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.
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David Laurila: I want to ask you about a pitcher you were with 40-plus years ago, a guy who had a great changeup.
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 »
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.
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?
At any given point in the season, it’s not too hard to figure out which hitters are performing the best and which the worst — various leaderboards do a good job of that. But particularly when it’s early in the season and the samples are on the smaller side, it’s easy to miss when a slow-starting player has gotten it going, as his overall numbers may not be as eye-catching.
That isn’t exactly a new epiphany, but it’s one I was reminded of when writing about Oneil Cruz on Wednesday, and, to a lesser extent, when tracking Aaron Judge in the weeks before I finally wrote about his hot streak (which, remarkably, has continued). What may look like a stat line of fairly typical production can conceal some interesting developments or adjustments. Or maybe it’s just some positive regression.
With that in mind, I decided to take a look at players, such as Cruz, who started the season slowly but have come around more recently. I’ve used May 1 as the dividing line for creating my list, because the flipping of the calendar page is an obvious reference point, and in this case it’s still pretty close to the midpoint of the season to date; when I wrote about Cruz, for example, the Pirates had played 31 games before May 1 and 35 since. Read the rest of this entry »
Luke Raley is a big, strong man. The Seattle outfielder stands 6-foot-4, weighs 235 pounds, and spent much of his childhood in Ohio felling trees with a chainsaw. He’s got a huge arm, and he’s boasted a maximum exit velocity at or above the 90th percentile in three of the last four seasons. Former teammates have called him “a big ball of muscle” and said, “He kind of plays like a monster.” Just last night he launched a moonshot home run that reached an altitude of 104 feet. And yet somehow, if you Google the phrase luke raley feats of strength, this is all that comes up:
First of all, yes, Raley is married. He found out that he got traded to Seattle during his honeymoon, while playing pool volleyball. Second, there’s a pretty good reason that Raley’s strength doesn’t headline his search results: He’s more than just a beef boy. Raley has finesse. In fact, he’s currently tied with Jacob Young for the major league lead with five bunts for a base hit. While Young has a 35.7% success rate on his bunts, Raley is the only player so far this decade to bunt for at least five hits in a season while maintaining a 1.000 batting average on those bunt attempts. Want to guess who’s in second place? That would be 2023 Luke Raley, who went 5-for-6 in his bunt attempts. The big, strong man has a big, strong bunt game. Read the rest of this entry »
The Brewers always seem to have a good bullpen. They have an anchor at the top – either Josh Hader or Devin Williams – and a smattering of other arms behind them that complement what the team is doing. Historically, they’ve used those bullpen arms to back up the weaker members of their rotation as needed, while getting big chunks of innings from their top starters.
In 2024, things have gone differently – but not in the way you’d expect. Hader is gone. Williams is hurt. Abner Uribe, who began the season in a high leverage role, is in Triple-A after a disastrous start. Joel Payamps, who got some save opportunities after Uribe faltered, has been demoted to middle relief work. Naturally, Milwaukee has the fifth-best bullpen in baseball by WAR, the second-best by RA9-WAR, and the best by win probability added. They’ve thrown the most innings in baseball, to boot.
Even stranger, this might be their best bullpen unit in a while. You probably think of the Brewers as having a perennial top five relief corps without looking into the numbers. I know I did. But here are their finishes in a variety of metrics over the past five years:
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 hitter’s zBABIP from the last year and go, “Hey, sounds good, that’s the projection.” 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 actual homers allowed are. Also, the longer a hitter “underachieves” or “overachieves” in a specific stat, the more ZiPS believes the actual performance rather than the expected one.
A good example of this last point is Isaac Paredes. There was a real disconnect between his expected and actual performances in 2023 and that’s continued into 2024. But despite some really confounding Statcast data, ZiPS now projects Parades to be a considerably more productive hitter moving forward than it did back in March. Expected stats give us additional information; they don’t give us readings from the Oracle at Delphi.
One thing to note is that bat speed is not part of the model. The data availability is just too recent to gauge how including it would improve the predictive value of these numbers. It’s also likely that even without the explicit bat speed data, the model is already indirectly capturing a lot of the information bat speed data provides.
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. Read the rest of this entry »
There is no doubt the Milwaukee Brewers have outperformed expectations in 2024. Although they won the NL Central just last season and made the playoffs in five of the last six years, they were hardly postseason favorites on Opening Day. On the contrary, they were the only 2023 division winners that the majority of our staff did not pick to repeat as division champs; just four of the 25 participants in our preseason predictions exercise picked the Brewers to make the playoffs in any capacity. The only NL Central team with less support was the Pirates. Meanwhile, our playoff odds were only slightly more optimistic about Milwaukee’s chances. The Brewers had 18.1% odds to win their division and a 30.0% chance to make the postseason on Opening Day.
Sixty-seven games have passed between now and then, and over those 67 games, the Brewers have become the indisputable frontrunners in the NL Central. What once seemed like it would be the most closely contested division in the league – all five teams were projected to finish within 2.3 games of one another on April 14 – has become Milwaukee’s to lose. The Pirates, Cubs, Cardinals, and Reds are all smushed within half a game of one another, but the Brewers rest atop with a comfortable 6.5-game lead. Their divisional odds are up to 63.0%; their playoff odds, 78.6%. In the NL, only the three powerhouse clubs, the Phillies, Dodgers, and Braves, are more likely to play in October. Read the rest of this entry »
Oneil Cruz is a player of extremes. The 6-foot-7 shortstop — the tallest man ever to play the position regularly — doesn’t just have incredible bat speed and power, he can lay claim to the hardest-hit ball of the Statcast era, and he once held the record for the hardest throw by an infielder as well. But for as loud as his contact is, the frequency with which he makes it has been an issue, as he’s particularly prone to chasing pitches outside the zone. Defensive metrics don’t love him either. Yet he’s the kind of player you can’t take your eyes off, because when it all comes together, it’s a sight to behold — and gradually, it’s been coming together more frequently.
Case in point: Last week found Cruz in a prolonged funk, hitting just .151/.224/.283 in his previous 58 plate appearances dating back to May 15 while striking out 23 times (39.6%) in that span. After going 0-for-4 in last Tuesday’s series opener against the Dodgers, he collected a pair of hits the next night, including this three-run homer off Evan Phillips:
That’s a 462-footer into the Allegheny River, the longest homer of Cruz’s major league career by 25 feet, and the third splash hit of his career; he also had ones on September 6, 2022 and May 3 of this season. The 117.7-mph exit velocity on his shot off Phillips made it his hardest-hit home run to date by 0.2 mph, surpassing an August 28, 2022 dinger in Milwaukee. For both distance and exit velocity, he’s up there with the big boys; the homer off Phillips is the majors’ seventh-longest this year behind three from Aaron Judge (a 473-footer from May 9 being the longest) and ones by Mike Trout, Bobby Witt Jr. and Shohei Ohtani. Cruz’s homer is the fourth-fastest in exit velocity behind two by Giancarlo Stanton (a 119.9-mph shot from May 8 being the fastest) and one by Ohtani. He’s right there in flavor country when it comes to some of the new bat tracking metrics, second only to Stanton in average bat speed (78.0 mph) and fast-swing rate (74.6%); he’s below average in terms of his squared-up rate (23.1%) — that’s the rate at which he obtains at least 80% of the maximum exit velocity for that swing — but a respectable 15th in blast rate (16.2%), the rate at which he squares up balls on fast swings. Read the rest of this entry »
