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 his prime — and it was not a long prime — nobody hit a majestic home run like Joey Gallo. It was something about the violence of the swing, the loopy lefty uppercut, the two-handed follow-through, and the way he’d stand up straight right after contact, a confirmation that the baseball was indeed crushed.

Those high arcing blasts powered one of the more bizarre careers of his generation. In the heart of the Three True Outcomes era, he was its emperor, threatening to lead the league in either walk rate, strikeout rate, or home runs in any given year.

Sadly, time passes. Those with prominent residences on Gallo Island now fear foreclosure proceedings. The big slugger has fallen on hard times; last week, he signed a minor league contract with the Chicago White Sox. A non-guaranteed deal with the team that just set the major league record for losses carries some pretty clear subtext. Gallo is hanging off the cliffside of his career, one finger latched to a jagged rock.

It all feels too soon. He’s just 31 years old, a normal and cool age that is in no way old. As Tom Tango’s research shows, bat speed generally starts to decline right at this point, not years before. But even at his best, Gallo lived at the extremes. In his magical 2019 half-season, which unfortunately was cut short by a broken hamate bone, he posted a .635 xwOBA on contact. Across 2,865 player seasons in the Statcast era, only 2017 Aaron Judge topped that figure.

At his apex, nobody — save for one of the greatest hitters of all-time — crushed the baseball like Joey Gallo. He paired that supreme power with some of the lowest chase rates in the league, giving him enough on-base juice to offset the batting averages that made boomers want to gauge out their eyes. That excellent plate discipline allowed him to hunt mistakes in the middle of the plate, mostly fastballs and hanging sliders. His swing was geared for these middle-middle meatballs, and his 70-grade batting eye allowed him to lay off most pitches on the black. Yes, when he got into a two-strike count and was compelled to swing, he most likely was going to come up empty. But he forced pitchers to battle.

Over the last handful of years, though, the other extreme in Gallo’s game eclipsed his prodigious power. Remember those 2,865 player seasons? Two of Gallo’s seasons rank first and second across the decade in the percentage of all swings resulting in whiffs. That decade-leading 44.3% whiff rate came in the 2023 season, when he still managed, I must note, to run an above-average wRC+.

In retrospect, it all started to go downhill after that infamous July 2021 trade to the Yankees. Gallo was coming off perhaps his finest month as a big leaguer, striking out “just” 25.3% of the time, walking nearly as frequently as he struck out, and mashing 10 homers. Painfully, he hit just .160 following the trade, and despite his 16.2% walk rate and usual home run pace, his anemic batting average turned him into a villain with the Yankees. After another dismal half-season, the Yankees shipped him off to the Dodgers; things didn’t get much better in Los Angeles, where he ran strikeout rates that dipped into the 40s for the first time.

Gallo hit free agency for the first time after that 2022 season, and since then teams have made increasingly small bets on his ability to return to his prime form. It started with the Twins in 2023, who paid him $11 million for a single year’s services. Next up were the Nationals, who handed out a $5 million deal, and he turned in his worst season yet. So now here we are, with Gallo at the bleakest end of the baseball universe.

It isn’t hard to see how things ended up like this. Gallo is a big guy who swings hard, and the bills have come due for his high-impact style of play. Over the last two seasons, he battled a sprained shoulder, a strained oblique, a foot contusion, and two separate hamstring strains, the second of which forced him out of action for nearly two months. He even came down with a case of pink eye. His body appears to be breaking down rapidly, and you can almost see the effects of this as he sets up in the box, constantly shifting and readjusting like he’s in the middle seat on a Spirit flight.

Perhaps as a result of all this discomfort, Gallo’s carrying tool is showing signs of erosion. In the second half of 2023, his average bat speed of 73.9 mph ranked in the 84th percentile of hitters. That 2023 mark is the first bat speed data available to the public, and one can imagine that at his peak, Gallo could swing a few miles per hour harder than that, ranking among the likes of Giancarlo Stanton and Kyle Schwarber as one of the fastest swingers in the league.

Gallo’s bat slowed even further in 2024. His average bat speed dipped 1.5 mph, dropping him into the fat part of the bell curve, only a tick above the major league average of 71.3 mph. His once-excellent plate discipline now looks more like passiveness. White Sox manager Will Venable says Gallo will primarily play first base. He is definitively an aging slugger, and his career depends on whether he can revive his famous power skills.

It’s possible that some of Gallo’s bat speed decrease was intentional; in 2023, only Trey Cabbage squared up fewer balls, and that mark improved slightly in 2024. But it’s Joey Gallo. If you have him on your team, you don’t want him trading off power for contact because he’s never going to make enough contact for that to matter. You want him swinging out of his shoes, walloping tanks into the stratosphere.

As my editor Matt Martell pointed out, the White Sox have an institutional history of old slugger resuscitation attempts. There were the ill-fated midseason acquisitions of Manny Ramirez and Ken Griffey Jr., a deal for post-peak Andruw Jones, even the four-year deal they handed out to Gallo’s evolutionary predecessor, Adam Dunn. All these guys landed on the South Side hoping to recapture the magic one last time.

