. They like Garrett’s four-seamer the least, even though it has his highest velocity and second best Stuff+. The pitch’s Location+ score reveals its critical flaw: a lack of command. Stuff+, RV100, and xwOBA agree that his sweeper and splitter are his two best pitches. Based on usage, Garrett agrees with that assessment.

His pitches mostly hover around league average in terms of individual characteristics, but the sweeper and splitter are both a tick or two harder than average and generate a bit more spin leading to more horizontal break, which is likely why Stuff+ likes them more than the rest of Garrett’s arsenal.

He makes the most of middling pitches by playing them off one another. The sweeper and cutter mirror the spin direction of the sinker and the splitter. As a result the pitches look similar out of the hand but fork in four different directions as they approach the plate to keep the hitter guessing (see movement plot below). So even if hitters guess the horizontal direction correctly, they’ve still got two similarly spinning pitches that fan out vertically as they approach the plate.

Garrett deploys all of his pitches no matter the handedness of the hitter, but he does vary the flavor of his approach. To lefties, Garrett likes to fill the zone with his cutter and dangle the splitter down and away when looking for a chase. To righties, he keeps the hitter off balance by throwing the sweeper to a variety of locations, but then comes down and inside at varying speeds with the splitter and the sinker.

The flowchart below gives us an idea of Garrett’s sequencing habits. He tends to start hitters with a cutter or sweeper. Once ahead in the count, he’s more likely to play around on the periphery of the zone with his sweeper and splitter, whereas while behind in the count he rolls with the four-seamer and cutter as more zone-friendly options. The wOBA values for plate appearances passing through each given count indicate the approach works well in early counts and with two strikes, but not as well when the count forces him back into the zone, in part because his four-seam command limits his ability to actually hit the zone with that pitch when circumstances demand it.

Here’s a representative example of how hitters respond to Garrett’s two-strike splitter.

Looking at swing metrics by pitch type, each pitch adds a valuable tool to his kit. The splitter is Garrett’s best combo play for inducing swings (56% swing rate) without courting disaster. The pitch owns his best swinging-strike rate (30%) and second lowest hard-hit rate (20%) when batters do connect. He gets batters to swing at 74% of the sinkers he throws in the zone, he uses the sweeper to induce weak contact (17% hard-hit rate), and turns to the cutter to mix things up. The four-seamer is the weak link in the chain so long as it keeps taking the scenic route to the catcher’s mitt.

Chris Martin

Martin has thrown 21.1 innings for the Red Sox in 2024, logging a 4.22 ERA with a 3.90 FIP. He’s struck out 28.2% of the batters he’s faced while walking just 2.4% of them. He has been on the IL since June 5 while proactively seeking help with anxiety.

Again, we’ll start with a synopsis of each pitch he throws according to the value metrics. Stuff+, RV100, and xwOBA all like his splitter best. The pitch is very similar to Garrett’s splitter from a velo/movement/spin perspective, but he doesn’t throw it nearly as much. His four-seamer is his next best pitch by RV100 and xwOBA, but fourth best by Stuff+. However, he locates it well enough to still get results. Martin’s cutter is his consensus third-best pitch, striking a balance between stuff and command to get the job done. Like Garrett, Martin’s non-fastball pitch is a sweeper, but unlike Garrett, he throws it so infrequently that it’s hardly worth discussing.

His pitch characteristics all hover around average, thrown maybe a tick or two harder, but with slightly less spin and therefore less movement. What helps overcome somewhat middling profiles is a distinct release point created by his long levers. Though his delivery is composed of a pretty standard three-quarters-ish arm slot, the arm attached to his 6’8” frame allows him to release the ball several inches higher and farther to his right than other pitchers throwing from a similar slot.

Rather than mirroring the spin on his offerings like Garrett, Martin takes a different approach to cultivating deceit. The puzzle for his hitters is more akin to spotting the difference between two nearly identical photos. All of Martin’s pitches spin in a similar direction, and his four-seamer, sinker, and cutter do so at almost the same spin rates. Where they differ is in the amount of active spin, or the amount of spin contributing to the pitch’s movement. The four-seamer, as one might expect, has the most active spin and the most rise. The sinker has a little less active spin and creates more horizontal break and more drop. The cutter drops in a comparable fashion to the sinker, but refuses to follow his fellow fastballs and break toward the third base side. Then there’s the splitter that spins at a much slower rate and with less active spin, which translates to roughly the same amount of horizontal movement as his four-seamer, but with even more drop than the sinker. Yet another carbon copy, but with a small but crucial edit.

