Last week, I wrote about the careers of the two former college baseball players who have been featured on this season of Love Is Blind, and don’t worry, I’m not going to follow up with a detailed breakdown of their performance on the episodes released this past weekend. (Though if anyone wants the short version: It’s been pretty dire. Ben is getting flamed on TikTok so bad his fiancée is thinking about pulling the plug, while Dave… I don’t know what you’re doing, man. Get it together. You’re in your mid-30s. You should be able to have a frank, productive conversation with your partner.)

I bring all this up because it’s been hard to shake something I mentioned in Friday’s article: Ben Mezzenga’s astonishingly high incidence of taking strike three. In his best years, only about half of his strikeouts came swinging. A typical big league hitter strikes out three times swinging for every time he strikes out looking. Last year, José Ramírez ran a ratio north of 15-to-1, the highest mark in baseball. Cavan Biggio was the only hitter who had 50 or more strikeouts with more than half of them coming with the bat on his shoulder. Read the rest of this entry »

Nobody wants to throw a backup slider. They are, definitionally, an accident. But announcers and analysts alike have noted that these unintentional inside sliders — perhaps due to their surprise factor — tend not to get hit. In 2021, Owen McGrattan found that backup sliders, defined as sliders thrown inside and toward the middle of the strike zone, perform surprisingly well.

I’ll add one additional reason these pitches are effective: They move more than any other slider.

I analyzed over 33,000 sweepers thrown by right-handed pitchers in the 2024 season. I found a clear linear relationship between the horizontal release angle of a sweeper and the horizontal acceleration, better understood as the break of the pitch. On average, as the horizontal release angle points further toward the pitcher’s arm side, the pitch is thrown with more horizontal movement.

Josh Hejka, a pitcher in the Philadelphia Phillies minor league system, told me these results corresponded with his anecdotal experience.

“I’ve often noticed — whether in game or in the bullpen — that the sliders I throw arm side tend to actually have the best shape,” Hejka said. “I believe it’s conventional wisdom across baseball that the backup sliders tend to actually be the nastiest.”

Check out all the movement Corbin Burnes gets on this backup slider from last season.

The relationship between horizontal release angle and movement also holds true for sinkers. When a sinker is aimed further to the glove side — for pitchers facing same-handed hitters, this would be a backdoor sinker — the pitch gets, on average, more horizontal movement, as is the case with this pitch from Anthony Bender.

The explanation for the relationship is straightforward enough. When sweepers are thrown to the arm side and sinkers are thrown to the glove side, the pitcher’s grip is such that maximum force is applied to the side of the baseball, allowing for more sidespin. In a 2015 interview with David Laurila, then-Royals pitching coach Dave Eiland described why sliders back up.

“They really get around it; they don’t get over the top and pull down,” Eiland said. “It’s unintentional, more of a misfire, so to speak. If you could do that intentionally, you’d have a decent pitch.”

It isn’t just sweepers and sinkers that show a relationship between release angles and movement. Back in August, I investigated the mystery of the invisible fastball. Why was a pitch like Shota Imanaga’s fastball, with its elite vertical movement and flat approach angle, so rare? I found that vertical release angles mediate the relationship between both variables. A fastball thrown with a flatter release angle gets less backspin, and so to achieve both requires outlier mechanical skills.

Release angles don’t just measure the nature of a grip, they also dictate the location of the pitch. I conclude that where the pitcher aims a pitch changes the way it moves. For fastballs, pulling down on the ball allows for more backspin. For sweepers and sinkers, getting around the ball allows for more sidespin. Analysts attempt to separate “stuff” from “location;” these findings complicate that conversation.

***

Before we go any further, it’s important to know what exactly is a release angle. Release angles measure — or, in this case, approximate — the angle at which the ball comes out of the pitcher’s hand. For vertical release angles, anything above zero degrees suggests the ball is pointing upward at release; most vertical release angles, particularly for four-seam fastballs, are negative, meaning that the pitcher is aiming the ball downward at release.

Horizontal angles work the same way, but in the x-dimension. Positive values mean the ball is pointed toward the pitcher’s left; negative values point toward the pitcher’s right. (This is a feature of the original Pitch F/X coordinate system, when it was determined that x-dimension pointed to the catcher’s right.) In any case, release angles, both horizontal and vertical, attempt to capture the exact position of the ball at release. Because they capture the position of the ball at release, they contain information about the pitcher’s aim and, it turns out, the force they’re applying to the ball.

My research finds that there is a relationship between horizontal release angles and horizontal acceleration. In simpler terms, the way the ball is released out of the hand, and therefore where it is aimed, impacts the movement of the pitch.

