Over the past month, smart people have been deciphering the relationship between swing length and pitch location in MLB’s new bat tracking data. If you’re looking at raw data, it’s hard to know whether someone has a long swing because they like inside pitches (Isaac Paredes) or because their swing is actually long and loopy (Javier Báez). In order to make solid contact with an inside pitch, the barrel needs to meet the ball out in front of the plate, which means that it will take a longer journey to the point of contact than it would to meet a pitch over the middle of the plate. Below is a breakdown of Luis Arraez’s swing length against fastballs. As you can see, even the king of the short swing gets long when he has to reach pitches up-and-in or down-and-away.

Some of this is as old as the game itself. It’s the reason pitchers throw fastballs up and in, where a necessarily longer, slower swing makes them harder to catch up with. Bat tracking has given us numbers to back up another intuitive part of the game: Swing length is positively correlated with bat speed, confirming that players who are short to the ball sacrifice bat speed for bat control and contact ability. Those two correlations, pitch location to swing length and swing length to bat speed, got me thinking about the launch angle revolution.

The launch angle revolution really got its hooks into Major League Baseball in 2015. That’s the year Joey Gallo and Kris Bryant debuted, and the year Justin Turner and Daniel Murphy fully turned themselves from contact hitters into power threats. In The MVP Machine, Ben Lindbergh and Travis Sawchick documented what Turner was thinking in 2013, the very first time he tried out the new approach for which teammate Marlon Byrd had been proselytizing. “I was thinking, I’m just going to try and catch the ball as far out as I can in batting practice,” Turner said.

Catching the ball out front often means pulling it, especially in the air. The league’s overall pull rate is roughly the same as it was in 2011, but as you can see from the chart above, its pull rate on air balls — the line drives and fly balls where hitters do damage — hit an all-time high in 2017 and then again in four of the next five years. The exact approaches can differ. “I’m going to be on the fastball and drive it to right center, and if I’m a little early on the slider I’ll catch it out in front,” Austin Riley told Eno Sarris last year. And as Ben Clemens has noted, hitters have increased their pulled balls in the air simply by choosing to attack pitches that lend themselves to being launched in that direction. But strictly speaking, there isn’t a huge inherent advantage to pulling the baseball. If you’re going to hit a long fly ball, it’s better not to hit it to straightaway center, where the fence is deeper and the fielders are better, but that’s equally true for both pulling the ball and going the opposite way. In a sense, pulling the baseball is just a side effect of catching the ball out in front. Read the rest of this entry »

I continue to find Statcast’s bat tracking data fascinating. I also continue to find it overwhelming. Hitting is so complex that I can’t quite imagine boiling it down to just a few numbers. Even when I look at some of the more complex presentations of bat tracking, like squared-up rate, I sometimes can’t quite understand what it means.

I’ll give you an example: when I looked into Manny Machado’s early-season struggles last week, I found that he was squaring the ball up more frequently when he hit grounders than when he put the ball in the air. That sounds bad to me – hard grounders don’t really pay the bills. But I didn’t have much to compare it to, aside from league averages for those rates. And I didn’t have context for what shapes of squared-up rate work for various different successful batters.

What’s an analyst to do? If you’re like me in 2024, there’s one preferred option: ask my friendly neighborhood large language model to help me create a visual. I had an idea of what I wanted to do. Essentially, I wanted to create a chart that showed how a given hitter’s squared-up rate varied by launch angle. There’s a difference between squaring the ball up like Luis Arraez – line drives into the gap all day – and doing it like Machado. I hoped that a visual representation would make that a little clearer. Read the rest of this entry »

Like many other nerds, I have devoted a lot of time to slicing and dicing Baseball Savant’s new bat tracking data over the last few weeks. And like many other nerds, I’m not entirely sure how we’ll end up using this wealth of new information. More time, more data, and more brain power is needed to wring out whatever sweeping new truths it may hold. I’m going to write about bat tracking data in a more focused way next week. There are a couple things I think are really interesting; not necessarily new information, but ways that bat tracking data can give us hard numbers for things that we’ve already learned. In this article, I’ll be a bit more scattershot. I’d just like to take you through how I’ve processed all the information that has come out over the last few weeks.