Unlike those other players, though, there are no guarantees that Gallo makes the team, especially because Miguel Vargas is out of minor league options. But let’s just dream for a minute that Gallo took up yoga or any of the other offseason workout routines that prompt players to boast that they’re in the best shape of their lives. Picture this: a .190 average, a 35% strikeout rate, 30 home runs, a permanent spot in the middle of the order against right-handed pitchers. Gallo is one of the strangest and most spectacular players I’ve ever seen. I’m crossing my fingers he gets one last go.

Let’s get this out of the way at the start: The Mariners are pretty good. Their starting pitching is incredible, and some projections systems even think they have a top-10 offense. This is not a Pirates situation, where a core led by Paul Skenes on a league-minimum contract is somehow projected to finish well under .500. In Seattle, the pieces are almost all there. Sadly for fans, “almost all there” might well define this era of Mariners baseball.

The latest expression of Seattle’s complacency came last week, when the team brought back Jorge Polanco on a one year, $7.75 million contract. (The deal is pending a physical.) According to a report from Adam Jude at the Seattle Times, Polanco’s signing means the “Mariners’ roster is effectively set.” For those counting at home, $3.5 million for 37-year-old Donovan Solano, a trade for Austin Shenton, and the Polanco deal represent the entirety of Seattle’s offseason roster upgrades. The Mariners missed the playoffs by one game in 2023 and 2024; they missed it by two games in 2021. They are always good but never great. And the team — or at least ownership — appears totally fine with that. Read the rest of this entry »

You might not know the name Jack Kochanowicz. It’s a tricky name to pronounce, after all. (Ko-hawn-o-witz). He also made his major league debut on the very day in July when the Angels’ playoff odds hit 0.0%. So if this is your first Jack Kochanowicz experience, just know that he’s capable of doing stuff like this:

Another thing you should know: No pitcher last season missed fewer bats. Out of 351 pitchers with at least 50 innings pitched in the 2024 season, Kochanowicz’s 9.4% strikeout rate ranked 351st.

I’ve been fascinated by Kochanowicz because of this contradiction. He can ramp his heater up to 99 mph, and yet his K/9 started with a three. What gives? Read the rest of this entry »

It must have been fifth grade or so when I encountered the “compare and contrast” essay prompt for the first time. I remember thinking: What the hell? These two passages were written by different people. Why is it on me to tell you what is similar about them?

Over the years, I got better at these prompts. But it appears I’ve regressed. Two relievers signed eight-figure contracts last week. What’s similar? They both closed out games for World Series-winning teams in the 2020s, will likely handle the eighth inning for their new employers, and were born in the glorious and blessed year of 1993. What’s different? One throws right-handed, one throws left-handed. One signed with a contender; one perhaps got paid a premium so his team can try to avoid an MLBPA grievance.

But there are limits to the illuminating qualities of comparison. These days, individualized analysis is required to assess the effectiveness of a pitcher, so that’s how this post will proceed today. A.J. Minter and José Leclerc will earn life-changing quantities of money to chuck leather a few dozen times. Let’s find out why. Read the rest of this entry »

It wasn’t supposed to go down like this. After a sensational contract year — striking out 30% of hitters, posting the lowest walk rate of his career, bedazzling his jewelry cabinet with a World Series ring — Jack Flaherty looked like he was set to make a boatload of money. Days after his 29th birthday, we here at FanGraphs ranked Flaherty eighth among our Top 50 Free Agents, one spot behind his former high school teammate, Max Fried.

Early in December, Fried blew away expectations, inking an eight year, $218 million deal with Yankees. And he wasn’t the only one. Starters from Blake Snell to Luis Severino landed surprisingly lucrative contracts; when Ben Clemens checked in last week on his free agency projections, he noted that he’d under-projected the deals for starting pitchers by about $17 million on average. His takeaway: “Pitchers are getting paid this winter.”

Not all of them, it turns out. As the calendar creeps uncomfortably close to the start of spring training, the youngest available starting pitcher in free agency finds himself without an employer. Every couple weeks, a sparsely sourced rumor about Flaherty bubbles up on MLB Trade Rumors — there’s “mutual interest” with the Orioles, the Tigers have “some interest” in a reunion — but for a large part of the offseason, it’s been silence on the Flaherty beat.

The most substantial of these rumors flowed from the estimable pen of Ken Rosenthal over the weekend. Rosenthal and Will Sammon reported in The Athletic that Flaherty was “open to considering” a “short-term deal” with a “high average annual value.” The hot market for starters and the comparatively cool market for Flaherty suggest that, unlike the rest of the nominal “front-end” starting pitchers at the top of the market, something about him scares teams. All of this leads me to ask: What’s the matter with Jack Flaherty?

One obvious answer is the track record. On some level, teams are going to be somewhat hesitant to commit serious resources to anyone whose last healthy and effective season before 2024 came prior to the pandemic. Rosenthal and Sammon wrote in their report that “teams perhaps want to see Flaherty put together two consecutive seasons of elite performance.” And there are the shoulder issues in 2021 and 2022 that limited him to just 114 1/3 innings over that two-year span.

But I don’t think the track record tells the full story. I think the weak Flaherty market comes down to concerns about his 2024 season itself. Specifically, I think teams are worried about his fastball.