Martin uses the same theory to guide his approach to both righties and lefties: Fill the zone with the primary fastball(s), use one of the secondary fastballs as a threat inside, and pepper the bottom of the zone with splitters. Against right-handers the four-seamer and cutter are the pitches he consistently throws to all parts of the zone and the sinker backs the hitter off the inner half of the plate. Against left-handers, Martin stays away from the sinker, so the cutter becomes the weapon he aims inside, while the four-seamer and the splitter maintain their existing roles.

The job of each fastball is further etched in stone by Martin’s sequencing, visualized below. He starts an overwhelming majority of hitters with the four-seamer or cutter and sticks to those zone-friendly pitches if he falls behind in the count. But if he gets ahead, Martin starts mixing in the splitter and sinker. His results tend to be better if he gets to those splitter/sinker counts, but it’s unclear whether that’s because of the effectiveness of those pitches or because he gets too predictable in unfavorable counts.

The swing metrics indicate Martin’s cutter is his best option for getting swings (55% swing rate) that lead to either strikes (13% swinging-strike rate) or weak contact (27% hard-hit rate). The splitter is his overall best bet for a swinging strike (19%), but when hitters do make contact, it yields the highest hard-hit rate (70%). The sinker is most effective when thrown in the zone because it has the lowest out-of-zone swing rate (18%) and in-zone contact rate (78%) compared to Martin’s other offerings. And avoiding contact is key, since the sinker has the second highest hard-hit rate (67%) of the bunch.

Cole Sands

Sands has pitched 32 innings for the Twins this season. Those innings have amounted to a 4.22 ERA and a 3.30 FIP. His strikeout rate sits at 28% and his walk rate is a measly 3%. Sands’ season trajectory mimics Garrett’s: on a rocket to the moon in April, a crash landing in May, and now back up and cruising at altitude in June. At his peak, Sands was striking out Shohei Ohtani on three pitches, and Minnesota was considering stretching him out to start while managing injuries in the rotation; now he’s settled into a multi-inning relief role.

Digging into Sands’ repertoire via the pitch evaluation metrics, his cutter, curveball, and splitter all clock in right around average according to Stuff+, but RV100 favors the four-seamer and hates the curve and split. Comparing the curveball’s xwOBA (.305) to its wOBA (.372) suggests the pitch’s actual outcomes have been a bit unlucky compared to what’s expected based on the batted ball characteristics, which in turn is likely deflating its RV100. Meanwhile the four-seamer and sinker both have better wOBAs when compared to their xwOBAs, suggesting some good luck has swung their way and their RV100s might be a little full of themselves. Luck doesn’t explain the metrics’ diverging opinions on the splitter, suggesting something is amiss with Sands’ execution. Hopefully, this contradiction will untangle itself as we proceed.

In terms of the movement profile broken down in the table below, Sands, like Garrett, mirrors the spin of his breaking ball relative to the four-seamer, sinker, and splitter in an attempt to disguise their true identities until it’s too late for the hitter to react. And concealing those identities is necessary because, as with the other two pitchers, Sands’ pitch characteristics are far more average than overpowering. The furthest he deviates from average is with his extension, but unfortunately he deviates in the wrong direction. His 5.8-foot extension puts Sands roughly six to eight inches below league average. Releasing the ball farther from home plate gives the hitter more of a chance to identify the pitch’s trajectory, which likely explains the lower Stuff+ scores relative to what Garrett and Martin receive for comparable pitches. And while we’re talking pitch trajectory, the extra couple inches of drop on his splitter relative to an average right-handed offering of the pitch might be too much of a good thing; at times it dives too far, too quickly to really tempt hitters.

How the pitches move relative to one another is basically a hybrid of what we’ve seen so far from Garrett and Martin. The fastballs land on the movement plot in roughly the same orientation as the other two, aside from being stretched more vertically. Sands’ curveball operates similarly to Garrett’s sweeper, just with more drop.

Like Martin, Sands doesn’t throw his sinker to lefties, but beyond that omission, Sands attacks hitters in the exact same manner regardless of handedness. He aims to fill up the zone with his four-seamer, works arm side with the cutter and sinker, and keeps the ball down and/or to the glove side with the splitter and curve.

Sands mostly sticks to the standard sequencing playbook, but he’ll reach for any of his non-splitter offerings to begin a plate appearance. If he gets ahead, expect a heavy mix of splitters and curveballs; if he falls behind, expect him to thrown mostly cutters and four-seamers. His adequate command keeps him competitive, since even after falling behind, the average outcomes remain respectable and in line with the more favorable counts.