There are some confounding variables in this specific relationship. The Hawkeye cameras (and, in earlier times, the Pitch F/X technology) report accelerations in three dimensions. These accelerations are measured relative to a fixed point on the field, which happens to be right in front of home plate. Because these accelerations are fixed to one point, the reported values can be biased by the position of release in space. This is far from intuitive, so it might be helpful to consider an example.

Remember that Burnes sweeper from the introduction? It accelerated at roughly 16 feet per second squared in the x-dimension. Imagine that instead of throwing his sweeper from the mound, Burnes threw it from the third base dugout. It’s the exact same pitch as before — same velocity, same horizontal break — but the release point has completely changed. On a fixed global coordinate system of movement measurement, the acceleration in the x-dimension no longer describes the pitch’s relevant movement; all that sideways movement would instead be measured in the y-dimension.

This is an extreme example to illustrate the point, but on a smaller scale, this fixed point measurement system biases acceleration measurements. In order to fix this bias, accelerations can be recalculated to be relative to the pitch’s original trajectory, removing the influence of the release point on the acceleration value. These calculations come courtesy of Alan Nathan; Josh Hejka rewrote them as Python code, making my job easy.

A slight nuance:

The accelerations given by MLB (ax, ay, az) are biased by pitch location.

To make these values location-agnostic, we need to adjust the acceleration vector to be relative to the initial trajectory (i.e. the initial velocity vector vx0, vy0, vz0).

?: https://t.co/CVVNKtG3YM

— Josh Hejka (@hedgertronic) November 14, 2024

Even after accounting for these confounding variables, the relationship between release angles and movement is still present. As the plot shows, it isn’t a particularly strong relationship — when modeled, a two-degree change in horizontal release angle is associated with roughly a foot per second increase in transverse acceleration. But while the relationship is not as strong as that between four-seam fastballs and vertical release angle, it is nonetheless meaningful.

Alternatively, the relationship can be measured using good old-fashioned “pfx_x,” or horizontal movement, which is also measured relative to the pitch’s original trajectory. Why go through all this effort to transform the accelerations? For one thing, I had a good time. And also, isn’t it fun to imagine Burnes throwing sweepers from the dugout?

The plot of horizontal location and horizontal movement, with each pitch colored by its horizontal release angle, illuminates the ostensible lack of a relationship between pitch location — measured by “plate_x” on the plot below — and movement. Draw your attention to the patch of dark blue dots around the -2 line of the x-axis. There are two potential ways for a sweeper to end up two feet off the plate inside. It can be thrown with a horizontal release angle around zero and little sideways movement, or it can be thrown with a negative horizontal release angle and lots of sideways movement.

The same relationship holds true for horizontal release angles and two-seam fastballs after the aforementioned adjustments.

On the individual pitcher level, the relationship is slightly weaker; on average, the r-squared is roughly 0.04 for sweepers, with variation between pitchers on the strength of this relationship. Zack Wheeler’s sweeper movement, for example, appears to be particularly sensitive to release angles:

***

Ultimately, analysts attempt to separate “stuff,” defined as the inherent quality of a pitch, from “location,” defined as where the pitch ends up. But what this research suggests is that, to some degree, these two qualities are inseparable. (I wrote about this a bit on my Pitch Plots Substack last September.) Certain pitches generate their movement profiles because of where they’re aimed out of the hand.

These findings naturally lead to deeper questions about the interaction between biomechanics and pitch movement. While there are variables (arm angle, release height, etc.) that are commonly understood to influence movement, these findings suggest that there are even more granular factors to explore.

Is the angle of the elbow flexion at maximum external rotation the most influential variable? Is it hip-shoulder separation? Torso anterior tilt? Pelvis rotation at foot plant? How much do each of these components contribute to pitch shapes?

Thanks to data from Driveline’s OpenBiomechanics Project, it’s easy to model the relationship between dozens of biomechanical variables and the velocity of the pitch. There are about 400 pitches in the database; by attaching markers to a pitcher moving through space, points of interest can be calculated and then compared to the pitch’s velocity.

In this public dataset, Driveline does not provide the movement characteristics of the pitch. But if the force applied to the ball based on the direction of its aim affects the movement of the pitch, it follows that these variables could be measured in a detailed manner. On the team side, KinaTrax outputs provide the markerless version of these data, providing a sample of hundreds of thousands of pitches from a major league population. Imagine the possibilities.

Correction: A previous version of this article misstated the units of acceleration of Burnes’ backup sweeper. It is feet per second squared, not feet per second.

In the middle of the 2024 season, MLB released bat tracking data for the current year. It was a huge revolution in publicly available data, taking something previously observable but not measurable and turning it into numbers. You can see how hard Giancarlo Stanton swings, but now you can also quantify how different that is from other large hitters. Luis Arraez’s superhuman coordination is obvious from watching him play. But in terms of getting his barrel on the ball, relative to the rest of the league, how superhuman is he? Now we know. I think that public research on this front is likely to deliver more and more insights in the coming years.

