I know this isn’t really a blind item, what with the name of the article and the picture at the top and all, but bear with me for a moment. I’m going to give you some details about a mystery player. Here’s a list of all the transactions they’ve been involved in since their initial signing in 2016:

This feels like a pretty boring player, right? Some kind of replacement level journeyman, probably a reliever given how teams shuffle them on and off the end of the roster. It’s true: He’s a reliever, and a replacement level one at that, just like you’d expect. This particular player pitched to a 5.80 ERA and 4.44 FIP (4.94 xFIP, 4.35 SIERA, etc.) in 40 1/3 innings of work. His WAR was exactly zero across parts of three major league seasons.

Oh, here’s another data point. Our mystery man started the 2024 season in Triple-A, and things didn’t go so well. He struck out 29.7% of the opposing batters he faced, but walked 10.1% of them and gave up a ghastly eight homers in 34 innings of work. That’s, uh, not great. That’s how you end up with a 5.77 ERA. It’s also apparently how you end up as the reliever with the second-best ERA and best FIP in all of baseball, and earn a job as the closer for the Tampa Bay Rays. Read the rest of this entry »

On August 7, Randy Arozarena slashed a double to right. He came into second base at a trot, so evidently safe that he didn’t need to sweat it. As the camera focused on him, he turned and hyped up the dugout. There was nowhere else to look; there had been no runners on base and thus no other action to follow.

Things weren’t so sunny 10 days later. Arozarena batted with two on and two out, and a double would have been absolutely glorious. The runners would be off on contact, which meant the difference between a double and an out was two-plus runs — the two that would actually score, plus some chance of Arozarena himself scoring. But Arozarena struck out on a 1-2 slider from Bailey Falter, and the inning ended.

Advanced statistics don’t assess the value of a play in just one way. You can think about these two moments extremely differently depending on which metric you’d prefer to use. Our main offensive statistic, wRC+, ignores context on purpose. It works out the average value of a home run across all home runs hit in the majors in a given year, and uses that as the value for every home run. It does the same for every offensive outcome, in fact. Read the rest of this entry »

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Programming note: I’m taking a break from my Five Things column this week, as I’m traveling to Chicago for Saberseminar. Five Things will return next week with events from the last two weeks. In the meantime, please enjoy a ridiculous hypothetical.

This week, someone in my chat asked me an interesting hypothetical: How different would baseball be if the fences were the same distance from home plate all the way around? It would obviously be wildly different from how the sport currently works. Center field is the deepest part of the park by far, of course, and it’s hard to picture exactly what an equidistant fence would look like. You might think it’s a triangle, but that’s not right – it looks more or less like an arc, which is what an actual stadium looks like, only with a much sharper curvature.

That sounds so darn weird that I wanted to see what it would mean for offense. I don’t have any strong analytical reason for doing so. We aren’t plumbing the depths of smart baseball analysis here; we’re making up a dumb world and wondering what kinds of dumb things would happen in it.

First things first: There would be more home runs. I picked 370 feet as the distance because it feels reasonably close to the real world average of fence depths. I picked a 10-foot tall wall for similar reasons; if we’re getting weird in some ways, I’d prefer to standardize the others. There’s an easy math trick you can use here; baseballs tend to fall at roughly a 45 degree angle by the time they’re descending, their forward momentum getting slowly blunted by air resistance. That means that a ball that clears the wall by a millimeter would travel 10 more feet before hitting ground that was at field level – in an outfield bullpen, say. In other words, every ball that travels 380 or more feet in the air is going to be a home run now. Read the rest of this entry »

Remember the halcyon days of April? The season had just kicked off. Aaron Judge was bad. Alec Bohm was one of the hottest hitters in baseball; Colt Keith was the worst. Blake Snell couldn’t buy an out. The Cubs led the NL Central. The White Sox… okay, the White Sox have been bad all year, but my point is that we ascribe outsize importance to the first month of the season as it’s happening.

Bohm was hitting so well that it felt like he was a completely different hitter. Since May 1, he’s been almost exactly the same as his prior career self. Snell figured things out. Judge obviously did too. But there was also signal in that first month. Bobby Witt Jr.’s breakout was center stage. Juan Soto and Gunnar Henderson set the tone for their impressive campaigns. The key to interpreting early-season results is to let a bunch of ideas in, ideas suggested by that first month, but to be willing to discard them quickly if they turn out to be flashes in the pan.

