David Hamilton doesn’t wait casually at first base. He lurks, waiting for the slightest opening to take off. Watch an at-bat where Hamilton is on the bases, and he’s often as much a point of discussion as the man at the plate. Take the game between the Red Sox and Guardians on September 1, for example. Hamilton pinch-ran for Carlos Narváez with Connor Wong at the plate. Wong fouled off a bunt for strike one with the entire defense focused on Hamilton at first base. Then Hamilton stole second on the next pitch even with the catcher, pitcher, and infielders all fixed on his every move.

Hamilton isn’t the most prolific basestealer in the majors. He isn’t the most successful. But he is the baserunner who tries to steal most frequently, after adjusting for opportunities, and so he’s a great poster boy for what I’d like to talk about today: stolen base opportunities and takeoff rate.

It doesn’t take much to make a stolen base possible, just a runner and an open base. You do need both of those, though. Draw a walk to load the bases, and you’re not attempting a steal without something very strange going on. Stolen base opportunities aren’t easy to find in a box score or a game recap. They’re the negative space of baseball – no one’s counting them, and it’s easier to see where they aren’t than where they are. So, uh, I counted them. Read the rest of this entry »

Watch this play. What do you notice?

Here’s what I see: Brooks Lee lofts a soft fly ball 248 feet from home plate. Chandler Simpson circles it but loses a bit of momentum by the time it lands in his glove. Twins third base coach Tommy Watkins sends the not-particularly-fast Trevor Larnach (18th-percentile sprint speed). Shallow fly ball, slow runner, close play at the plate — Larnach slides in just ahead of the throw. It’s an exciting sequence, and I’ve missed an important part of it. Read the rest of this entry »

I’d like to present the meatball-iest pitch thrown so far in 2025:

I know, I know! I said that, but it’s just a foul ball. Hear me out, though, because I can put some data behind my claim. Here at FanGraphs, PitchingBot, our in-house pitch modeling system, looks at every single pitch thrown, regresses it against a huge database of past pitches, and uses some mathematical ingenuity to turn that into the expected outcomes of the pitch. That’s not the same as knowing which pitch is most likely to turn into a home run, but luckily, a good bit of mathematical wrangling can turn pitch grades into home run percentages.

Last year, I worked out the rough contours of converting PitchingBot grades into home run likelihood. This year, I’ve expanded that methodology to try to learn a little bit more about the pitchers doing the meatballing. If you’d like to skip through the how, you can head right down to the table labeled “Meatball Mongers.” If you’re here for the nitty gritty of turning pitch metrics into home run likelihood, though, here’s how I did it.

That Trent Thornton fastball had a lot of things working against it, and those things help explain how PitchingBot estimates the chances that a pitch will be hit for a home run. PitchingBot has a flowchart that explains how the model works. Here’s how the system assesses every pitch it grades:

Hey, a convenient “start here” label! How great! The “swing model” takes location, count, pitch type, movement, platoon matchups, and pretty much everything else you can imagine into account and guesses at the likelihood of a batter swinging at each pitch. That Thornton fastball was down the middle in an 0-1 count, and it’s not a particularly deceptive offering. In other words, hitters often swing at fastballs like that – 92.7% of the time, per PitchingBot’s model. Read the rest of this entry »

A warning up top: This article is about seeking and not finding, about the unique ways that data can mislead you. The hero doesn’t win in the end – unless the hero is stochastic randomness and I’m the villain, but I don’t like that telling of the tale. It all started with an innocuous question: Can we tell which types of hitters are streaky?

I approached this question in an article about Michael Harris II’s rampage through July and August. I took a cursory look at it and set it aside for future investigation after not finding any obvious effects right away. To delve more deeply, I had to come up with a definition of streakiness to test, and so I set about doing so.