First off, bat tracking will give us new stats that stabilize more quickly than existing ones, as that’s how granular metrics that separate underlying skills from results tend to work. In smaller samples, exit velocity turned out to be a better predictor of overall batting performance than wRC+ or wOBA. Now we have swing speed, which in smaller samples turns out to be a better predictor of exit velocity. To wit, I pulled data from the first week of bat tracking, April 3 to April 9, and compared it to each player’s overall numbers this season. I eliminated any player with fewer than five plate appearances during the first week or fewer than 100 PA during the entire season, which left me with a sample of 295 players. It was no contest. Full-season exit velocity had a much stronger correlation to first-week swing speed (R = .60) than it did to first-week exit velocity (R = .40). It also predicted full-season hard-hit rate better than first-week hard-hit rate (R = .66 for swing speed, compared to R = .46 for hard-hit rate). If, after the first week, you want to know who’s going to hit the ball hard for the rest of the season, don’t look at exit velocity. Look at swing speed:

Read the rest of this entry »

Season one of One Tree Hill is a perfect season of television, and I will not be entertaining arguments to the contrary. In it we meet Nathan and Lucas Scott, the sons of hometown basketball hero, Dan Scott, who runs a local car dealership. Nathan was raised in the traditional nuclear family structure by Dan and his college sweetheart and wife. Lucas was raised in a single-parent household by his mother, Dan’s high school sweetheart. Despite sourcing their foundational genetic material from the same DNA pool, Nathan and Lucas are depicted at odds with one another in several key ways. Nathan is his father’s golden child and characterized as hyper competitive, entitled, and emotionally stunted; Lucas receives no acknowledgement from Dan and skews more intellectual, reserved, and empathetic. Both are super good at basketball and both crave the approval of their father. Nathan seemingly has it all, but presents as lonely and ill at ease in his environment. Lucas drew the short straw, but is mostly content and supported by several meaningful relationships.

The whole concept is a pretty straightforward exercise in nature vs. nurture, and if you haven’t seen One Tree Hill, don’t worry, I haven’t spoiled anything; this is all part of the show’s initial setup in the pilot episode. What the viewer is intended to puzzle out as the season unfolds is how much of Nathan’s arrogance and aggression is a reaction to his surroundings and how much is an inherent part of his character. And on the other hand, can Lucas, against his father’s wishes, learn to thrive in new surroundings as he steps into the spotlight of varsity basketball? Or is he more naturally suited to exist in the shadows?

I recently read almost six years of scouting reports, statistical breakdowns, and interviews covering two prospects from the 2018 MLB draft in an attempt to to understand the how and why of each player’s career arc. More on that later, but for now, I want to emphasize how much easier it is to analyze a teen soap opera. And it’s not that the scouting reports were unclear, or that the statistical analysis was misleading, or that the players misrepresented themselves in interviews. It’s that taking 18- to 22-year-olds and turning them into big leaguers is a hard thing to do under the best of circumstances. Read the rest of this entry »

The fastball is dead. Or is it?

Every season has its share of articles detailing the league-wide decline in fastball usage, and 2024 is no exception. This time around, the spotlight has been on the Red Sox, who have seemingly crafted an elite rotation based on a delightfully succinct philosophy: Spin go brrrr. Indeed, they trail the league in four-seam fastball usage by a wide margin. But they’re also ninth in sinker usage and first in cutter usage as of this writing. This is incredibly interesting to me, especially after you consider the graph below:

In early counts (0-0, 0-1, and 1-0), when batters are more eager to swing and hunt for fastballs, we’ve reached a new minimum for four-seam fastballs. That checks out. But look at the combined rate of sinkers and cutters: It’s back up to levels last seen in 2018. So really, the Red Sox aren’t being hipsters. If anything, they represent what the league is thinking as a whole. The uptick is there, even if you exclude Boston. Read the rest of this entry »

For the last few years, I’ve been checking the accuracy rate of the ball-strike calls made by umpires, dividing the number of correct calls by the total number of takes. It’s a blunt approach, but because umpires make so many thousands of calls each year, it yields solid results. On Tuesday, I pulled the numbers for the 2024 season, and I found something I didn’t expect: Accuracy is going down rather than up. In every single season since the beginning of the pitch tracking era in 2008, umpires have gotten better at calling balls and strikes according to the Statcast strike zone. This is the first time I’ve ever pulled the numbers and seen a lower accuracy rate. However, this is also the first time I’ve checked the numbers this early in the season, and it turns out umpires tend to make better calls as the season goes on. Since 2017, accuracy in March, April, and May has been 0.19 percentage points lower than accuracy over the full season (though the difference in 2023 was just 0.03 percentage points). Here’s what that looks like in a graph.