Of course, what we all wanted to know about bat speed wasn’t available right away. Namely: How does it change? Was Ronald Acuña Jr.’s disappointing start to the season related to an inability to impact the ball with force? Did Matt Olson’s decline have more to do with bat speed or plate discipline? Also, plenty of non-Braves questions, presumably. In any case, we couldn’t say much about that because all we had were the 2024 numbers.

Guess what: Now we have some 2023 data. MLB and Statcast released 2023 data starting after the All-Star break, the earliest data we’ll ever get because that’s when the bat tracking infrastructure got going. Obviously, we’re also going to get more year-over-year data when the 2025 season starts. But our first crack at multiple seasons of data is still noteworthy, so I set out to look through the numbers and came to a few conclusions. I don’t intend for these to be comprehensive, and I’m sure that a measured and careful approach is going to tease out some new insights that I don’t have. But the data came out yesterday, and here are a few highlights.
Read the rest of this entry »

Earlier this week, I published my findings about the relationship between when free agents sign and the size of their contracts. As a quick refresher, in recent years, the last 20% or so of free agents to sign have been settling for contracts meaningfully lower than pre-offseason expectations. But that finding raises more questions, some of which I hope to answer today.

First, there’s an obvious question: Did the free agents who got those late, discounted deals perform worse than expected during the following season? In other words, did their low-dollar-value deals foreshadow lower-than-projected production? To examine this, I took the upcoming season’s projections for the players ranked on my Top 50 Free Agents list in each of the past three years, 150 players in all, to come up with a projected WAR for each segment of players. I then compared it to how they actually did in the ensuing year. There is indeed a drop-off for those who signed late:

First things first: Every group underperformed its projections. That comes down to playing time. Our projections use Depth Charts playing time, which approximates the most likely distribution of playing time across a given roster without accounting for the likelihood of injuries. Just as an example, non-catcher batters were projected for an average of more than 600 plate appearances in this dataset, and they came in closer to the mid-500s in practice. So don’t pay too much attention to the absolute numbers; the relative differences are what to look at here.

The last 10 free agents to sign saw huge shortfalls in production relative to expectations. One reason: They played less. The average hitter in this group of 150 free agents batted 70 times less than projected. Hitters signed among the last 10 free agents in their class batted 100 times less than projected. Likewise, the average pitcher in the group came up 25 innings shy of projections, but pitchers among the last 10 players signed came up 40 innings short.
Read the rest of this entry »

“It’s better not to know so much about what things mean.”
– David Lynch in Rolling Stone, September 1990

A few friends and I have a recurring movie night where we take turns choosing the featured film for the evening. Because one friend has decided to make his picks in the “campy horror” genre, last week we wound up watching Peter Jackson’s Dead Alive (yes, THAT Peter Jackson). Rotten Tomatoes describes it as a “delightfully gonzo tale of a lovestruck teen and his zombified mother,” while Wikipedia goes with “zombie comedy splatter film.” It deals in absurdity and surrealism and its favorite tool of the trade is fake blood. The production reportedly went through about 80 gallons of the stuff.

Absurdist storytelling launders its messaging through exaggerated extremes and by defying or subverting logic in ways frequently so morbid or dark that they surpass tragedy and come all the way back around to comedy. Extremes that defy logic exist in baseball too. A particularly rich source being players’ home/road splits. I went searching the 2024 season for the most extreme differences in player performance (minimum 200 plate appearances) between their home parks and road venues across a variety of offensive metrics. In my own act of defying reason, I don’t really have an explanation for choosing hitters over pitchers. Maybe I’ll do pitchers in the future. Maybe I won’t. Who needs symmetry or balance in the universe? Anyway, I found the largest disparities, and ignored the boring, expected ones like Rockies hitters clubbing a bunch more homers at Coors Field, and instead, locked in on the truly bizarre.

Certain occurrences earn their bizarre status not because of their unexpected nature, but rather because they take an expected outcome to such an extreme as to feel over the top, or a bit “on the nose,” as an editor might put it. Dead Alive depicts Lionel, a young adult man, still living at home with his mother, an overbearing type who domineers his life. Lionel and his mother portray the standard “momma’s boy” archetype, but exaggerated to nth degree — the film culminates with the supercharged zombie version of Lionel’s mother inserting her son back into her womb, where she can finally regain complete control over his life.

Like an overbearing mother, certain ballparks have a strong influence on the type of hitter who thrives under their care. Some encourage power, or prefer a certain handedness, while others look down on hitting and choose instead to emphasize pitching and defense. Petco Park in San Diego does not favor offense in general, but it is among the least friendly ballparks for lefties who hit a bunch of singles. Enter Luis Arraez, the singles hitter of all singles hitters.