They get there in different ways, but the three pitchers who received $200 million (or thereabouts) contracts this winter all have plus fastballs. Snell throws the prototypical ace four-seamer, averaging 96 mph with 19 inches of induced vertical break. Corbin Burnes’ cutter is one of the signature pitches in baseball, capable of missing bats and neutralizing contact quality against hitters on both sides of the plate. Fried is a bit of a different case — his fastball averaged just 93.9 mph last season — but the shape is totally bizarre relative to his arm angle, resembling Burnes’ cutter from the left side. Fried also throws five other pitches, minimizing the importance of his four-seamer.

Flaherty’s fastball was the slowest of these four primary offerings, averaging 93.3 mph. In September, that dropped all the way to 92.6 mph. At below-average velocities, even a half-tick of heat loss can be brutal. And while the shape of the fastball is unusual — Flaherty throws from a low slot and gets just four inches of horizontal movement, meaning the pitch unexpectedly cuts in a funky fashion — it doesn’t have the sink that allows Fried’s and Burnes’ fastballs to burrow beneath barrels. Also unlike Fried, Flaherty in effect throws just three pitches: his four-seam fastball and the two breaking balls. (He also flashes a changeup and sinker, but last season he used each of those pitches less than 3% of the time.)

Given the mediocrity of his fastball, Flaherty must aim for fine locations. His success can vary. (I want to caveat the following with the fact that the Dodgers have a strong organizational preference for where their pitchers locate their fastballs, which may or may not be the way Flaherty will pitch with a new team.)

I’ll start with his matchups against left-handed hitters, because these were the majority of hitters that Flaherty faced in 2024. After his trade to the Dodgers in late July, the target for his fastball was almost exclusively set up in one location: Low and away.

A handful of times per start, Flaherty tried to climb the ladder, aiming for swinging strikes at the top of the zone. But in the three starts I watched in full, he almost always targeted his fastballs low and away when facing lefties.

Now, as the plot below of his fastball location to lefties shows, his execution wasn’t perfect. Aiming a baseball is really hard. But I’d venture to say that it was pretty good — he hardly yanked any of his fastballs to the glove side, and most of his misses drifted harmlessly off the plate. In any case, the plot tells a clear story: Flaherty was looking to paint with his fastball rather than challenge hitters over the plate.

This sort of nibbling quality with the fastball is perhaps not what teams want to see from their high-paid free agent starter. Snell, Burnes, and even Fried to some extent can throw fastballs with a large margin for error. Flaherty’s margins are thinner.

This is especially true against right-handed hitters, where his glove-side command is not as good. Against righties, Flaherty also frequently targeted low and away. But as the plot below of fastballs to righties shows, Flaherty doesn’t have the same level of command to the outer edge of this side of the plate. Note the lack of dots in that low-away corner compared to the yanked misses off the plate:

To lefties, Flaherty has the luxury of his misses generally drifting off the plate for balls. When he misses his target to righties, however, the miss tends to drift middle-middle. And when you’re missing middle-middle with 93-mph four-seamers, it’s generally not going to turn out well for you. (This might explain part of Flaherty’s reverse splits last season.)

When executed well, the low-and-away target serves an important function — it sets up his two nasty breaking balls, a harder gyro slider at 85 mph and a loopier knuckle-curve at 78 mph. As this pitch plot shows, these two pitches blend together in a deceptive manner, forcing hitters to guess which one is coming:

Flaherty is at his best when he’s mixing in the low fastballs with the two breakers right below the zone. Check out this two-pitch sequence to Ryan O’Hearn. He nails his 0-0 target to get ahead:

On 0-1 — the perfectly executed fastball fresh in O’Hearn’s head — Flaherty buries a curveball right below the previous location, getting O’Hearn to swing way over the pitch:

After a couple of breaking balls in the dirt, Flaherty punches O’Hearn out on a high fastball. With hitters laser-focused on the bottom of the strike zone, that occasional late-count high heater leads to a ton of whiffs. It’s a pretty combo when it works.

But if Flaherty falls behind, there just isn’t a great option to induce weak contact. When the early-count fastball execution is less than perfect, he tends to back himself into a corner. And when he’s forced to come over to the plate with the heater, he can be vulnerable to the long ball. Just ask O’Hearn:

If Flaherty’s fastball velocity remains in that 92-93 range, it will likely be a tradeoff between giving up a few too many walks due to nibbling (as he did early in his career) or risking extra-base damage by coming over the plate.

So, yes, there are reasons to be concerned about Flaherty. But overly fixating on his fastball risks ignoring his upside.

That two-breaking-ball attack works against both righties and lefties; when he gets ahead in the count, there’s almost nobody better. That strikeout rate is no illusion. So the question becomes: How can Flaherty reliably get ahead of hitters?

One option is pitching backwards. Flaherty’s fastball usage in 0-0 counts is roughly 50%. (On the plot below, red represents the four-seamer, gold represents the slider, and blue represents the curveball.) Given the frequency of his slider usage in 3-1 and 3-2 counts (50% and 44.8%, respectively) it follows that he has the confidence to throw it for a strike when he needs it. Mixing in more breaking balls in early counts could take some pressure off the four-seamer.

