As the man who inspired Brad Pitt’s most memorable role once said, “My shit doesn’t work in the playoffs.” Assuming Billy Beane wasn’t explaining an October Metamucil purchase to a grocery store cashier who simply asked how his day was going, what Beane likely meant was that the statistics used to construct his major league rosters don’t accrue large enough samples during postseason series to eventually even out in his favor. Over the course of 162 games, a team’s production settles into a reasonable representation of the squad’s true talent. But zoom in on any random seven-game stretch and the team on the field might look like a bunch of dudes in baseball player cosplay.

What applies to team outcomes applies just as well to player outcomes. A player with a perfectly respectable stat line in the regular season might morph into a pumpkin as the calendar shifts to fall, or on the flip side, an unlikely hero may emerge from the ashes of a cruel summer and put the whole team on his back.

With the law of averages in mind, I’d always assumed that the more consistent hitters would be better positioned to perform well in the playoffs. My thinking went like this: The natural variation in these hitters’ performances would never wander too far from their season-long average, making them the safer, more predictable options. Whereas streaky hitters — the ones with high highs, low lows, and steep transitions between the two — would be too reliant on “getting hot at the right time” to be the type of hitter a front office should depend on in the postseason.

Reader, I was incorrect. Read the rest of this entry »

Last week, I cracked the PitchingBot black box open a tiny bit and asked it to show me the worst pitches of the year. It was for fun, mostly; I think there are some interesting data in there, but the main thing I learned was that the worst pitches are non-competitive balls. That’s always a tough concept to grasp, because the ones that stick with us are the hanging sliders and no-ride fastballs right down the pipe, the kind of pitch that we see and go, “Oh I could hit a home run on that.” Like this one:

That’s the worst pitch in baseball this year by one specific metric: the likelihood that PitchingBot assigns it of turning into a home run. I’ll show you some more of them in a moment, but first I thought I’d lay out how I did this so you can get a sense of how the model is reaching its conclusions.

Cameron Grove, the creator of PitchingBot, wrote about this idea back before he started working for the Guardians, and he was kind enough to nudge me in the right direction when it came to looking at pitches not just for the “worst,” but the ones that are the most crushable.
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This is Michael Rosen’s first piece as a FanGraphs contributor. You may have read his previous work at the site, including his article about the Kirby Index, a metric he created to measure command using release angles. He lives in Los Angeles and works as a transportation planner.

Earlier this year, I tried to solve the riddle of how Shota Imanaga threw his invisible fastball. The pitch had (and still has) a rare combination of traits: At the time of writing, only Imanaga and Cristian Javier threw fastballs from super flat vertical approach angles (VAA) with elite induced vertical break (IVB). A fastball with a flat VAA or high IVB plays a trick on the hitter’s perception; a fastball with both qualities becomes nearly unhittable, or invisible, when located at the top of the zone. I posed two questions in that piece: Why was this invisible fastball so rare? And what was Imanaga specifically doing to throw a fastball with these traits?

The first question can be answered, my research shows, by looking directly at release angles. Release angles reflect the direction that the pitcher is aiming the ball at release, which I wrote about at length in my article on the Kirby Index from May. That act of aiming — specifically, the direction the ball is oriented out of the pitcher’s hand — also affects the amount of backspin on a four-seam fastball. Read the rest of this entry »

I’m sorry, assorted old people and grumps of the world. Michael Baumann got you all riled up yesterday by looking into whether clutch exists. It does! It’s inarguably a real thing. It’s also not very predictive, and even maybe not predictive at all. I know! It’s shocking (note: it’s not shocking). After reading that, I had no choice but to look into that other baseball truism: momentum.

There have been plenty of studies about it. The findings are consistently uninteresting. It’s basically this: Momentum probably has some effect, but it’s minimal. You can slice it a ton of different ways and get some version of that conclusion, whether you’re talking about a big win helping the next day or a string of important games begetting more.