The infielder/DH was traded to the Padres from the Marlins last May 4. Like Lionel, who in the early scenes of Dead Alive meets a nice young woman named Paquita and takes her on a date to the zoo, Arraez continued to do his thing for the month of May, hitting 38 singles, compared to the 30 he hit during the first month of the season. But then Lionel’s mother interrupts the date, gets bit by a Sumatran Rat Monkey, and chaos ensues, just as the influence of Petco Park eventually exerts its will on Arraez. He ended the season with a .268 average at home and a .359 average on the road, due in part to his hitting about 20% fewer singles (71 vs. 90) and almost 50% fewer doubles (11 vs. 21) at home compared to on the road. This placed him at the extreme end of Petco Park’s offense dampening effects, so extreme as to feel like the stadium stuffed Arraez inside her womb until he learned his lesson about hitting all those singles.

Batting average is one thing, but there are other stats that you wouldn’t necessarily expect to have extreme home/road splits; similarly, you wouldn’t necessarily expect a scene at the beginning of a movie that depicts the main character mowing the lawn at the behest of his mother to foreshadow a momentum shift in the big fight scene at the end. Nevertheless, Brice Turang’s stolen base success rate was 15 points higher at home than on the road, which was the largest differential among base stealers with at least 30 attempts (omitting Jazz Chisholm Jr. who was around 20 percentage points better on the road, but also switched home stadiums in late July). The Brewers second baseman stole 28 bases at American Family Field and was caught just one time there, while in away parks he stole 22 bases and was caught five times. The discrepancy becomes all the more notable when considering Turang reached base less frequently at home, posting a .290 OBP in Milwaukee compared to a .341 OBP everywhere else.

There aren’t too many data points to suggest why Turang was better at swiping bags at home, but as a player with just over 1,000 big league plate appearances, it makes sense that some of his visual timing and positioning cues might be more locked in at AmFam than they are elsewhere in the league. Things like the first base cutout in the infield grass and the sightlines behind the pitcher as he’s taking his lead from first are likely more dialed in at the place where Turang has taken the majority of his reps in the majors. Using one of his strongest tools (94th percentile sprint speed) and the comforts of a familiar environment, Turang almost completely compensated for his otherwise negative contributions on offense, just as Lionel, in defending his home from a horde of zombie partygoers, turned to a trusted tool — his lawnmower and its sharp, speedy blade — to mow through the walking dead.

The largest split I could find with respect to wRC+, which is already adjusted for park factors, belongs to Luis García Jr., who after several up and down seasons with the Nationals, spent 2024 as Washington’s primary second baseman. The lefty logged a 156 wRC+ at home and a 63 wRC+ on the road, a 93-point spread. This is where it’s helpful to know exactly how the park adjustment is applied to wRC+ and why that might make a fairly neutral hitting environment like Nationals Park seem like an oasis for one hitter in particular. Or, in other words, why a young lady like Paquita might continue to see someone even after his zombie mother ate her dog.

(Here is where I must note that there is a character in Dead Alive named Scroat. Unfortunately, I couldn’t make a baseball analogy to Scroat because I do not remember which character was Scroat, and an IMDb search through the movie’s cast list does not have a headshot next to the actor who played Scroat. Really, I just wanted a chance to write Scroat in a FanGraphs post, so here we are. Scroat!)

Anyway, the park factor applied to wRC+ is a single value that captures the run environment in the stadium overall, as opposed to the more granular component level park factors that consider the stadium’s influence on the individual components of offense, such as singles, doubles, triples, home runs, etc. Component park factors that take into consideration the batter’s handedness are also available. Digging into the components of García’s home/road splits reveals that when in D.C., he struck out less and hit more singles and homers. Component park factors explain part of why García might benefit more from hitting in Washington than an average hitter: Nats Park does suppress strikeouts relative to its peers, and left-handed hitters get a boost with respect to singles. Fewer strikeouts means more balls in play at a ballpark where a ball in play off the bat of a lefty is more likely to lead to a hit. However, Washington remains neutral on home runs for those hitting from the left side. Looking at García’s splits with respect to batted ball characteristics reveal his home run-to-fly ball rate drops from 19.2% at home to 6.7% on the road. But it’s not just that the ball is carrying better because, additionally, his hard hit rate increases from 24.1% on the road to 38.4% at home. That García’s strikeout rate drops 10 percentage points in his home ballpark relative to everywhere else, in conjunction with his improved contact quality on fly balls, seems to suggest he sees the ball better at Nats Park than anywhere else. And for what it’s worth, a special aptitude for vision is what kept Lionel’s girlfriend from abandoning him, as she believed the tarot reading done by her seer/grandmother that foretold a fated, long-term romantic entanglement with Lionel.