You know how at the beginning of every season, there are a couple blown calls during a nationally televised game (or at least, calls that appeared to be wrong according to the on-screen strike zone), and certain people start complaining that umpires are terrible and they’re getting worse? Those people always catch me off guard. I usually forget about the missed calls when the season ends, but those people somehow manage to keep their umpire anger at a high idle through the entirety of the offseason so that the instant baseball returns, they’re ready to shout about the umpires again without any need to ramp up. I don’t know how they do it without pulling an oblique, but in a sense, those angry people are right. Even though the umpires are always getting better year after year, they’re nearly always more accurate toward the end of the season than at the beginning — so much so that when the season starts, they’re worse than they were at the end of the previous season. For a month or two, the umpires really have gotten worse. We often say early in the season that pitchers are ahead of hitters. It turns out they’re ahead of umpires too.

For each season, I broke down the overall accuracy in two-month increments, essentially dividing the season into thirds. I also broke down the accuracy during spring training and the playoffs, although there are plenty of factors that make those numbers suspect. During spring training, the umpiring pool is much wider. Perhaps more importantly, there are far, far fewer tracked pitches during spring training, both because the number of games is so small and because not every stadium is set up for Statcast. That results in a much smaller, much less reliable sample. The playoffs are also a much smaller sample, but they’re also, at least in theory, selecting for better umpires. Working the playoffs is seen as an honor and a reward for performing well in the regular season. We should expect accuracy to be at its lowest during spring training and highest during the playoffs.

Generally speaking, the results fit our preconceptions. Spring training accuracy is very low and it features the volatility that we’d expect from a small dataset. Umpires are also more accurate in the playoffs. The red line is March, April and May, and as you can see, it’s nearly always below everything but the spring training line. Not only do umpires start getting better in June, but they keep getting better right through the end of the season, which is why the light blue line for August, September, and October is usually above the yellow line for June and July. The trend is a little bit easier to see if we focus just on pitches in the shadow zone, the area that’s one baseball’s width from the edge of the zone on either side.

In the graph above, the dotted line represents that season’s overall accuracy on calls in the shadow zone. Each data point represents the number of percentage points above or below that year’s average. Not only do the calls get better as the season goes on, there’s a definite gap between the first two months and the rest of the season. Umpires are decidedly worse in those first two months. However, 2023 was a real outlier. It was first time since 2008 that umpires were more accurate in the beginning of the season than the end.

With that, I want to bring you back to 2024. So far this season, umpires have gotten 92.46% of calls right, down from 92.81% in 2023 and just two thousandths of a percentage point higher than in 2022. Based on everything I’ve shown you, we should expect umpires to get better over the rest of the season. However, the drop-off from last year is noticeable. Accuracy over the first two months of the season has only fallen once before, from 2009 to 2010, when it dropped by 0.16 percentage points. So far this season, accuracy has fallen by twice that amount: 0.32 percentage points. That’s a tiny change, on the order of one call per game, but that doesn’t make it any less real. We’ll have to wait and see how the rest of the season goes, but perhaps this year really could end up being different. Or, if it follows the pattern of the past decade and a half, accuracy will soon be in its way up.

Hi! It has been made to clear to me that my use of f = m * a as a narrative device herein was quite distracting, chiefly because in this context it’s incorrect. I apologize in advance to renowned baseball physicist Prof. Alan Nathan, should he ever read this; to all other physics enthusiasts who have remarked on the mistake; and to me, for embarrassing me. Ideally you will see past it and appreciate the meat and potatoes of the post for what they are: that there is possibly declining marginal utility to bat acceleration in a way we don’t seem to witness for bat speed. Thanks, and sorry again!

I can’t possibly begin to cover all the excellent work concerning Statcast’s new bat tracking data. Now as much as ever, it’s important to support your local Baseball Prospectuses, PitcherLists, Baseball Americas, FanGraphses and freelance Substack writers. We move quickly in these parts. There’s so much analysis to consume, all of it superb.