Flaherty could also use his sinker more often. If his problem at present is mostly with right-handed hitters, the sinker could give him a weak-contact option and a pitch that he feels comfortable throwing on the inner-half of the plate. Notably, the sinker grades out fine by stuff models — PitchingBot, for example, gives it a 56 on the 20-80 scale.

It’s also not impossible that some of his velocity could return. Maybe he no longer can regularly dial up 95-96 mph as he did in his early 20s, but it’s also possible that his late-season swoon can be chalked up to his posting his highest innings total in five years. In the range of velocity that he sat in the later months of the season, every half-tick is crucial, but if he can consistently live at 93-94 mph with the ability to touch 96, that softens many of the concerns.

Concluding this article definitively is challenging. On the one hand, the skittishness of the clubs is perfectly understandable. But plenty of contending teams need starting pitching, and an industry-wide fear of Flaherty’s weaknesses could cost clubs their chance to add someone who just performed like one of the best hurlers in the game.

It’s roughly the midway point of the offseason, and things are starting to slow down. Most of the headline free agents are off the board, clearing the way for the Trevor Williamses, Joe Rosses, and Caleb Thielbars of the world to find their 2025 homes.

Those guys all signed in the final days of 2024, inking modest deals for National League clubs. Here’s a little bit about all three.

Trevor Williams Re-Signs With the Washington Nationals

Read the rest of this entry »

It’s been dark here at FanGraphs for a few days, so admit it — you’re desperate to read anything right now. How about a roundup of analysis on three pitchers that went off the market right before our holiday hiatus?

Griffin Canning, Michael Soroka, and Patrick Sandoval all fit somewhere between the back of their new team’s rotation or the front of its starter depth; each received deals commensurate with those expectations. If the going rate for a fourth starter these days is something like $15 million AAV (Alex Cobb got one year and $15 million, Matthew Boyd got two years and $29 million), this trio is probably one tier below that.

Do these three signings, grouped together, mean anything in particular? Probably not. Each year, the starter/reliever binary grows blurrier, and perhaps someday, every pitcher will throw exactly three innings and the distinction will disappear completely. Perhaps each of these signings brings us closer to that day; Soroka, in particular, seems best served to go through a lineup once and then head out on his way. For various reasons, the expectation for all of these pitchers should be somewhere in the 80- to 120-inning range for the 2025 season. But for now, no further trends will be drawn. Without further ado, here is the lowdown on the three hurlers.

Griffin Canning

Canning drew some attention on the pitching nerd internet earlier this year due to the remarkably unremarkable shape of his fastball. The image below is courtesy of Max Bay’s dynamic dead zone app:

the deadest zone. cc: @choice_fielder https://t.co/3pkdXbfXhA pic.twitter.com/krJWXKN4yN

— Alex "Oxlade" Chamberlain (@DolphHauldhagen) October 31, 2024

Because Canning throws his fastball from a roughly league-average arm angle (45°), a league-average release height (5.8 feet), and with league-average ride (16.2 inches of induced vertical break), the pitch — in theory! — moves on a trajectory that hitters expect. (I say “in theory” because, as Remi Bunikiewicz pointed out, Canning does a great job hiding his fastball during the windup, complicating any perceptive analysis.)

This fastball was the bane of Canning’s existence in 2024. He did qualify for the ERA title, something only 57 other pitchers could claim they did, but his 5.26 FIP was worst among those qualified starters, and his strikeout rate was third worst. That strikeout rate dropped nearly eight percentage points from 2023 to 2024, and the performance against his fastball explained essentially all of that drop. The whiff rate on Canning’s three other primary pitches stayed virtually the same; on the fastball, the percentage of swings that resulted in misses went from 28% in 2023 to just 14% in 2024.

A drop in velocity appears to be the main culprit for the decline in performance. The four-seamer averaged 94.7 mph in 2023; that dipped to 93.4 mph in 2024. Could a 1.5-mph difference in velocity be the entire explanation? I’m inclined to think that the answer is mostly yes. But it’s also possible that the decline in slider quality impacted batter performance against his fastball. Canning’s death ball slider dropped three fewer inches relative to 2023, reducing the separation between his fastball and his primary out pitch against right-handed hitters.

Could a reduced role help Canning return to his prior form? These considerations could be part of the plan. The Mets employ something like eight starters; Canning sits outside the favored five. Assuming perfect health, it’s likely that they will deploy him in two- or three-inning bursts, perhaps allowing him to get back to that mid-90s velocity on the heater. Even in a swingman role, the $4.25 million contract makes good sense — with fewer workload responsibilities, it doesn’t feel unreasonable to expect Canning to deliver something like a 4.00 ERA over 100ish innings. And if injuries do strike the rotation, he can stretch out to a starter’s workload. Either way, there’s a role to play in this era where quality innings can be difficult to come by, especially in the late summer months.

Michael Soroka

Soroka exploded after a midseason move to the White Sox bullpen. As a reliever, Soroka struck out 39% of the hitters he faced, which would’ve ranked second in all of baseball.