My chosen method was to look at 20-game stretches to determine hot and cold streaks, then look at performance in the following 20 games to see which types of players were more prone to “stay hot” or “stay cold.” I started throwing out definitions and samples: 2021-2024, minimum 400 plate appearances on the season as a whole, overlapping sampling (so check games 1-20 vs. 21-40, 2-21 vs. 22-41, and so on), wOBA as my relevant offensive statistic, 50 points of wOBA deviation against seasonal average to convey hot or cold, 40-PA minimum per 20-game set to avoid weird pinch-hitting anomalies, throw out games with no plate appearances to skip defensive replacements — the list goes on and on. Read the rest of this entry »

What is the sound of a batter not getting hit by a pitch? I ask because as hit-by-pitch rates climbed over the years (and kept climbing), we writers have made lots of noise about them. In 2007, Steve Treder published an article called “The HBP Explosion (That Almost Nobody Seems to Have Noticed)” in The Hardball Times. After that, everybody noticed. We’ve seen articles about rising hit-by-pitch rates here at FanGraphs, Baseball Prospectus, the Baseball Research Journal, MLB.com, The Athletic, SportsNet, FiveThirtyEight, the Wall Street Journal — even the Clinical Journal of Sports Medicine. The venerable Rob Mains of Baseball Prospectus has been writing about it (and writing about it and writing about) ever since he was the promising Rob Mains of the FanGraphs Community Blog. Tom Verducci wrote about the “hit-by-pitch epidemic” for Sports Illustrated in 2021, then wrote a different article with a nearly identical title just two months ago. There’s good reason for all this noise, and in order to show it to you, I’ll reproduce the graph Devan Fink made when he wrote about this topic in 2018:

Hit-by-pitches have been rising since the early 1980s, and despite a decline in the 1970s, you could argue that they’ve been rising ever since World War II. Devan’s graph ends in 2018, but the numbers kept on going up — for a while, anyway. Here’s a graph that shows the HBP rate in recent years. After a couple decades of sounding the HBP alarm, it’s time for us to unring that bell (which I assume, without having looked it up, is an easy thing to do):

Congratulations everybody, we’ve done it! We’ve ended the epidemic. The HBP rate has fallen in four of the last five seasons. It’s safe to leave your home again. You can enter a public space without fear that you’ll be bombarded with stray baseballs. Rob Mains can finally take a vacation. Tom Verducci can finally take a deep breath. Read the rest of this entry »

“Who is the most feared hitter in baseball?” is not a question I set out to answer. That would be too easy! Step one: Write “Aaron Judge.” Step two: Let out a bemused chuckle. Obviously it’s Aaron Judge. Who would have commissioned such a silly article? Step three: Get lunch. That does sound pretty tempting, I must admit, but that’s not this article. This one is a little bit weirder.

I started by asking the opposite question: “Who is the least feared hitter in baseball?” I had a simple idea for how to test it. Take a look at the rate of pitches over the heart of the plate that each batter sees when behind in the count – more strikes than balls. A hitter who sees tons of pitches down the middle in a bad hitting situation isn’t a guy who scare opponents. Pitchers are so not afraid that they’re chucking pitches down Broadway even in the situations where that’s least necessary and least advantageous. Read the rest of this entry »

Last week, I took a peek at which offenses have exceeded (or missed) expectations this year. I did that by taking every player’s preseason projection and actual playing time to create a projected wOBA for the entire offense. I compared that to what has actually happened. The difference? That’s what we’re looking for, how much a team has surprised to the good or bad in 2025.

I couldn’t leave it at just one phase of the game, though. Pitching can be measured the same way (ish, see methodological notes below if you’re interested in the nitty gritty). I didn’t want to compare ERA (too noisy) or FIP (too regressed, aka not noisy enough). I settled on wOBA as a good representation of how well a pitching staff is doing overall. It’s a middle point between the two other options, so we are neither ignoring what happens on balls in play, nor caring too much about sequencing. Here, for example, are the Texas Rangers, the biggest overachievers of the season:

Right away, you can see why they’ve beaten expectations by so much. Four-fifths of their starting rotation, four of the five pitchers who have faced the most batters, have performed meaningfully better than their preseason projections. The fifth is Jacob deGrom, who had one of the best projections in baseball coming into the season and has hit it on the nose. Even their most-used bullpen arms have been pleasant surprises. That’s how you allow the fewest runs in baseball by a mile, apparently. Read the rest of this entry »