I thought I’d add to the literature with a different study. I can’t remember which game in particular, but I was watching some ball last week when a team tied the game in the bottom of the fifth or sixth. One announcer mentioned offhandedly that they were heading in the right direction and had the opposition right where they wanted them. This isn’t rare. If you watch baseball, you’ve heard some version of it for sure. I tuned out before the end of the game, so I can’t tell you whether they were right, but I made a note to look at it later.

That particular definition of momentum – rallying to tie the game in the bottom half of the inning – felt ripe for study. I grabbed game logs from every game played since 2000 to take a crack at finding this effect. I went through the score after every half inning and noted a few things. First, I noted the score differential. Next, I noted the change in differential since the last half inning. Finally, I checked who won the game in the end. That let me find whatever subset I wanted and study the difference between games that were tied when the half-inning began and the ones where the home team tied it up during that half-inning.
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The other week, I was talking to a friend as she fretted over her lack of productivity. Her struggle wasn’t with laziness or a lack of motivation, but rather a severe case of burnout, following her around the way No-Face follows Chihiro in Spirited Away. In the moment, I could see her desperately reaching for more energy to get through the day, like when the chip bag is nearly empty, so you bring it to your mouth and tilt your head back to suck down whatever salty goodness remains.

But instead of encouraging her to power through, I went a different way.

“Y’know, it’s okay to take a break.”

She laughed and said, “That’s what my therapist keeps telling me.”

If you’ve ever described yourself as a perfectionist or a people pleaser, or tied your self-worth to your measurable output, you know taking breaks can be hard.

Athletes learn from the beginning to idolize hard work. Hard work is the salve for every ailment. Wanna get stronger? Work harder. Wanna play better? Work harder. Wanna go pro? Work harder than everyone else. The hardest workers earn their own dedicated titles and recognition, separate from their actual production. Cal Ripken Jr.’s consecutive games played streak earned him the Ironman title. MLB gives out a Heart and Hustle award to whichever player’s heart tells him to hustle the hardest. And every year, we read stories about players and coaches who are the first to arrive to work each morning and the last to leave at night. Read the rest of this entry »

Last Friday, Davy Andrews put Aaron Judge into context. That’s fun for the usual reasons – “Wow, look how dominant Aaron Judge is!” never gets old. But his final conclusion – Judge hits like most batters do when opponents hang a bad slider middle-middle – got me thinking. We have a pitch-level model that estimates the worst pitches. Can we use it to get an idea of what it looks like to throw a pitch so bad it turns your opponents into Judge-esque offensive producers?

There’s an easy way to sort this out. When you look at a pitcher’s player page here at FanGraphs, you can see how we model each pitch. There are a ton of scores, but I’m going to be focusing on PitchingBot today, for reasons I’ll explain shortly. The player pages break each pitch down by command and stuff. In our internal database, it’s even better: You can look at any individual pitch and get a grade on it. I set out to find the worst pitches on the year to see whether they made hitters look invincible.

Ironically, the worst pitch thrown this year made the batter look extremely vincible. Here it is in all its glory:

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“An egregious error of Umpire Hurst in construing the rules helped Boston to two runs and added to the confusion of the Orioles. In the fourth inning Boston had three men on bases and one out. Ryan came to the bat and scratched out a short fly over third base. Jennings ran for the ball, got under it and muffed it. According to Rule 45, Section 9, a batter is out ‘if he hits a fly ball that can be handled by an infielder while first base is occupied with only one out.’ Ryan should have been declared out whether the ball was muffed or not…

When seen at the club-house after the game he started in defense of his position by attempting a distinction between the outfield and infield, claiming that the ball was not hit to the infield, but when his attention was called to the wording of the rule, which does not state that the ball must be hit to the infield, but simply that it shall be such a ball as an infielder can handle, he abandoned that position, and argued that it was not a fly ball, but a line drive. He soon saw the absurdity of that argument, as a line drive which does not touch the ground is as much a fly ball as if it were hit 100 feet up into the air.”

– “Errors Lost the Game,” The Morning Herald, April 26, 1894

The graph below has been haunting me for weeks now. I made it, but there’s nothing unique about it. You can find an identical graph in this Alex Chamberlain piece, this Tom Tango blog post, or any number of other articles. It shows the batting average and wOBA for every batted ball, based on launch angle.

