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.
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.
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.
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.
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?
Below is an analysis of the prospects in the farm system of the Atlanta Braves. Scouting reports were compiled with information provided by industry sources as well as my own observations. This is the fourth year we’re delineating between two anticipated relief roles, the abbreviations for which you’ll see in the “position” column below: MIRP for multi-inning relief pitchers, and SIRP for single-inning relief pitchers. The ETAs listed generally correspond to the year a player has to be added to the 40-man roster to avoid being made eligible for the Rule 5 draft. Manual adjustments are made where they seem appropriate, but we use that as a rule of thumb.
A quick overview of what FV (Future Value) means can be found here. A much deeper overview can be found here.
All of the ranked prospects below also appear on The Board, a resource the site offers featuring sortable scouting information for every organization. It has more details (and updated TrackMan data from various sources) than this article and integrates every team’s list so readers can compare prospects across farm systems. It can be found here. Read the rest of this entry »
Welcome back to Top of the Order, where every Tuesday and Friday I’ll be starting your baseball day with some news, notes, and thoughts about the game we love.
It’s not exactly uncommon for league champions to struggle the following year. The most extreme versions of this are the Marlins, who sold off just about all of their good players after winning the World Series in 1997 and in 2003. The Nationals, who still haven’t had a winning season since their World Series title in 2019, are a more recent example. But, usually, at least one of the two teams to play in the previous World Series has a strong follow-up season. In fact, over the first 29 seasons of the Wild Card era, only twice have both league champs from the same year missed the playoffs the next season; interestingly, those two years came back to back, in 2006 (White Sox and Astros) and 2007 (Cardinals and Tigers). That’s why it’s quite jarring to see both the Rangers and Diamondbacks under .500 entering play this weekend.
While both teams won on Thursday, they’re not in great position right now. The Diamondbacks are 8.5 games out of first place with a 33-36 record (though they’re just a game out of the final NL Wild Card spot); the Rangers’ 33-35 record has them five games behind the first place Mariners and 3.5 games away from a wild card berth. The sluggish start gave the reigning world champs just a 19.2% chance of making the playoffs entering Thursday; Arizona’s odds weren’t that much better, at 27.9%.
Considering this, let’s look at what has gone wrong for each team and determine how they can avoid becoming the third pair of league champions in three decades to each fall short of returning to the postseason in their follow-up campaigns. Read the rest of this entry »
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 »
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