Curiously, this wasn’t a case of Soroka ramping up the stuff over 15-pitch spurts. Unlike those pitchers topping the strikeout leaderboards — Mason Miller, Edwin Díaz, Josh Hader — Soroka did it mostly in chunky multi-inning appearances. Soroka pitched 36 innings out of the bullpen; all but 5 2/3 of them came in appearances that spanned two innings or more. In those slightly shorter appearances — he averaged nearly five innings per appearance as a starter and 2 1/3 as a reliever — the strikeout rate somehow tripled.

After moving full-time to relief work, Soroka added 1.5 mph to his four-seam fastball. But the four-seamer isn’t anything special; instead, at 94 mph with dead zone-ish movement, it’s mostly there to set up the slider, which generated nearly a 42% whiff rate.

What’s so special about the slider? It isn’t the velocity — it averages just 82.2 mph, well below the average for major league sliders. But its shape is distinct. There are slower curveballs that resemble the movement profile, but outside of Bryan Abreu, nobody really throws a slider with the combination of depth and sweep that Soroka manages to get. Starting May 18, when Soroka shifted to a bullpen role, the slider averaged -4.5 inches of induced vertical break with 5.2 inches of sweep, moving sharply on two planes.

But averages obscure the full truth. Soroka can also manipulate the pitch to move in a variety of break patterns. Look at the range of movement profiles on his slider, seen in yellow on his pitch plot below:

Soroka can firm it up, throwing it more like a gyro slider at 84 mph with zero inches of induced vertical break:

But he can also bend it like a curveball, dropping over 10 inches more than his firmest sliders:

(Look at poor Spencer Torkelson there — I think he was expecting the gyro.)

Between the identical frequency of the fastball and slider, the distinct two-plane movement profile, and the diversity of potential shapes, Soroka had batters swinging and missing more than almost any pitcher in baseball.

Evidently, the Nationals, who gave Soroka $9 million on a one-year deal, plan to use him as a “starter.” Given his usage patterns as a reliever, I’m not exactly sure what that means. I would expect that the Nationals will tell Soroka to let it loose for 60 or so pitches, just as he did in Chicago, and he’ll take on 12 or 13 hitters in a game. Like Canning, I think Soroka will end up closer to 90 innings than 180, letting his best stuff cook in outings that sit somewhere between a one-inning shutdown reliever and a starter trying to turn the lineup over three times.

Patrick Sandoval

Sandoval, who signed a two-year, $18.25 million deal with the Red Sox, is a perfect fit for their “no fastballs” organizational philosophy. This guy hates four-seamers now — they made up just 16% of his pitches in his injury-shortened 2024 campaign, by far a career low. Regardless of batter handedness, Sandoval mixes in all six of his pitches, but he works them in differently depending on whether he’s facing a righty or lefty. A plurality of his pitches to righties were changeups; to lefties, Sandoval spammed his slider and sweeper over half the time.

As one would expect with a pitcher who throws all that junk, Sandoval struggles to get the ball in the strike zone. He ran a 10% walk rate last year; even in his excellent 2022 campaign, in which he racked up 3.7 WAR, his walk rate was above 9%. The walks are just part of the package with Sandoval, but the hope is that at his best, he can pitch around them, striking out enough hitters and staying off enough barrels with his diverse pitch mix and refusal to throw anything straight.

Sandoval is likely to pitch the fewest innings of this trio in 2025. He tore his UCL and was shut down in mid-June before undergoing Tommy John surgery, so he’ll miss a big chunk of the upcoming season. When he returns, it figures that he will assume a traditional starter’s workload, though following the Walker Buehler signing, Boston’s rotation looks pretty packed. Ultimately, this deal is mostly a 2026 play, with some nice depth for the end of next year as a bonus.

Conclusion

None of these guys is too exciting. All of them have stanky fastballs. But each has a reason to believe that he might contribute surplus value on a modest deal. In the end, that’s what a minor pitcher signing is all about.

Right after FanGraphs published my piece on the Kirby Index, the metric’s namesake lost his touch. George Kirby’s trademark command — so reliable that I felt comfortable naming a statistic after him — fell off a cliff. While the walk rate remained under control, the home run rate spiked; he allowed seven home runs in May, all on pitches where he missed his target by a significant margin.

Watching the namesake of my new metric turn mediocre immediately following publication was among the many humbling experiences of publishing this story. Nevertheless, I wanted to revisit the piece. For one, it’s December. And writing the story led me down a fascinating rabbit hole: While I learned that the Kirby Index has its flaws, I also learned a ton about contemporary efforts to quantify pitcher command.

But first, what is the Kirby Index? I found that release angles, in concert with release height and width, almost perfectly predicted the location of a pitch. If these two variables told you almost everything about the location of a pitch, then a measurement of their variation for individual pitchers could theoretically provide novel information about pitcher command.

This got a few people mad on Twitter, including baseball’s eminent physicist Alan Nathan and Greg Rybarczyk, the creator of the “Hit Tracker” and a former member of the Red Sox front office. These two — particularly Rybarczyk — took issue with my use of machine learning to make these predictions, arguing that my use of machine learning suggested I didn’t understand the actual mechanics of why a pitch goes where it goes.

