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 »
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 »
Surely this is just some piece of cosmic performance art. If you’re looking for proof that we live in a simulation, Isaac Paredes’ spray chart is strong evidence. Sure, you’ve heard of pull hitters. What about only-right-at-the-foul-pole hitters, though?
Paredes is doing the same thing he always seems to. Through 259 plate appearances this season, he has the best wRC+ of his career at 147. Think it’s all about his one simple trick for hitting homers? He’s 16th in baseball in on-base percentage. He’s still walking roughly 10% of the time and striking out far less frequently than average. None of it makes sense, and yet it keeps happening.
The “Paredes approach” has been endlessly rehashed at this point. He puts the ball in the air. He pulls the ball in the air. He makes a tremendous amount of contact, and he cuts down on his swing to do so. His bat speed and exit velocity numbers are unimpressive, and he hits a ton of fly balls that would be outs if they went anywhere other than the left field corner. But, well, they keep going to the left field corner, as we’ve already covered.
Let’s put it this way: Here’s a list of pull rate on fly balls for all hitters, from Paredes’ debut in 2020 through the end of last season:
We’re back at it again with another batch of baseball lingo. As usual, I encourage you to go check out previous installments of this series to catch up on what you missed or familiarize yourself with the premise of these primers. You can find each of them by clicking on each individual part for its corresponding article:
At the end of my last piece, I hinted at moving beyond four-seamers, and digging into the types of pitches that typically make up the rest of a pitcher’s arsenal. But as soon as I sat down to start cataloging the ways that secondary pitch shapes are described, the vastness of the array of breaking balls and offspeed offerings throughout professional baseball quickly became overwhelming. That is largely due to how pitching practices and preferences vary from player to player, and how those individual approaches impact how each respective arsenal is most effectively used.
Asking a major league pitcher how to throw a slider would be like asking a world-renowned chef how to make scrambled eggs. They probably wouldn’t actually answer the question of how to make scrambled eggs, but rather, they’d tell you how they make their scrambled eggs. And those preparation processes would vary drastically. Some would be of the Anthony Bourdain ilk, with an inclination toward old-school simplicity. Beat eggs in a bowl with nothing but salt and pepper. Throw some butter in a hot pan and add the eggs, then move them around with a wooden spoon for a while. Meanwhile, others would take more of a Gordon Ramsay angle, insistent that a cold pot, a 60-second timer, and a dab of f—ing crème fraiche are all necessary for perfect scrambled eggs. The only shared components between these two preparations are the eggs, the heat, and the fact that they are kept in motion while cooking. And yet, both outputs, while different in innumerable ways, are classified simply as “scrambled eggs.”
Similarly, pitchers’ grips and releases of their secondary offerings also vary greatly from pitcher to pitcher. Depending on what a pitcher is naturally adept at, what he prefers, or even the length of his fingers or his overall grip strength can dictate how a he throws a given breaking ball or offspeed pitch. As a result, despite being classified as the same type, the shape of a pitch from one hurler to the next can look so different as to hardly seem comparable. So, before we dig into describing the shapes of specific pitches, and the way those shapes are created by a given pitcher, let’s boil down these classifications to their essential elements – the eggs, heat, and perpetual movement, as it were.
Secondary pitches, while individually unique, can also be broken down into basic elements. Namely, we can boil them down to the type of spin a pitcher applies to the ball, the angle of the spin axis he creates in doing so, and the degree of supination or pronation in his release that accomplishes these distinct spin attributes. Of course, there’s much more to pitch design than these elements, but understanding them is a great place to start.
So, let’s jump in!
Spin Axis
The spin axis is the central point that the ball is spinning around. In other words (apparently, I’m on a food metaphor kick right now), if the ball were a candy apple, and you wanted to use it to illustrate the spin of a certain pitch, the spin axis would be where you would hold the stick. It’s very rare for a ball to have perfect forms of any type of spin, with spin axes at perfect parallels or perpendiculars. Instead, variation comes from the pitcher’s arm slot, release point, supination/pronation (which I’ll discuss in a moment), and many other personalized characteristics. Those variations, among other factors, influence the degree to which a pitch’s shape digresses from pure north/south or east/west movement.
On a ball with pure backspin, the spin axis would be in the exact center of either side of the ball, horizontal to the ground. As mentioned in Pitching, Part 2: Backspin is created by the pitcher letting the ball roll off his fingertips.
Kopech keeps his fingers behind the ball upon release, and the seams move upward across the front of the ball as it travels toward the plate.
Gyroscopic spin is the term used to describe clockwise or counterclockwise spin. On a ball with pure gyroscopic spin, the spin axis would be in the exact center of the front and back of the ball, horizontal to the ground.
To create this bullet-like spin, Vodnik moves his fingers along the side of the ball as he releases it.
Topspin, also referred to as “forward spin” or sometimes “tumble,” is the inverse of backspin. On a ball with pure topspin, the spin axis would also be in the exact center of either side of the ball, horizontal to the ground, but spinning in the opposite direction.
As the ball travels toward the plate, the seams move downward across the front of it. This requires Cusick to move his fingers around the side of the ball even more than what is required for gyroscopic spin, to the point where his fingers are moving downward across the front of the ball as he releases it.
Supination vs. Pronation
Supination and pronation refer to the direction and degree to which a pitcher rotates his wrist and forearm. Applying supination or pronation to a pitch will most often sacrifice some amount of velocity in favor of some amount of movement. The exact type of movement, and the effect on velocity, depends on how the supinated or pronated release is being utilized – i.e. what type of spin it’s creating on the ball, and on what spin axis.
Supination is when a pitcher rotates his forearm such that his knuckles move toward the outside of the ball, and his palm moves toward an upward position. This creates glove-side cut on a pitch.
Pitches that feature supination include cutters, sliders, and curveballs, to name a few.
Pronation is the inverse of supination. When a pitcher pronates his arm, his wrist and forearm rotate in the other direction, finishing with his palm facing away from his body or toward the ground. This creates arm-side run on a pitch.
A non-comprehensive list of pronated pitches includes two-seamers, circle changeups, and screwballs.
Again, we’re only talking about the fundamentals here, when it comes to understanding pitch design. The fun part occurs when these elements are mixed and matched to create different types of pitches. Now that we’ve defined and illustrated our terms, we can move on to how these terms combine and commingle to make up a pitcher’s full arsenal, as well as which pitches are most and least open to interpretation. If sliders are scrambled eggs, for example, then knuckleballs are poached eggs; there’s only very slight variation in how pitchers throw them, and the output should be virtually the same from pitcher to pitcher, with mistakes being easy to spot. I look forward to digging into these comparisons and more in installments to come!
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 »