In that spirit, I’m about to get breathlessly excited about some post-All-Star break statistics. Some of what’s gone on in the last month won’t surprise you – Witt, Soto, and Judge are absolutely incandescent. Chris Sale is on his way to a Cy Young. The Brewers are cruising to an NL Central title. All of those things have mostly been true all year, so seeing them in the first month of the second half doesn’t feel strange. But there’s other stuff happening too, and the bits that feel shocking now but would have seemed normal if they’d taken place in April are what I’m focusing on today. Read the rest of this entry »

Hey there, and welcome to a segment that I’m hoping to turn into a recurring feature. Last week, I started delving into the individual event-level predictions built into our pitch grading model, PitchingBot. I made some broad generalizations about the kinds of pitches most likely to be hit for home runs and then looked at which pitchers threw them most often. I gathered some information about those pitches (fastballs, poorly located, in hitter-friendly counts but not 3-0), and tried to figure out what that meant for home run rate.

More specifically, it’s fun to look at these bad pitches, and it’s fun for me to see how few of them actually result in homers. The 50 pitches most likely to be hit for a home run surrendered one homer combined. The top 100 resulted in only three homers, while the next 100 resulted in six homers. There’s a ton of variability, but at its core, baseball is still a game of failure – even when a pitcher does the worst thing they possibly can, hitters mostly don’t punish them.

To that end, I’m going to try a new weekly roundup: various meatball-related items that show who’s been exceptional in one direction or another over the past week. Given that it’s mostly a list of things without a ton of analysis necessary, I’m going to start out by trying to add it on top of my normal schedule, and I’ll also use this to update some of my favorite junk stats (whomps per whiff, Kimbrels, etc.). If it’s popular, great! If not, hey, it’s a long baseball season and you have to keep trying things. Anyway, let’s get going.
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Welcome to another edition of Five Things I Liked (Or Didn’t Like) This Week. It’s an incredible time to be a baseball fan, particularly one who isn’t tied to a single team or division. There are three tight division races, and both wild cards hold some intrigue. Some of the brightest stars of the game are playing incredibly well right now. The A’s are on a kelly-green-clad respectability streak that is both improbable and delightful. The White Sox are fun to watch for their ever-evolving pursuit of futility (more on that below). There’s no time for August doldrums when the games are this exciting. So no more talking vaguely about what a great week this was; let’s get right to it. And thanks, as always, to ESPN’s Zach Lowe for the format I’m borrowing here.

1. Barry Would Never

Perhaps the least likely story of the season, on an individual level, is Tyler Fitzgerald, who has turned into one of the great offensive forces in the game overnight. After putting up average offensive numbers as he climbed through the minors across four seasons plus the lost COVID year, he has established an everyday role on the Giants and unexpectedly caught fire this season. He’s now hitting home runs faster than I can count and getting his name in Giants history next to Barry Bonds for his power feats. (As an aside, this clip of Bonds and Greg Maddux discussing an old at-bat is amazing, and I highly recommend it.)
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On Sunday evening, Camilo Doval stepped to the mound without his usual light show. He showed the range of his game right away – after striking out the first two batters he faced, he walked Greg Jones, who promptly stole both second and third. Then Nolan Jones ripped a scorching line drive to center for a triple, Elehuris Montero stroked a line drive single, and two runs had scored just like that. To make matters worse, this wasn’t even against the Rockies – it was against the Triple-A Albuquerque Isotopes.

It’s hard to wrap your head around Doval’s sudden fall from ace closer to minor leaguer. He debuted in the magical 2021 season, picked up closing duties at the tail end of that year, and looked like one of the best relievers in the sport almost immediately. In 2022 and ’23, he pitched to a 2.73 ERA and 2.87 FIP. He was wild at times, overpowering at others, and impossible to square up in every iteration. When you have a 100 mph cutter and a tight 90 mph slider to throw off of it, you don’t need much else. He was fifth in baseball in saves, ninth in reliever WAR, 10th in innings pitched among relievers; in other words, he was a one-man back-of-the-bullpen for the Giants.

One thing always bothered me about Doval, even when he was dominating the opposition for the last two years: His cutter doesn’t cut correctly. That sounds nonsensical, or at best like a weak nitpick. But here, take a look at his cutter as compared to Emmanuel Clase’s best-in-class offering, using our pitch-type splits:

The “cut” in a cutter refers to glove-side movement. A good cutter looks like a fastball out of the hand before the spin takes over; then it veers sharply away from the four-seam path that hitters have spent their whole lives tracking. It’s not just a matter of how differently shaped the cutter is from the rest of a given pitcher’s arsenal – just ask Mariano Rivera. Instead, it’s more about defying the brain patterns batters have built up over decades of playing baseball. That’s just not where a fastball should go, and so hitters either swing fruitlessly over it or, in the case of opposite-handed batters, end up breaking their bats when the pitch bores in on their hands.
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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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