I cut off 20 degrees from either side, but you get the point. Worthless groundballs and popups are on the sides, and valuable line drives and fly balls make up a narrow sliver in the middle. It occurred to me a few weeks ago that we’ve been splitting batted balls into those same four categories for a very long time now. Moreover, one of those categories is suspect. If you’ve been reading FanGraphs for a while, you know that line drive rate is considered fluky rather than sticky. Only a handful of elite players – Luis Arraez, Freddie Freeman, maybe Steven Kwan – are capable of consistently putting up top-10 line drive rates. According to Baseball Savant, batters have a .639 wOBA on line drives this year. Hitting line drives is what every single batter is trying to do, and yet somehow what Russell Carleton wrote seven years ago still holds true: “There is some skill in hitting line drives, but it is hard to repeat, and how many line drives you hit seems to be unrelated to where you fall on the ground-ball/fly-ball spectrum.” I set out to find some new way to look at this old puzzle, figuring that with all of the tools as our disposal, there had to be a better way to slice this particular pie. I failed, but I came across some interesting things along the way, and that (I have decided after the fact) is what’s really important. Read the rest of this entry »

Imagine an auction that takes place between three bidders. The item in question? An envelope filled with money. All three bidders employ teams of analysts that attempt to ascertain how much money is in the envelope, based on a variety of evidence that isn’t important for this analogy. Each bidder thus arrives at an estimate of the fair value of the envelope. Then they place a single sealed bid. The highest bidder out of the three gets the envelope.

What bidding strategy would you employ? Here’s a bad one: Just bid what your team of analysts calculates as the expected value of what’s in the envelope. The reason this is bad is known as the winner’s curse. If each bidder comes up with an estimate of fair value and bids that number, the winner will be the one with the highest estimate of fair value. In other words, you’ll only win if your estimation of the envelope’s value is higher than everyone else’s, and since you’re always paying exactly what you’re hoping to gain, you’ll tend to lose in the long run.

Allowing for a lot of approximation, this situation describes free agency in major league baseball. Every free agent has an unknowable amount of expected future production. Teams employ armies of analysts who attempt to estimate that production. Then, armed with that knowledge, they make contract offers to that free agent, in competition with other teams.

As I said, there’s a ton of approximation and simplification going on here. Players aren’t envelopes filled with money. Team context matters. Players don’t have to accept the highest bid. Tax regimes aren’t equal, and non-monetary incentives matter, too. Contracts are complex, and there’s no requirement that they be the same number of years, have the same number of options, no trade clauses, or anything of the sort. There’s no agreed-upon universal value system; different players present different value to different teams.

But that doesn’t mean the abstracted case has no use. As we approach the trade deadline, I think there’s one clear one: dispelling the myth that teams refuse to give up much to trade for a player who just signed a big free agent deal — after all, if they valued them enough for a blockbuster, they would have just offered a bigger contract, right? That’s a great soundbite, so you hear it all the time, but it doesn’t jive with established economic theory. Read the rest of this entry »

Remember back in 2021 when Gen Z tried to tell everyone to move their side parts to the middle and swap their skinny jeans for a looser variety? While most Millennials responded with outward indigence, offline they begrudgingly tried on high-waisted mom jeans and posted up in the bathroom blowing out their hair in a new direction. But before long they let their hair go back to lying in the manner to which it had become accustomed and eschewed jeans completely in favor of athleisure-wear. Even as many of us considered complying with the directive of our teenaged overlords, it felt absurd that people who haven’t even finished developing their prefrontal cortexes are left in charge of dictating what’s cool. As it turns out, though, that’s exactly why teenagers decide what’s cool. Teenagers are the only members of society with the time, energy, and lack of rationality to care so deeply about something that matters so very little.