When confronted with this new data, one of my first instincts was to see which metrics from other areas of sabermetric analysis could be replicated within the bat tracking framework. Enter 90th-percentile exit velocity (90EV); it’s a powerful shorthand metric that distills a lot of information about the top end of a hitter’s exit velocity distribution into a single number. It’s not perfect, and other metrics outperform it, but it’s easy to see how it has become popular in contemporary analysis, especially in prospecting and scouting circles. Read the rest of this entry »

Statcast’s new public repository of bat tracking data has been out for a few weeks now. Like every number manipulator with a sense of curiosity and middling technical skills, I’ve been messing around with the data in my spare time, and also in my working time, because messing around with data is both my job and hobby.

Mostly, I’ve been reaching some conclusions that mirror what others have already shown, only with less technical sophistication on my part. This article by Sky Kalkman does a great job summing up the biggest conclusion: Pitch location and spray angle (pull/oppo) influence swing length so much that you probably shouldn’t quote raw swing length. But I thought I’d look for something slightly different, and I think I found something.

Here’s the high level conclusion of my search: When pitchers throw harder fastballs, hitters slow down their swings to compensate. It sounds counterintuitive. Shouldn’t hitters speed up their bats to try to get to the faster pitch? But I had a hunch that this wasn’t the case. If you listen to hitters describe their approach against flamethrowers, they focus on shortening up and putting the ball in play. “Shortening up” might sound like it describes swing length, but it also surely describes swing speed. A hitter who is just punching at the ball likely won’t swing as hard as one trying to launch one. If you’re prioritizing having your bat on plane with the ball as long as possible, you probably aren’t focusing as much on raw speed. Read the rest of this entry »

For years now, a simple message has been gaining traction in major league bullpens and pitching labs: Just throw it down the middle. As big league pitches have gotten speedier and bendier, the people who throw them have been increasingly advised to trust their stuff, stop nibbling around the edges, and attack the heart of the zone. Adam Berry wrote about the Rays adopting this approach in 2021. In 2022, Bryan Adams superfan Justin Choi looked into the numbers and noted, “In each season since 2015, when Statcast data became public, hitters have accumulated a negative run value against down-the-middle fastballs.” Last year, Stephanie Apstein documented the phenomenon in Baltimore, while Hannah Keyser and Zach Crizer did the same on a league-wide basis, describing the Rays model thusly:

Step 1: Develop unhittable stuff
Step 2: Let it rip down the middle
Step 3: Win

Just last week, Jeff Fletcher wrote that after trying and failing to get their pitchers to attack the zone more often, the Angels started putting their pitchers in the box to face their own arsenal, courtesy of a Trajekt pitching machine. “I knew my pitches were good,” said José Soriano through an interpreter, “but when I faced myself, I find out they’re really good. So I have more trust in my stuff now.” Pitches right down the middle are called meatballs for a reason, but if you’ve ever watched peak Max Scherzer demolish the heart of the other team’s lineup by simply pumping 97-mph fastballs across the heart of the plate, none of this comes as a galloping shock.

Still, I wondered whether I could find data to back up this shift in mindset. Are pitchers really attacking the zone more often? And are better pitching staffs (or staffs with better stuff) really attacking the middle of the plate more often? After all, the Angels rank 22nd in Stuff+ and 14th in PitchingBot Stuff, not to mention near the bottom in ERA, FIP, and xFIP. If they feel this good about their stuff, I’d imagine that every team does. Read the rest of this entry »

On the first knuckleball thrown at Coors Field in 16 years, Matt Waldron hit home plate umpire Bill Miller right in the nuts.

Nobody — not Waldron, not his catcher Kyle Higashioka, not Miller — appeared to know where the ball was going. Despite Higashioka frequently (and understandably) struggling to track the flight of the ball throughout the rest of the night, Waldron delivered a career-best performance, allowing just one run over six innings.

Perhaps the most surprising part of his performance was the setting. Since 2008, knuckleballers have dodged outings at Coors Field, which sits 5,200 feet above sea level. Conventional wisdom dictates that knuckleballs at altitude are a bad idea, as Cy Young-winning knuckleballer R.A. Dickey told Dave Krieger back in 2012. Read the rest of this entry »