Many don’t believe in fate and instead subscribe to the nihilistic view that the universe is composed of randomness, which at times manifests as utter, uninterpretable chaos. T-Mobile Park in Seattle is one of the worst ballparks for hitters, both overall and across all individual components, unless, by chance, you happen to be Luke Raley. The Mariners outfielder/first baseman defied the natural order of the universe (to the extent that there is one) and posted a .393 wOBA, 166 wRC+, and hit 15 home runs across 229 plate appearances at home, with a .295 wOBA, 91 wRC+, and seven homers over 226 PA on the road. Looking at component factors does absolutely nothing to explain Raley’s performance at T-Mobile Park, since as a lefty, all of Seattle’s horrible hitting juju applies even more so than it does for righties. His BABIP hovered around .300 both at home on the road, suggesting that if there’s luck in his performance, it was distributed evenly at home and on the road. In terms of his batted ball profile, Raley did have a higher hard hit rate at home, and he also pulled the ball more and put it in the air more, which collectively signals an overall higher quality of contact. Perhaps like the tarot-reading grandmother, Raley possesses some special sight that allows him to see the ball in a way that no one else has mustered at T-Mobile Park, or perhaps, as is the messaging of much absurdist art, we must simply submit to the random, chaotic winds of the universe, blowing some fly balls over the fence and leaving others to die on the warning track.

Whatever force is tasked with inflicting chaos upon the masses, it seems to enjoy unleashing Yordan Alvarez as often as possible. It’s true that Houston’s lefty DH/left fielder was not involved with the Astros’ banging scheme scandal, but he nevertheless is a frequent recipient of boos at away ballparks due to his uncanny ability to launch game-winning, soul-crushing moonshots in front of opposing fans. Though the booing is more of a vibes-based response, the data show that Alvarez does tap into his power more frequently on the road, hitting both doubles and home runs at a much higher rate, leading to a road wRC+ that is 62 points higher than his mark at home (a road advantage topped only by J.P. Crawford of the Mariners).

Again, wRC+ already accounts for the overall run environment, but not the components by which a particular player might be more heavily impacted. Houston’s ballpark, which is now called Daikin Park, grades out as neutral to hitters overall and with respect to left-handed home runs, but for doubles, a lefty hitter should have an easier go of it (though it’s worth noting Alvarez pulls the ball at a below average rate for lefties). But despite the neutral or better home park environment, in 2024, Alvarez hit 12 doubles and 13 homers across 315 plate appearances at home, while hitting 22 doubles and 22 homers across 320 plate appearances on the road. Alvarez also walked slightly more on the road, while holding his strikeout rate constant, suggesting a more patient approach that led to higher quality contact; this is reinforced by his higher home run-to-fly ball rate (20.4% vs. 11.7%) and hard hit rate (46.1% vs. 36.1%) away from Houston.

As with some of the other extreme splits, the increased patience and improved contact might mean that Alvarez doesn’t see the ball as well at Daikin Park as he does elsewhere. Or this instance of absurd home/road splits might be trying to send a different message. Absurdist art and its close relative, surrealism, frequently serve to defy logic, or at least quantifiable logic. At the end of Dead Alive, Lionel cuts his way out of his mother’s womb using a talisman that Paquita’s grandmother gave him for good luck. She probably thought its magical properties would prevent anything bad from happening to him, rather than its physical properties allowing him to puncture zombie flesh. Even magic follows no logical order.

Meanwhile, when asked to describe the experience of playing in a big league stadium in front of a packed crowd during the highest leverage moments of the game, players frequently use the word surreal. And in the surreal world, there wouldn’t necessarily be a logical explanation for why Alvarez becomes more powerful on the road, why he happens to be holding a talisman that can puncture the hearts of opposing fans. Maybe he feels less pressure away from the home fans. Maybe he takes a twisted pleasure in making a stadium full of fans fall silent. Maybe, like the zombies in the movie, he takes a poison intended for animals that has the unintended effect of supercharging his abilities. I’m mixing my talisman and poison metaphors now, but as previously established, there are no rules and nothing matters, so just roll with it and instead linger on the thought that if Alvarez ever leaves the Astros, he may morph into a supercharged monster permanently.

While we’re defying logic, I did stumble upon one member of the Colorado Rockies with a home/road split worth mentioning. In 228 plate appearances at Coors Field, Michael Toglia hit eight home runs; in 230 plate appearances away from Coors Field, he hit 17 home runs. So, in nearly the same number of opportunities, Toglia smacked more than twice as many home runs on the road as he did at Coors Field, a park notorious for juicing fly balls. My best guess is that the stadium’s reputation is doing psychic damage to a 26-year-old first baseman with just one full season under his belt. His hard hit rate is still higher at home, suggesting maybe he thinks that all he needs to do is swing out of his shoes and the thin air will do the rest. Meanwhile, his Med% is higher on the road and he hits the ball to the opposite field more often, suggesting a more controlled, purposeful swing away from the influence of Colorado. Maybe he’s overthinking the atmospheric conditions, or maybe he made a deal with an evil imp that granted him 60-grade raw power everywhere except the Mile High City.