Those who stuck to their dated stylings and weathered the petty hail storm of Zoomer mocking were vindicated a couple of months ago, when the celebrity and influencer cohort brought back the side part, declaring it on-trend once more. Around that same time another trend was taking hold among the baseball commentariat: Using strength of schedule to determine which teams had actually earned their W-L records. Mostly, this meant arguing that the Phillies weren’t a top team in the league because they’d played a soft schedule. The discourse eventually spawned multiple articles arguing that while yes, Philadelphia hadn’t exactly been slaying dragons while walking a tightrope, its act wasn’t entirely smoke (generated by the clubhouse fog machine) and mirrors either.

Strength of schedule is not typically a prominent talking point when comparing MLB teams. It might occasionally come up when comparing September schedules in a tight postseason race, but as a phrase uttered in May, it’s typically part of a college baseball discussion, or because you’ve wandered into a BCS-era college football forum. College sports need strength-of-schedule metrics because teams don’t all play one another and the variation in team quality spans the Big Ten’s new geographical footprint. But in the major professional leagues, the schedule is fairly balanced, and even though the White Sox and Rockies exist, dominating the worst teams in MLB presents a tougher task than rolling over the University of Maryland Baltimore County Golden Retrievers. Read the rest of this entry »

It’s been five weeks since Major League Baseball unveiled its first trove of bat tracking data. In that time, we’ve learned that Giancarlo Stanton swings hard, Luis Arraez swings quickly, and Juan Soto is a god who walks among us unbound by the irksome laws of physics and physiology. We’ve learned that Jose Altuve really does have the swing of a man twice his size, and that Oneil Cruz has the swing of a slightly less enormous man. Mostly, though, we’ve learned when and where batters swing their hardest. This is my fourth article about bat tracking data, and in gathering data for the previous three, I constantly found myself stuck in one particular part of the process: controlling for variables.

As baseball knowledge has advanced from the time of Henry Chadwick to the time of Tom Tango, we first found better, more descriptive ways to measure results. We went from caring about batting average to caring about OPS. We found better ways to weight the smaller results that add up to big ones, going from ERA to FIP and from OPS to wRC+. Then we got into the process behind those results. We moved to chase rates and whiff rates, and the ratio of fly balls to groundballs. With the advent of Statcast, we’ve been able to get deeper than ever into process. We can look at the physical characteristics of a pitch, just a single pitch, and model how well it will perform. Within a certain sample size, we can look at a rookie’s hardest-hit ball, just that one ball, and predict his future wRC+ more accurately than if we looked at the wRC+ from his entire rookie season.

Similarly, when I looked at average swing speed and exit velocity from the first week of bat tracking, I found that swing speed was more predictive of future exit velocity. Exit velocity is the result of several processes: You can’t hit the ball hard unless you swing hard and square the ball up, and you can’t square the ball up if you pick terrible pitches to hit. Between 2015 and 2023, our database lists 511 qualified batters. I measured the correlation between their average exit velocity and their wRC+ over that period. R = .63 and R-squared = .40. But because bat tracking takes us one more step away from results and toward process, it’s further divorced from overall success at the plate. The day after bat speed data was first released, Ben Clemens ran some correlation coefficients between some overall metrics of success. He found a correlation of .11 between average swing speed and wRC+. Now that we have more data, I re-ran the numbers and found that correlation has increased to .25. That’s a big difference, but over the same period, the correlation between wRC+ and average exit velocity is .47.

If you want to know how hard a batter is swinging, you’ll find that it’s dependent on the count, the type of pitch, the velocity of the pitch, the location of the pitch, the depth of contact, and whether contact takes place at all. As a result, if you want to measure any one factor’s effect on swing speed, you need to control for so, so many others. The more I’ve sorted through the data, the more I’ve come to appreciate the old adage that pitchers control the action. Bat tracking shows us just how right people are when they say that hitting is reactive. It shows us that different pitches essentially require different swings.

When Tess Taruskin started putting together her Visual Scouting Primer series, she asked around for scouting terms and concepts that people had a hard time picturing. Barrel variability was at the top of my list. I know that Eric Longenhagen is giving a glowing compliment when he says that a player can move his barrel all around the zone, but I’ve always had trouble picturing that. Maybe it’s because of the way I played the game when I was younger, but I’ve never really understood the concept of a grooved swing. When I was digging through the bat tracking data, seeing the effect of the pitch type, the location, and where in space the batter has to get the barrel in order to make solid contact, it finally clicked.