“You’re spot on, Alan,” wrote Rybarczyk. “The amazement that trajectory and launch parameters are strongly associated with where the ball ends up can only come from people who see tracking data as columns of digits rather than measurements of reality that reflect the underlying physics.”

While the tone was a bit much, Rybarczyk had a point. My “amazement” would have been tempered with a more thorough understanding of how Statcast calculates the location where a pitch crosses home plate. After publication, I learned that the nine-parameter fit explains why pitch location could be so powerfully predicted by release angles.

The location of a pitch is derived from the initial velocity, initial release point, and initial acceleration of the pitch in three dimensions. (These are the nine parameters.) Release angles are calculated using initial velocity and initial release point. Because the location of the pitch and the release angle are both derived from the 9P fit, it makes sense that they’d be almost perfectly correlated.

This led to a reasonable critique: If release angles are location information in a different form, why not just apply the same technique of measuring variation on the pitch locations themselves? This is a fair question. But using locations would have undermined the conclusion of that Kirby Index piece — that biomechanical data like release angles could improve the precision of command measurements.

Teams, with their access to KinaTrax data, could create their own version of the Kirby Index, not with implied release angles derived from the nine-parameter fit, but with the position of wrists and arms captured at the moment of release. The Kirby Index piece wasn’t just about creating a new way to measure command; I wanted it to point toward one specific way that the new data revolution in baseball would unfold.

But enough about that. It’s time for the leaderboards. I removed all pitchers with fewer than 500 fastballs. Here are the top 20 in the Kirby Index for the 2024 season:

And here are the bottom 20:

A few takeaways for me: First, I am so grateful Kirby got it together and finished in the top three. Death, taxes, and George Kirby throwing fastballs where he wants. Second, the top and bottom of the leaderboards are satisfying. Cody Bradford throws 89 and lives off his elite command, and Joe Boyle — well, there’s a reason the A’s threw him in as a piece in the Jeffrey Springs trade despite his otherworldly stuff. Third, there are guys on the laggard list — Seth Lugo and Miles Mikolas, in particular — who look out of place.

Mikolas lingered around the bottom of the leaderboards all year, which I found curious. Mikolas, after all, averages just 93 mph on his four-seam fastball; one would imagine such a guy would need to have elite command to remain a viable major league starter, and that league-worst command effectively would be a death sentence. Confusing this further, Mikolas avoided walks better than almost anyone.

Why Mikolas ranked so poorly in the Kirby Index while walking so few hitters could probably be the subject of its own article, but for the purposes of this story, it’s probably enough to say that the Kirby Index misses some things.

An example: Mikolas ranked second among all pitchers in arm angle variation on four-seam fastballs, suggesting that Mikolas is intentionally altering his arm angle from pitch to pitch, likely depending on whether the hitter is left-handed or right-handed. This is just one reason why someone might rank low in the Kirby Index. Another, as I mentioned in the original article, is that a pitcher like Lugo might be aiming at so many different targets that it fools a metric like the Kirby Index.

So: The Kirby Index was a fun exercise, but there are some flaws. What are the alternatives to measuring pitcher command?

Location+

Location+ is the industry standard. The FanGraphs Sabermetric library (an incredible resource, it must be said) does a great job of describing that metric, so I’d encourage you to click this hyperlink for the full description. The short version: Run values are assigned to each location and each pitch type based on the count. Each pitch is graded on the stuff-neutral locations.

Implied location value

Nobody seems particularly satisfied with Location+, including the creators of Location+ themselves. Because each count state and each pitch type uses its own run value map to distribute run value grades, it takes a super long time for the statistic to stabilize, upward of hundreds of pitches. It also isn’t particularly sticky from year to year.

The newest version of Location+, which will debut sometime in the near future, will use a similar logic to PitchProfiler’s command model. Essentially, PitchProfiler calculates a Stuff+ and a Pitching+ for each pitcher, which are set on a run value scale. By subtracting the Stuff+ run value from the Pitching+ run value, the model backs into the value a pitcher gets from their command alone.

Blobs

Whether it’s measuring the standard deviation of release angle proxies or the actual locations of the pitches themselves, this method can be defined as the “blob” method, assessing the cluster tightness of the chosen variable.

Max Bay, now a senior quantitative analyst with the Dodgers, advanced the Kirby Index method by measuring release angle “confidence ellipses,” allowing for a more elegant unification of the vertical and horizontal release angle components.

Miss distance

The central concern with the Kirby Index and all the blob methods, as I stated at the time, is the single target assumption. Ideally, instead of looking at how closely all pitchers are clustered around a single point, each pitch would be evaluated based on how close it finished to the actual target.

But targets are hard to come by. SportsVision started tracking these targets in the mid-2010s, as Eno Sarris outlined in his piece on the state of command research in 2018. These days, Driveline Baseball measures this working alongside Inside Edge. Inside Edge deploys human beings to manually tag the target location for every single pitch. With these data in hand, Driveline can do a couple of things. First, they created a Command+ model, modifying the mean miss distances by accounting for the difficulty of the target and the shape of a pitch.