Sometimes chaotic occurrences exist purely for comedic relief, offering no larger societal lesson or commentary on humanity. At one point in Dead Alive, Lionel visits his mother’s grave because he knows she’s a zombie and, therefore, not actually dead, so his master plan is to administer sedatives to her indefinitely in order to keep her safely in the ground. When he gets jumped at the cemetery by a band of local hooligans, he’s saved by a priest (literally, not spiritually), who seems to have exactly one skill, which is, as the priest puts it, to “kick ass for the Lord.” He does single-handedly wipe out the hooligans with what appears to be self-taught kung fu, but then promptly gets conscripted to the zombie ranks. The kung fu priest of baseball is Mike Yastrzemski, right fielder for the Giants, whose extreme singular skill is striking out way less at home than on the road. All of his other splits are as expected, but when batting at Oracle Park, he strikes out 19.7% of the time, compared to 32.6% everywhere else. It’s the most extreme strikeout difference in the bigs by a couple of percentage points.

In the movie’s next scene, Lionel has rounded up the current group of zombies, including the priest and a nurse, who was originally dispatched to look into his mother’s ailments before her transition to undead was complete. The priest and the nurse take a liking to one another and wind up birthing a baby zombie. This leads to a scene that was not in the original script and serves no purpose to the larger narrative; really, it’s just there for the jokes. Jackson decided to add it after they’d finished filming everything else, because they were still under budget, and since then, he has called it his favorite scene in the movie. For no comprehensible reason, Lionel takes the baby to the park, pushing it along in a stroller and mimicking the actions of the mothers he observes interacting with their babies. Perhaps Lionel thought that a change of scenery and treating the baby like a regular human baby would coax it into acting like a regular human baby, but it did not. Instead the viewer is treated to a series of hijinks, where the baby drags Lionel all over the park, and Lionel has to act like tackling a baby is perfectly normal behavior.

New Orioles outfielder Tyler O’Neill is the zombie baby hoping that a change of scenery does prompt a transformation. O’Neill experienced an even stranger flavor of Yastrzemski’s strikeout split. It’s not particularly unusual for hitters to strike out less in San Francisco (though not to the extreme reached by Yastrzemski), and the same holds true for Boston, where O’Neill played his home games last year. But O’Neill flipped the script; instead of striking out less at Fenway Park, he struck out significantly more frequently, posting a rate of 39.7% as opposed to 27.9% on the road. O’Neill hasn’t always struck out more at home than on the road. For example, during his final two years with the Cardinals, he was better in St. Louis than he was away from it, which is interesting considering that Fenway is a much more hitter-friendly park than Busch Stadium. It’s pretty funny to think that Fenway of all places could act as one hitter’s kryptonite, but the Orioles are hoping that was the case here. Perhaps getting O’Neill into a different park will do for him what Lionel couldn’t do for the zombie baby. If O’Neill’s overall line winds up resembling something closer to last year’s road performance, he’s much more likely to be a productive contributor in Baltimore. The spike in strikeouts caused his on-base percentage to crater to .301 in Boston, compared to .369 everywhere else.

For as much as we’d like for everything in baseball and life to follow some logical, rational, and quantifiable natural order, it doesn’t always work that way. There are too many lurking variables, agents of chaos, and forces we don’t yet understand. Sometimes it’s incredibly funny when something happens that we can’t explain. Sometimes it teaches us something completely separate from what we set out to divine. Sometimes we just have to accept that we don’t know what a weird thing is really about.

Last week, Michael Rosen wrote about Jack Flaherty’s delayed free agency market. Michael advanced a number of theories about why Flaherty hadn’t yet signed a deal, and what that might mean about his fastball, teams’ perceptions of his fastball, and the trajectory of his career broadly speaking. I found that piece really interesting – and I also started thinking about what Flaherty not having signed yet means in a larger sense.

You don’t have to look any further than last year to get an idea of what could happen to Flaherty. Blake Snell and Jordan Montgomery both waited a long time before settling for short-term deals. The year before that, Carlos Correa’s multiple failed physicals kept him on the market until the very end. In 2022, Correa, Kenley Jansen, and Trevor Story all found themselves looking for employment well into March.