The swing metrics suggest Sands’ cutter is his best option for inducing weak contact (51% swing rate, 32% hard-hit rate), the four-seamer has the lowest in-zone contact rate (80%) to pair with the second highest in-zone swing rate (71%), and the curveball is best for forcing swings out of the zone (35%) that lead to either a strike (14% swinging-strike rate) or weak contact (25% hard-hit rate).

***

With the four-fastball approach to relief pitching now fully dissected on the lab table before us, I can’t truly say we’ve discovered the next big thing that pitchers everywhere will be rushing to replicate. Though Garrett, Martin, and Sands are the only three relievers doing this out of almost 200, their approach is not as novel as those numbers suggest. What they’re actually doing is leaning on all of the classic pitching fundamentals: changing the hitter’s eye level, attacking the zone to get ahead in the count and then make the hitter chase, varying speeds, varying locations, keeping the hitter off balance. Most relievers execute these fundamentals using one or two overpowering pitches, or in lieu of dominant stuff, they cobble together a few crafty junk pitches. Garrett, Martin, and Sands pitch as if they were junkballers, but instead of throwing knuckleballs or Bugs Bunny changeups, they take their collection of middling fastballs and deploy them as junkballs. They mix and match movement profiles and velocities so hitters can’t sit on certain pitches or locations. They do all the same stuff every pitcher does; they just dress it up a little different. Which in and of itself is novel enough to still be impactful. After all, 10 Things I Hate About You is a singularly great movie, but it’s also a classic Shakespeare play, just dressed up a little differently.

Matt Tuiasosopo has fond memories of his 2013 season with the Detroit Tigers. An October swing of the bat is responsible for one of the few unpleasant memories. Now the third base coach for the Atlanta Braves, Tuiasosopo was watching from the bench when David Ortiz blasted an eighth-inning, game-tying grand slam, a play that saw Torii Hunter tumble into Fenway Park’s home bullpen in a futile attempt to snare the drive. It was the signature moment of an epic ALCS Game 2 that the Red Sox went on to win, and a catalyst to their eventual capturing of the series.

What was it like to be on the wrong side of such a memorable event, and how does he look back at it now that a decade’s worth of water has passed under the bridge? I asked Tuiasosopo those questions when the Braves visited Boston earlier this month.

“That was an intense moment, “ recalled Tuiasosopo, who while not on Detroit’s ALCS active roster was in uniform for the games. “The whole stadium was going nuts. It was really loud. Of course, my first concern was Torii, because he flew over that wall. When he got up, it was ‘Thankfully he’s okay.’ I mean, there were a lot of different emotions.

“It obviously wasn’t fun,” continued Tuiasosopo. “At the same time, as a baseball fan it was, ‘Big Papi against one of our best relievers — Joaquín Benoit was big for us that season — and there was also everything that happened for the city of Boston [the Marathon bombing] that year. The moment was special, even though it sucked on our end.” Read the rest of this entry »

Ben Lindbergh and Meg Rowley answer listener emails about Kyle Schwarber batting leadoff, teams paying their players not to do in-game, on-field interviews, the phrase “potential World Series preview,” a team purchasing and privatizing a valuable public baseball website, a player who can’t hit anything except grand slams, a player who homers in every game he plays but is usually injured, Justin Verlander’s Hall of Fame plaque cap, and what constitutes a “teammate” (plus a real-time reaction to the Astros releasing José Abreu). Then (1:06:33) Ben meets major leaguers Daniel Schneemann and Jamie Westbrook and (1:17:58) briefly reacts to news about MLB disciplining “perfect” umpire Pat Hoberg for gambling.

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Among the panoply of stats created by Statcast and similar tracking tools in recent years are a whole class of stats sometimes called the “expected stats.” These types of numbers elicit decidedly mixed feelings among fans – especially when they suggest their favorite team’s best player is overachieving – but they serve an important purpose of linking between Statcast data and the events that happen on the field. Events in baseball, whether a single or a homer or strikeout or whatever, happen for reasons, and this type of data allows us to peer a little better into baseball on an elemental level.

While a lucky home run or a seeing-eye single still count on the scoreboard and in the box score, the expected stats assist us in projecting what comes next. Naturally, as the developer of the ZiPS projection tool for the last 20 (!) years, I have a great deal of interest in improving these prognostications. Statcast has its own methodology for estimating expected stats, which you’ll see all over the place with a little x preceding the stats (xBA, xSLG, xwOBA, etc). While these data don’t have the status of magic, they do help us predict the future slightly less inaccurately, even if they weren’t explicitly designed to optimize predictive value. What ZiPS uses is designed to be as predictive as I can make it. I’ve talked a lot about this for both hitters and for pitchers. The expected stats that ZiPS uses are called zStats; I’ll let you guess what the “z” stands for!