There’s obviously a reason that every hitter has a book, a certain way that pitchers try to get them out. I’m just not sure I ever connected it quite so clearly to the physical act of swinging, the flexibility, quickness, strength, and overall athleticism required to execute a competitive swing on different kinds of pitches in different locations. And that’s before we even get to the processing speed, judgment, and reaction time that comes with recognizing the pitch and deciding not just whether to swing, but how to attack the ball. Bat tracking highlights the how.

There are a million ways to succeed at the plate. Derek Jeter used an inside-out swing to send the ball the other way. Isaac Paredes uses an inside-even-further-inside swing, reaching out and hooking everything he can down the line. Chas McCormick and Austin Riley time their swings in order to drive a fastball to the right field gap and pull anything slower toward left. Arraez, like Tony Gwynn before him, stays back and places the ball in the exact spot that he feels like placing it. Ted Williams preached a slightly elevated swing, making him the progenitor of today’s Doug Latta disciples, who try to get on plane with the ball early and meet it out front, where their bat is on an upward trajectory. Some players talk about trying to hit the bottom of the ball in order to create backspin and carry. I could go on and on. But no matter what school of thought batters subscribe to, they’re not the ones who decide what kind of pitch is coming. Bat tracking data show us just how adaptable their swing has to be. Here’s a map of the 13 gameday zones, broken down by the average speed of competitive swings in each zone for right-handed batters.

The batter can bend at the waist and drop his bat head on a low pitch, especially inside. A high pitch requires a flatter swing, and it’s much more about pure rotational speed. An outside pitch requires hitting the ball deeper, where the bat might not have reached full speed yet, but it also allows the batter to get his arms extended. I just described three different skills, and there are plenty more that we could dive into. Because every batter is an individual, each will be better or worse at some of them than others.

At the moment when all this clicked, I thought of Shohei Ohtani. Ohtani hits plenty of balls that are very obviously gone from the second he makes contact. But he also hits some of the most awkward home runs imaginable, swings that end up with his body contorted in some weird way that makes it seem impossible that he managed to hit the ball hard. He looks like he’s stepping in the bucket and spinning off the ball, he looks like he’s simply throwing out his bat to foul off an outside pitch, or he looks like he’s just not swinging very hard, and yet the ball ends up over the fence. Somehow this ball left the bat at 106.4 mph and traveled 406 feet.

It might appear that this swing was all upper body. However, a swing is a little bit like cracking a whip, where you’re working from the bottom up to send all of the energy to the very end of the line. Some hitters are better than others at manipulating their bodies to time that energy transfer perfectly. Here’s another way of looking at this.

On the left are the 26 homers that Cody Bellinger hit in 2023. On the right are Ohtani’s 44 homers. I realize that because Ohtani hit 18 more, his chart looks more robust. But it’s not just about the number of dots. It’s about the spread. I’m not trying to pick on Bellinger. I used him in part because he had a great season. I found his pitch chart by searching for players with the highest percentage of home runs in the very middle of the strike zone. At 46%, Bellinger had the highest rate of anyone who hit 20 home runs. If you make a mistake in the middle of the zone, he’ll destroy it. On the other hand, Ohtani is capable of hitting the ball hard just about anywhere. It’s even clearer if you look at the two players’ heat maps on hard-hit balls from last season.

Bellinger has never been the same player since his 2019 MVP campaign, and it’s generally assumed that the significant injuries that followed affected his swing. He can still do major damage, but on a smaller subset of pitches. This is one of the reasons that scouts focus so much on flexibility and athleticism and take the time to describe the swings of prospects as grooved or adaptable, long or short, rotational or not, top-hand or bottom-hand dominant. These things may not matter much in batting practice, but if there’s any kind of pitch you can’t handle, the game will find it. The best hitters find a way to get off not just their A-swing, but a swing that can succeed against whatever pitch is heading toward them.