All of those players came into the offseason expecting a major contract, and all of them ended up getting less than anticipated, bringing to mind some classic FanGraphs articles from Travis Sawchik, back in the halcyon days of 2018. Those articles drew on a study by Max Rieper that separated free agents into pre- and post-New Year’s signings and found a large discount for the latter group. Read the rest of this entry »

Earlier this week, I threw some numbers together on the value of productive outs. I focused on Corbin Carroll, and rightly so: His electric skill set is a perfect entry point for explaining how hitters can add (or subtract) value relative to average even when making an out. Putting the ball in play? We love it. Avoiding double plays? We love that too. The Diamondbacks are a team full of speedsters, and Carroll’s productive outs gave their baserunners a chance to show off their wheels.

A quick refresher: I calculated the difference in run scoring expectation between the average out and a specific type of out (strikeout, air out, non-GIDP groundout, double play) for each base/out state. Then I had a computer program tag each out made in 2024 with that difference. For example, the average out made with a runner on second and no outs cost teams 0.35 runs of scoring expectation in 2024. Groundouts in that situation only cost 0.25 runs, a difference of 0.1 runs.

Thus, on every groundout that occurred with a runner on second and no out, I had the computer note ‘plus 0.1’ for the “productive out” value. A strikeout in that situation, on the other hand, lowered scoring expectancy by 0.43 runs, a difference from average of -.09 runs. So the computer noted ‘minus 0.09’ for every strikeout with a runner on second and no out. Do this for every combination of base/out state and out type, add it all up, and you can work out the total value of a player’s productive outs. Read the rest of this entry »

Not every out is created equal. Take this fly out from Corbin Carroll, for example:

A lot of things can happen when you make an out with the bases loaded. You could strike out, leaving every runner in place. You could hit into a double play, an inning-ending one in this case. You could ground out some other way, or hit an infield fly. But Carroll’s here was the most valuable imaginable; with one out, he advanced every single runner, including the runner who scored from third.

Mathematically speaking, you can think of it this way. The average out that took place with the bases loaded and one out lowered the team’s run expectancy by a massive 0.61 runs in 2024. That’s because tons of these outs were either strikeouts (bad, runner on third doesn’t score) or double plays (bad, inning ends). But Carroll’s fly out was far better than that. It actually increased the run expectancy by a hair; driving the lead runner home and moving the trail runners up a base is exquisitely valuable.

That’s not the only way this could have gone. Consider a similar situation, a groundout from Aaron Judge:

Like Carroll, Judge batted with a runner on third and fewer than two outs. In this situation, the average out is bad, lowering run expectancy by 0.514 runs. But Judge’s was obviously worse. It cost the Yankees all the expected runs they had left in the inning, naturally, which added up to just a bit more than 1.15. Read the rest of this entry »

In 2008, the first year of PitchF/X pitch tracking, 13.9% of all pitches across the major leagues were sliders. Ah, those were the days – flat, crushable fastballs as far as the eye could see. More or less every year since then, sliders have proliferated. Don’t believe me? Take a look at the graph:

Are you surprised? Of course not. You’ve seen Blake Snell pitch – and Lance McCullers Jr., Sean Manaea, five of your team’s best relievers, and pretty much anyone in the past half decade. Pitchers are flocking to sliders whenever they can get away with throwing one. It used to be a two-strike offering, then an ahead-in-the-count offering, and now many pitchers would rather throw sliders than fastballs when they desperately need to find the zone. Look at that inexorable march higher.

Only, maybe it’s not so inexorable anymore. Between 2015 and 2023, the average increase in slider rate was 0.9 percentage points year-over-year. The lowest increase was half a percentage point; each of the last three years saw increases of a percentage point or more. But from 2023 to 2024, slider rate stagnated. In 2023, 22.2% of all pitches were sliders. In 2024, that number only climbed to 22.3%, the lowest increase since the upward trend started a decade ago.

That’s hardly evidence of the demise of the slider. For one thing, the number is still going up. For another thing, it’s one year. Finally, 2024 marked the highest rate of sliders thrown in major league history. If I showed you the above graph and told you “look, sliders aren’t cool anymore,” you’d be understandably unmoved.

Not to worry, though. It might be January 9, but I won’t try to pass that off as genuine baseball analysis even in the depths of winter. I’ve got a tiny bit more than that. Raw slider rate is a misleading way of considering how pitcher behavior is changing. There are two ways to increase the league-wide slider rate. First, pitchers could adjust their arsenals to use more sliders and fewer other pitches. Second, the population could change – new, slider-dominant pitchers could replace other hurlers who throw the pitch less frequently.

For example, Adam Wainwright retired after the 2023 season. He threw 1,785 pitches that year, and only five were sliders. Plenty of the innings Wainwright filled for the Cardinals went to Andre Pallante, who graduated from the bullpen to the rotation and made 20 starts in 2024. Pallante actually threw fewer sliders proportionally in 2024 than he did in 2023 – but his pitch count ballooned from 1,139 to 1,978. Similarly, Michael McGreevy made his big league debut in 2024 and threw 311 pitches, 19% of which were sliders.