It’s important to remember that these aren’t predictions in themselves. ZiPS certainly doesn’t just look at a pitcher’s zSO from the last year and go, “Cool, brah, we’ll just go with that.” But the data contextualize how events come to pass, and are more stable for individual players than the actual stats. That allows the model to shade the projections in one direction or the other. And sometimes it’s extremely important, such as in the case of homers allowed for pitchers. Of the fielding-neutral stats, homers are easily the most volatile, and home run estimators for pitchers are much more predictive of future homers than are actual homers allowed. Also, the longer a pitcher “underachieves” or “overachieves” in a specific stat, the more ZiPS believes the actual performance rather than the expected one.

One example of the last point is Tyler Anderson. He has a history of greatly underperforming what ZiPS expects, to the extent that ZiPS barely believes the zStats at this point (more on Anderson below). Expected stats give us useful information; they don’t conjure up magic.

What’s also interesting to me is that zHR is quite surprised by this year’s decline in homers. There have been 2,076 home runs hit in 2024 as I type this, yet before making the league-wide adjustment for environment, zHR thinks there “should have been” 2,375 home runs hit, a difference of 299. That’s a massive divergence; zHR has never been off by more than 150 home runs league-wide across a whole season, and it is aware that these home runs were mostly hit in April/May and the summer has yet to come. That does make me wonder about the sudden drop in offense this year. It’s not a methodology change either, as I re-ran 2023 with the current model (with any training data from 2023 removed) and there were 5,822 zHR last year compared to the actual total of 5,868 homers.

Let’s start the pitchers off with the summary data. Read the rest of this entry »

One advantage of living in an age where the wealth of human knowledge is at one’s fingertips is that no curiosity need go unsatisfied. I was just sitting around wondering idly about the relationship between how hitters get pitched and how well they do against certain types of pitches. So I ran a couple of Baseball Savant searches and played around in Excel over lunch and ended up with something that would surely have made Henry Chadwick soil his trousers.

Which probably overstates the impact of these findings, such as they are. One of my major takeaways is that Aaron Judge is a preposterously good hitter, which I feel like we all knew going in. Still, it’s a fun journey to go on, so let’s take it together. Read the rest of this entry »

Welcome to another edition of Five Things I Liked (Or Didn’t Like) This Week. I was under the weather late last week, which was not fun at the time. On the bright side, it gave me plenty of time to sit on the couch and watch baseball. To be fair, that’s what I do even when I’m not sick, but this time I had a good excuse. Baseball cooperated, too: There were some elite series and fun matchups over the past week. Stars facing off? We’ve got that. Baserunning hijinks and defensive lapses? You bet. Beleaguered backups bashing baseballs belligerently? Absolutely, alliteration and all. Shout out to Zach Lowe – now let’s get down to business.

Read the rest of this entry »

Ben Lindbergh and Meg Rowley banter about this season’s giant cluster of wild card contenders, the potential for an inactive trade deadline, the role the White Sox will play, whether the compression of the standings is good or bad for baseball, and expanded-playoffs incentives. Then (33:46) they talk to NPR producer Alana Schreiber, creator and executive producer of the NPR/MLB podcast Road to Rickwood, about the history of the park that’s hosting its first MLB game this month, plus a postscript (1:17:48) about a postseason mid-PA pitching change.

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Well, it’s Friday, and over the past couple weeks, I have crunched so, so many bat tracking numbers. I wrote about them last week and then again on Wednesday, and the effort required to write those two articles has worn me down into a smaller, duller baseball writer than I was back in May. Today, I’d like to look at the lighter side of bat tracking. In particular, I’m interested in the lower limits of squared-up rate. Before we get into it, though, I need to make a detour and speak directly to the industrious baseball savants over at Baseball Savant who made all of this pitch-, ball-, player-, and bat-tracking possible.

Dear Baseball Savant baseball savants,

I love you. You are doing God’s work. You are making known the unknown, shining the light of truth into the dark corners of the world, and I would gladly bake brownies for you any day of the week. However, after a month of bat tracking data, it’s time that we acknowledge a solemn truth: You probably need to shuffle around a few names. Here’s the big one: Squared-Up Rate should actually be called Barrel Rate.