Using intended zone data, Driveline also shows pitchers where exactly they should aim to account for their miss tendencies. I’m told they will be producing this methodology in a public post soon.

Catcher Targets (Computer Vision)

In a perfect world, computers would replace human beings — wait, let me try that sentence again. It is expensive and time-intensive to manually track targets through video, and so for good reason, miss target data belong to those who are willing to pay the price. Computer vision techniques present the potential to produce the data cheaply and (therefore) democratically.

Carlos Marcano and Dylan Drummey introduced their BaseballCV project in September. (Drummey was hired by the Cubs shortly thereafter.) Joseph Dattoli, the director of player development at the University of Missouri, offered a contribution to the project by demonstrating how computer vision could be used to tag catcher targets. The only limitation, Joseph pointed out, is the computing power required to comb through video of every single pitch.

There are some potential problems with any command measurement dependent on target tracking. Targets aren’t always real targets, more like cues for the pitcher to throw toward that general direction. But Joseph gets around this concern by tracking the catcher’s glove as well as his center of mass, which is less susceptible to these sorts of dekes. Still, there’s a way to go before this method scales into a form where daily leaderboards are accessible.

The Powers method

Absent a raft of public information about actual pitcher targets, there instead can be an effort to simulate them. In their 2023 presentation, “Pitch trajectory density estimation for predicting future outcomes,” Rice professor Scott Powers and his co-author Vicente Iglesias proposed a method to account for the random variation in pitch trajectories, in the process offering a framework for simulating something like a target. (I will likely butcher his methods if I try to summarize them, so I’d encourage you to watch the full presentation if you’re interested.)

The Powers method was modified by Stephen Sutton-Brown at Baseball Prospectus, who used Blake Snell as an example of the way these targeting models can be applied at scale to assess individual pitchers. First, Sutton-Brown fit a model that created a global target for each pitch type, adjusting for the count and handedness of each batter. Then, for each pitcher, this global target was tweaked to account for that pitcher’s tendencies. Using these simulated targets, he calculated their average miss distance, allowing for a separation of the run value of a pitcher’s targets from the run value of their command ability.

“Nothing”

On Twitter, I asked Lance Brozdowski what he saw as the gold standard command metric. He answered “Nothing,” which sums up the problem well. This is a challenging question, and all the existing methods have their flaws.

There are ways that the Kirby Index could be improved, but as far as I can tell, the best way forward for public command metrics is some sort of combination of the final two methods, with active monitoring of the computer vision advancements to see if consistent targets can be established.

But one would imagine the story is completely different on the team side. By marrying the KinaTrax data with miss distance information, these methods could potentially be combined to make some sort of super metric, one that I imagine gets pretty close to measuring the true command ability of major league pitchers. (In a video from Wednesday, Brozdowski reported on some of the potential of these data for measuring and improving command, as well as their limitations.) The public might not be quite there, but as far as I can tell, we’re not that far off.

Editor’s Note: This story has been updated to include Vicente Iglesias as a co-author on the 2023 presentation, “Pitch trajectory density estimation for predicting future outcomes.”

Technically, there wasn’t much at stake. Even though Mason Miller was looking to protect a one-run lead with two outs in the 10th inning of an early September clash, the A’s and Rangers were playing out the string, battling for wins in a lost season. For Wyatt Langford, however, it meant something more.

On the first pitch, Miller fired 101 mph down the middle. Langford was aggressive, fouling it straight back for strike one. He watched 102 mph sail high, then flicked his bat to foul off an up-and-in 101-mph heater to fall into a 1-2 hole. A slider sailed outside before he fouled off pitches at 102 mph and 103 mph to stay alive, and then he laid off two wicked sliders to secure the base on balls. Langford faced down some of the best stuff in baseball, and emerged the victor.

It was just a walk, but it sparked a resurgence. After a dismal five months, Langford exploded in September, posting a 180 wRC+ and leading the American League in WAR. In the series following his successful encounter with Miller, he blasted titanic tanks off Luke Weaver and Clay Holmes, catching up to heaters at the top of the zone and depositing hanging sweepers deep into the left field bleachers.

It led to a question: Was September reflective of Langford’s new level? The answer, in part, was conditional on prior expectations.

And the expectations were certainly high heading into the season. After landing in Texas with the fourth overall pick in the 2023 draft, Langford incinerated the high minors, posting a .360/.480/.677 line across four levels and 200 plate appearances. ZiPS pegged Langford’s 50th percentile outcome above three wins, reasonably confident that Langford would go from the SEC to an above-average regular in the span of a year. As Cactus League play began, the hype train picked up steam; Langford hit .365 with six homers, leaving no doubt as to whether he’d start the year on the big league roster.

It turns out hitting in the majors is hard. No longer was Langford tasked with fending off the pitching staff of Mississippi State or the El Paso Chihuahuas; instead, he had to deal with Chris Sale sliders, Hunter Greene fastballs, and Tarik Skubal changeups.

Fittingly, he looked like a rookie. The plate discipline was there early on; his walk and strikeout rates hovered around league average, suggesting that Langford was not completely overmatched like his rookie counterpart Jackson Chourio, who struck out over 32% of the time in March and April. But Langford’s batted ball quality was lacking. He slugged just .314 in April, lifting heaps of lazy fly balls into outfield gloves.

