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 plentyofstudiesabout 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. Read the rest of this entry »
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 Judgeinto 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:
“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.
Athletes like Elly De La Cruz can skew our perception of reality. His powerful arm makes most shortstops look like they throw with a wet noodle. His 99th-percentile sprint speed makes most other baserunners look like they’re running on sand. His tall frame, which our website somehow lists at 6-foot-2, makes that guy on Hinge who claims he’s 6-foot-2 look like he’s actually 5-foot-8. Oh, and his 13 steals of third base this year might make you think steals of third are at an all-time high, which couldn’t be further from the truth.
As a fan of highly specific baseball stats – a bold statement to make on this website, I know – I like to check in on the stolen base rates at each bag. Practically speaking, that means I pay particularly close attention to steals of third, the oft-forgotten middle child of stolen bases. Steals of third are too common to receive the same amount of attention as steals of home; at the same time, they’re infrequent enough that they’ll always be overshadowed by the sheer number of second-base steals. Steals of home are almost guaranteed to make tomorrow morning’s highlight reel. Steals of second outnumber all others and thus dictate league-wide stolen base trends every year. Steals of third are stuck in the middle, and that’s especially true this season as their siblings are taking even more of the glory than usual.
The stolen base success rate at home (16-for-29, 55.2%) is the highest it’s been since at least 1969. Indeed, it’s above 50% for only the second time in that span. In addition, runners are on pace to steal home 36 times this year, which would rank second in the divisional era and well within shouting distance of first (38 SBH in 1998). Meanwhile, the overall stolen base rate (i.e. steals per game) is also on the rise, primarily driven by an increase in steals of second. The league is on pace to steal second base 166 more times in 2024 than it did last year, a 5.6% increase, as runners continue to test the limits of the New Rules™. Read the rest of this entry »
George Kirby had Javier Báez right where he wanted him. It was October 3, 2022, the last start of Kirby’s excellent rookie year, and Kirby had Báez, the king of chasing sliders off the plate, in an 0-2 count. His catcher, Cal Raleigh, set up off the plate, suggesting that Kirby would be targeting the outer edge.
Kirby hit his target with a well-executed slider. And Báez, instead of whiffing, hit it out of the park.
Báez wasn’t fooled; at seemingly no point did he think that pitch was a fastball. And Kirby’s lack of deception — defined here as a lack of overlap between the horizontal release angle (HRA) of his fastball and slider — may have played a part. Read the rest of this entry »
Over the past month, smart people have been deciphering the relationship between swing length and pitch location in MLB’s new bat tracking data. If you’re looking at raw data, it’s hard to know whether someone has a long swing because they like inside pitches (Isaac Paredes) or because their swing is actually long and loopy (Javier Báez). In order to make solid contact with an inside pitch, the barrel needs to meet the ball out in front of the plate, which means that it will take a longer journey to the point of contact than it would to meet a pitch over the middle of the plate. Below is a breakdown of Luis Arraez’s swing length against fastballs. As you can see, even the king of the short swing gets long when he has to reach pitches up-and-in or down-and-away.
Some of this is as old as the game itself. It’s the reason pitchers throw fastballs up and in, where a necessarily longer, slower swing makes them harder to catch up with. Bat tracking has given us numbers to back up another intuitive part of the game: Swing length is positively correlated with bat speed, confirming that players who are short to the ball sacrifice bat speed for bat control and contact ability. Those two correlations, pitch location to swing length and swing length to bat speed, got me thinking about the launch angle revolution.
The launch angle revolution really got its hooks into Major League Baseball in 2015. That’s the year Joey Gallo and Kris Bryant debuted, and the year Justin Turner and Daniel Murphy fully turned themselves from contact hitters into power threats. In The MVP Machine, Ben Lindbergh and Travis Sawchick documented what Turner was thinking in 2013, the very first time he tried out the new approach for which teammate Marlon Byrd had been proselytizing. “I was thinking, I’m just going to try and catch the ball as far out as I can in batting practice,” Turner said.
Catching the ball out front often means pulling it, especially in the air. The league’s overall pull rate is roughly the same as it was in 2011, but as you can see from the chart above, its pull rate on air balls — the line drives and fly balls where hitters do damage — hit an all-time high in 2017 and then again in four of the next five years. The exact approaches can differ. “I’m going to be on the fastball and drive it to right center, and if I’m a little early on the slider I’ll catch it out in front,” Austin Riley told Eno Sarris last year. And as Ben Clemens has noted, hitters have increased their pulled balls in the air simply by choosing to attack pitches that lend themselves to being launched in that direction. But strictly speaking, there isn’t a huge inherent advantage to pulling the baseball. If you’re going to hit a long fly ball, it’s better not to hit it to straightaway center, where the fence is deeper and the fielders are better, but that’s equally true for both pulling the ball and going the opposite way. In a sense, pulling the baseball is just a side effect of catching the ball out in front. Read the rest of this entry »