I imagine you would have called it that had you not already given the name away. After all, it’s right in the definition: A squared-up swing “can only happen on the sweet spot of the bat.” That’s the barrel of the bat, though Sweet Spot Rate is taken too. You currently classify a Sweet Spot as any ball hit at an optimal launch angle, whereas a Barrel is a hard-hit ball hit at an optimal combination of velocity and launch angle. But neither of those terms implies a particular trajectory. Sweet Spot Rate should be shifted to Lift Rate and Barrel Rate should be shifted to Launch Rate. That makes them more accurate and allows Squared-Up Rate to shift over to Barrel Rate where it belongs. Everybody wins.

I understand that this would be confusing at first, but that’s ok, baseball savants. We’ll get used to it. We got used to xwOBACON. You just changed Best Speed to EV50 and nobody so much as batted an eye. Besides, it’s not as if you did anything wrong. It was totally reasonable for you to call those balls Barrels a few years ago. How could you have even imagined you’d get to this point, measuring bat speed with cameras that capture 500 frames per second? But now you know better.

Hugs and kisses,

Davy

PS: Please start tracking the sprint speed of turtles (and any other animals) that wander onto the field.

PPS: I was serious about the brownies.

Ok, end of detour. For each batted ball, the respective speeds of the pitch and the bat make for a maximum possible exit velocity. Statcast calculates the squared-up percentage by dividing the actual exit velocity by that maximum possible exit velocity. Ben Clemens published a rough version of the formula on Tuesday:

Squared-Up Percentage = EV / ((Bat Speed x 1.23) + (0.2116 x Pitch Speed))

Because it’s just a percentage, there’s no minimum bat speed or exit velocity required to square up a ball. You can square up a ball even if your bat is barely moving. In theory, you could square up a ball if your bat were moving backward. You can square up a bunt. Here’s Masyn Winn doing just that against the Brewers. Not only did he produce the slowest squared-up ball in recorded history, he also singled and loaded the bases for the Cardinals on the play.

The 94.6-mph pitch contacted Winn’s bat, which was moving at 4.8 mph, resulting in a 20.9-mph batted ball that was 81% squared up. More importantly, after Winn squared up the ball so beautifully, multiple people fell down. First, pitcher Freddy Peralta started to make a diving play, then thought better of it and awkwardly spiked his knee into the turf. He next attempted to snare the ball on a short hop, but with its strange combination of spin and velocity, the seemingly sentient sphere took a perpendicular bounce away from him. Next, Peralta unleashed an off-target throw to first, which understandably frightened first base umpire Alan Porter enough that he toppled backward, only to pop up and make the correct call like a champion.

I watched every squared-up ball that was hit below 70 mph. The best part of that exercise by far was admiring the swings. They are a truly gorgeous collection of excuse-me swings, and as it turns out, they can all be sorted out according to a spectrum. On the left is The Swing That Never Really Got Started. In the middle is The Swing That Got Interrupted Before It Was Finished. And on the right is The Swing That Wasn’t Supposed To Happen in the First Place. Those poles are roughly correlated to spray angle, and in the supercut below, I’ve tried to put them in order as they go from one end of the spectrum to the other.

To be sure, I saw plenty more silly squared-up balls. I’ve seen more players fall down or fire the ball wildly into the stands. I’ve seen a ball bounce off Jonathan India’s bat, then the gloves of two different fielders. I’ve seen Nick Madrigal get credit for squaring the ball up on a 63.6-mph groundout that looked for all the world like every other Nick Madrigal batted ball.

All the same, after watching all these squared-up squibbers and squared-up swinging bunts, I hope you can begin to see the beauty of the statistic that should be called barrels. There’s something moving about the idea that there’s no limit to pure contact. It’s possible to square up the ball perfectly while touching it as lightly as a feather. It’s possible to square up the ball perfectly even if that’s the last thing on earth you want to happen. No matter how mangled your swing, perfection is always attainable.

Sure, squaring up a baseball means Oneil Cruz stress testing a center-cut fastball’s 108 stitches in the most brutal fashion imaginable, and it means Steven Kwan reaching out and slapping a changeup into shallow left field. Why shouldn’t it also mean Patrick Wisdom trying and failing to lay off a high inside pitch from a position player in a 17-0 game, chipping the ball toward the first baseman at 41.7 mph, throwing his head back in frustration, and then trudging off toward first base like a 5-year-old who just got told that if he didn’t march upstairs and take a bath this very instant, then there would be no dessert tonight, mister?

Bunts aside, that is the weakest squared-up ball ever recorded and I love it. Wisdom squared it up at 92% and so, so wished he hadn’t, which just makes it all the more perfect. In this age of seemingly infinite velocity and Edgertronic pitch design, shouldn’t we celebrate anyone who manages to square up the baseball, even if they did so accidentally?