We’re 10 years or so into the launch angle revolution, and the reasoning behind it hasn’t changed much. Groundballs have a .228 wOBA this season, while all other balls in play are at .462. Hit the ball on the ground, and you’re Christian Vázquez. Hit it in the air, and you’re Aaron Judge. Players are gearing their swings for damage in the air. They’re optimizing their bat path for an upward trajectory. They’re meeting the ball farther out in front. They’re looking to hit the bottom third of the ball. Knowing all this, I doubt you’d be surprised to learn that 2025 is shaping up to set the record for lowest groundball rate since 2002, when Sports Info Solutions first started tracking such things. But you might be surprised to learn just how extreme the shift has been.

So far, I’ve talked about all the reasons that batters have tried to put the ball in the air more, but that’s only half the story. Five years ago, Ben Clemens wrote a great article in which he tried to determine whether batters or pitchers exert more control over groundball rates. After separating the batters from the pitchers, he split each group into quartiles based on their 2018 groundball rates and then looked at the results when each group faced off in 2019. He found that the effect was nearly identical. When you moved the batter up one quadrant, the groundball rate of the new pairing went up by an average of 5.2 percentage points. When the pairing moved up a quadrant in the pitcher pool, the groundball rate went up by 4.8 percentage points. Knowing that, let’s not blame this all on the batters. Are pitchers as responsible as batters for the shrinking groundball rate across the majors? Let’s start by updating my 2023 league-wide update on pitch mix.

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If you’re a fan of a large-market team that has recently been struggling to score runs, you may be eligible for compensation. Wait, no – that’s not right. You may be eligible to complain about your team in my weekly chat? Not quite it, either. Let’s try it one more time… If you’re a fan of a large-market team that has recently been struggling to score runs, you are eligible to read this article and see to what extent your team has let you down and to what extent it’s just a narrative.

The Yankees and Mets have been having a tough time of late, which always brings out doubters, both fans and rivals. I don’t quite know what to tell those grumpy souls. You’re upset with the Yankees offense? Well yes, sure, absolutely, carry on, but they do have the highest team wRC+ in baseball. The Mets let you down? Without a doubt, they’re the Mets, so on and so forth – but they’re top 10 in baseball in wRC+, too. Orioles offense bumming you out? Yeah, I mean, they’ve been a bummer, but they’ve also been impacted by injuries, which seems hard to blame them for.

I came up with a quantitative test for how much teams have disappointed relative to preseason expectations. I took the actual playing time that each team has allocated so far. Then, I used preseason projections to come up with the offensive numbers we’d expect from each team given who has played and how good we projected them to be. I compared that to how good the team has actually been. The difference between those two numbers is the aggregate overachievement or underperformance that can’t be attributed to injury.
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Last week, I began a series of pieces about team win-loss totals as estimated by BaseRuns, first by taking a broad look at the methodology and its limitations, then by zooming in on the offenses that deviate most notably from their BaseRuns assessment in the run scoring department. Let’s wrap up with a look at the defenses that sit furthest from their runs allowed approximation.

In the offense edition, I used a game show format to evaluate whether the perspective offered by BaseRuns has a point, or if there’s something its methodology is overlooking. We’ll keep that framework going for the defenses as well. Here’s a reminder of how it works:

To determine whether or not BaseRuns knows what it’s talking about with respect to each team, imagine yourself sitting in the audience on a game show set. The person on your left is dressed as Little Bo Peep, while the person on your right has gone to great lengths to look like Beetlejuice. That or Michael Keaton is really hard up for money. On stage there are a series of doors, each labeled with a team name. Behind each door is a flashing neon sign that reads either “Skill Issue!” or “Built Different!” Both can be either complimentary or derogatory depending on whether BaseRuns is more or less optimistic about a team relative to its actual record. For teams that BaseRuns suggests are better than the numbers indicate, the skill issue identified is a good thing — a latent ability not yet apparent in the on-field results. But if BaseRuns thinks a team is worse than the numbers currently imply, then skill issue is used more colloquially to suggest a lack thereof. The teams that are built different buck the norms laid out by BaseRuns and find a way that BaseRuns doesn’t consider to either excel or struggle.

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