Whereas Chourio found his footing in the summer months, logging a 144 wRC+ in June and never looking back, Langford’s line remained stubbornly subpar — until the final month of the season. Finally, as the Rangers slogged through their September schedule, Langford went bananas. His .300/.386/.610 line and excellent baserunning led to 1.6 WAR in that month alone, trailing only the infernal Shohei Ohtani.

There are a few potential stories to tell about the Langford rookie campaign. One is that he ran into a few poorly located pitches across a small sample. Another is that Langford made his adjustments, just as Chourio clearly did, accumulating enough experience against major league stuff to leverage his immense tools.

ZiPS, as always, splits the difference. The projection system sees Langford as a 3.8 WAR, 128 OPS+ guy next year, baking in Langford’s transcendent minor league results with a slight skill bump as he heads into his age-23 season.

But splitting the difference is no fun. This approach, applied to players across the league, will lead to more accurate projections. There is no good empirical reason to weigh September results more heavily in the next season’s forecast. But there is a temptation, at least on my end, to believe that Langford is going to be the player we saw in September moving forward.

In this version of the narrative, the expectation for Langford’s sophomore campaign isn’t just an All-Star 4-WAR season, as ZiPS forecasts; it’s something more like 5 WAR as the 50th percentile expectation, mirroring the age-23 projections for recent breakouts Corbin Carroll and Julio Rodríguez.

To make that argument persuasively, it would require evidence that Langford identified and then fixed the flaws that held him back across his first 400 or so plate appearances. And there is at least some reason to believe he did.

There is no one culprit for a hitter’s poor performance. The reasons are layered and complex; it could be an issue with certain pitch types or certain locations, for example. In Langford’s case, it seemed like at least one of the issues was structural, tied to his hitting mechanics. Both he and Rangers offensive coordinator Donnie Ecker believed his swing was too vertical.

Even two weeks into the season, it was clear that Langford needed to adjust. There was one clear potential area of improvement: His distribution of weight on his swing. In a story written by Steve Kornacki (no, not that Steve Kornacki) at MLB.com, Ecker was quoted as saying that Langford’s mechanical tendencies needed a reboot.

“[Langford] came from college and regularly has not faced breaking balls that are breaking 18 to 20 inches,” Ecker told Kornacki. “So, some of the body position he was in in college is now starting to evolve. If you look where the pressure is at, maybe in college it was on his back side. All of the best hitters in the big leagues, their pressure, when they land [on swings], is in the middle of their body. So, he’s slowly evolving from a guy that’s back, to having to get over the center that’s in the middle of our body.”

Esteban Rivera, FanGraphs’ resident hitting mechanics expert, explained to me that loading up weight on the backside makes it easier for hitters to whip their barrel under the ball and therefore generate power. This approach works well in college, where hitters aren’t generally exposed to high velocity and see a lot of mistake pitches. It works less well when Brandon Pfaadt is spinning sweepers that teleport across the width of the plate. Esteban also pointed out that fastballs on the inner half or sliders off the plate could trouble a hitter with a swing oriented toward crushing middle-middle mistakes.

Langford, for his part, appeared well aware of the problem.

“We’re working on getting back to that center mass, and not staying back too much,” Langford said in April. “It’s caused me to swing a little more up than I wanted, and I’m leveling out my swing. That’s helping me see the ball better.”

The early results were not favorable. Langford’s average launch angle climbed each month, from 16 degrees in April all the way to 23 degrees in August. Perhaps as a result, he was flummoxed by sweepers and sliders thrown by same-handed pitchers; through August, his wOBA was .234 on these pitches. He was even worse on hard inside fastballs; his .205 wOBA on high-velocity sinkers and four-seamers thrown on the inner half ranked among the worst in the league.

But in September, the swing leveled out. Langford’s average launch angle in September — 11 degrees — was the lowest of any month in the 2024 season. And the results — perhaps coincidentally, perhaps not — followed.

On inside heat, Langford never really adjusted. But he started crushing fastballs left out over the plate as well as hanging breaking balls, staying back long enough to identify spin and punishing mistakes. A good portion of his damage came on swings like this double against Marcus Stroman, lasering sliders at the knees into the right-center field gap:

These improvements coincided with a change to his setup. In April, he was hunched at the moment of the pitcher’s foot strike, looking to my amateur eye more like a slap hitter:

But during his month of destruction, Langford stood much more upright, ready to attack balls at any depth or width.

Langford’s apparent mechanical adjustments, prospect pedigree, and chronologically convenient damage distribution leads to questions about the nature of projections. Prospect evaluators, including our own Eric Longenhangen, were unbothered by his slow start. In his Top 100 Prospects Update in May, as Langford sat sidelined with a hamstring injury, Eric wrote that he expected Langford would “be an offensive star upon his return, and probably pretty quickly,” noting that the “huge tools and plate coverage” remained intact. It was just a matter of adjusting.

In his final month of the season, Langford looked like the offensive star Eric expected. In most cases, a huge month should not be cause for altering a projection. Langford, though, could be an exception.