The numbers can lie to you. Pallante, the only one of our three pitchers to appear in both years, lowered his slider rate. But in 2023, Pallante and Wainwright combined for a 7% slider rate. In 2024, Pallante and McGreevy combined for a 17.1% slider rate. That sounds like a huge change in behavior – but it’s actually just a change in population composition.

The story we all think about isn’t Wainwright retiring and handing his innings to McGreevy and Pallante. It’s Brayan Bello going from 17.5% sliders to 28% sliders while pitching a similar innings load – something that also happened in 2024, just so we’re clear.

To measure how existing pitchers are changing their slider usage, we shouldn’t look at the overall rate. We should instead look at the change in each pitcher’s rate. That’s a truer reflection of the question I’m asking, or at least I think it is. And that answer differs from the chart I showed you up at the top of this article.

There were 315 pitchers who threw at least 50 innings in 2023 and 2024, and threw at least one slider in each of those two years. Of those 315 pitchers, 142 increased their slider usage, 24 kept their usage the same, and 149 decreased the rate at which they threw sliders. The story was similar from 2022 to 2023. There were 216 pitchers who fit the criteria in those years; 90 increased their slider usage, 19 kept theirs the same, and 107 decreased the rate at which they used the pitch. From 2021 to 2022, the effect went the other way; 122 pitchers threw sliders more frequently in 2022 than they did in 2021, 22 kept their usage the same, and 74 decreased their usage.

Put that way, the change is quite striking. The slider craze kicked off in earnest in 2017. From 2016-2017, 114 pitchers increased their slider usage and 89 decreased theirs. That rough split persisted in 2017-2018 and 2018-2019. Everything around the 2020 season is a little weird thanks to the abbreviated schedule, but the basic gist – more pitchers increasing slider usage than decreasing slider usage – was true in every pair of years from 2014-2015 through 2021-2022.

That sounds more like a trend than the overall rate of sliders thrown. Graphically, it looks like this:

Let’s put that in plain English. From 2015, the start of the spike in slider usage, through 2022, there were far more pitchers increasing their slider frequency than decreasing it. On average across those years, 1.3 pitchers threw more sliders for every one pitcher who threw fewer. In the past two years, that trend has reversed; more pitchers are reducing their reliance on sliders than increasing it. The population is going to continue to change – they don’t make a lot of Adam Wainwrights these days – but on a per-pitcher basis, the relentless increase in slider usage has halted.

I tried a few other ways of looking at this phenomenon. I held pitcher workloads constant from year one and applied year two slider rates to each pitcher (pitchers who only threw in year one obviously keep their rate unchanged). The same trend held – the last two years have seen a sharp divergence from the boom times of 2015-2022. I looked at the percentage of starters who started using a slider more than some other pitch in their arsenal and compared it to the ones who de-emphasized it; same deal. I also should note that I’ve grouped sweepers and slurves among the sliders for this article, so this reversal is not about pitchers ditching traditional sliders to get in on the sweeper craze.

No matter how you slice it, we’ve seemingly entered a new phase of pitch design. For a while, most pitchers took a hard look at what they were throwing and decided they needed more sliders. Now, though, it appears that we’ve reached an equilibrium point. Some pitchers still want more. Some think they’re throwing enough, or even a hair too many. Now splitters are on the rise, and hybrid cutters are starting to eat into sliders’ market share.

It’s far too early to say that sliders are on the decline. Factually speaking, they’re not. But to me, at least, it’s clear that the last two years are different than the years before them when it comes to the most ubiquitous out pitch in baseball. Sure, everyone has a slider now – but in the same way that four-seam fastballs were inevitable right until sinkers made a comeback, the slider is no longer expanding its dominance among secondary pitches. An exciting conclusion? I’m not sure. But it’s certainly backed by the evidence.

As of the time I’m writing this article, roughly half of our Top 50 free agents have signed new contracts this offseason. That sounds like a great time to take a look at how the market has developed, both for individual players and overall positional archetypes. For example, starting pitchers have been all the rage so far, or so it seems. But does that match up with the data?

I sliced the data up into three groups to get a handle on this: starters, relievers, and position players. I then calculated how far off both I and the crowdsourced predictions were when it came to average annual value and total dollars handed out. You can see here that I came out very slightly ahead of the pack of readers by these metrics, at least so far:

To be fair, none of us have done particularly well. The last two years I’ve run this experiment, I missed by around $1 million in average annual value, and the crowd missed by between $1 and $2 million. Likewise, I’ve missed by roughly $10 million in average annual value per contract, with the crowd around $18 million. This year, the contracts have been longer than I expected, and richer than you readers expected, though you did a much better job on a relative basis when it came to predicting total dollar outlay. We were all low on every category, though, across the board.
Read the rest of this entry »