I never pitched in Little League, but I remember many of the lessons our coach imparted to this day. Most specifically, I remember him harping on “hard in and soft away.” This was silly. Nobody on my team could throw a curveball, and even from my youthful perspective, no one could throw anything hard either. We all mostly struck out or walked; pitchers with command were pretty much untouchable in my small-town East Tennessee league. But we’re losing the plot here – as it turns out, that advice is omnipresent in baseball, from little leagues to the majors.

I’ve always been enamored with this simple and yet fascinating rule of thumb. Why does it work? Does it work, even? What’s so special about “in” and “away” relative to pitch speed? I’ve never quite found a satisfactory way to classify it. But while I was taking a look at contact point data last week, I came up with an idea for how to measure this. When you look at the data, the evidence has been there all along.

I focused on the “hard in” aspect of the adage, because major leaguers throw so many different secondaries that honing in on what “soft” meant seemed impossible. To that end, I devised a quick test to see how conventional wisdom behaves in practice. I defined “inside” and “outside” pitches by removing the middle third of the plate, then extending out nine inches past the edge of the strike zone in both directions. I looked at sinkers and four-seamers thrown in these areas to define “hard in” and “hard away.” Read the rest of this entry »

I’ve long been interested in measuring the value of making an out. Different outs count differently, and yet pretty much every baseball statistic you can imagine ignores that fact. I’m not just talking about advanced ones like wRC+ or wOBA, though those do indeed treat all outs as equal. I’m talking about basic things like batting average, on-base percentage, and slugging percentage. No one says, “Well, he batted .320, but some of those outs were in bad situations, so it was more like batting .313.” That’s not how we think about offensive statistics.

But just because we don’t count outs differently doesn’t mean that they all have the same value. This is obviously true. Striking out with a runner on third and fewer than two outs is a tragedy. Hitting a run-scoring groundout in the same situation gets the batter a long series of fist bumps back in the dugout. But when it comes to wRC+ or batting average, that distinction doesn’t show up.

There are good reasons for existing statistics to work the way that they do. Batters don’t control who’s on base and how many outs there are when they come to the plate. They don’t control whether there are fast runners on base, or whether the outfield has arms so weak that anyone could score from third base on a fly ball. In the same way that a home run is a home run is a home run, statistics that try to measure batter skill treat all outs the same. But still… I wanted to know more. Read the rest of this entry »

How much will the ABS challenge system hurt the ability of catchers to frame pitches? That question has been bouncing around my brain for quite a while now. I’d been waiting for the offseason to really dive into the numbers, and, well, we’re here. It’s the offseason. But now that I’ve dug into all the data I could find, I think the entire premise of that question might be flawed. I thought that correcting a couple of ball-strike calls a game would erase a couple of well-framed pitches. This would no doubt hurt the better framers more than it hurt the worse ones, simply because they earn more strikes and would have more to lose. At the same time, the lesser framers would have juicier pitches to challenge, boosting their numbers a bit. As a result, the gap between good and bad framers would shrink, furthering a trend that’s been going on since we first gained the ability to quantify the value of pitch framing. It would still be valuable, just not quite as valuable as it used to be. But I’m not so sure anymore. Let’s start with the data.

I pulled all the major league framing data I could. I pulled league-wide and individual catcher called strike rates both inside and outside the strike zone for the majors and for Triple-A, which in 2025 used the same challenge system we’ll see in 2026. I can tell you that 26 catchers got a significant amount of playing time in both Triple-A and the majors last season, and their called strike rate on pitches in the shadow zone in the majors fell by an average of 1.4 percentage points within the zone and 1.7 percentage points outside it relative to what it was in the minors. So while the Triple-A strike zone may be tighter, pitch framing is still harder in the majors. But the only data about how the challenge system has actually worked in the minors and in spring training of 2025 comes from MLB press releases, and it’s extremely sparse.

Of course, that data definitely exists. Baseball Savant guru Tom Tango wrote up a bunch of interesting takeaways from it on his blog a month ago. As you’d expect, players are more likely to challenge calls in higher-leverage moments, in the later innings, and on pitches that decide the outcome of an at-bat. For that reason, they tend to be less successful in those situations; they’re not challenging because they’re sure they’re right, but because they really want the call to go the other way. Tango also broke down some catchers and batters who were particularly good or bad at challenging. Not only did he provide their stats – poor Zac Veen challenged 24 pitches and got just three overturned – but Tango showed that Savant will be rolling out challenge probability numbers next year, using the distance from the edge of the strike zone to calculate the likelihood that any particular pitch will get challenged, and that any particular challenge will be successful. From there, it’s easy to calculate how much challenge value each batter or catcher creates above the average player. Read the rest of this entry »

For four years now, I’ve been updating you on the changing contours of the strike zone. By my count, this is the 10th installment in that series and the sixth specifically about the accuracy of ball-strike calls on the edges of the zone. With the implementation of the ABS challenge system in 2026, these updates will no doubt start to look a bit different. This is our last umpire accuracy update of the pre-ABS era, so let’s take stock of where we are at the end.

After a tiny dip in 2024, umpires were back on track in 2025, posting a record-high accuracy rate of 92.83% overall. In fact, 2024 was the only year in the pitch-tracking era in which umpires didn’t set a record for accuracy. However, this latest record came with a bit of controversy. Early in the season, pitchers and catchers picked up on the fact that the strike zone seemed to have shrunk. The league tightened up the standards that it used to grade umpires, reducing the size of the buffer zone around the edges of the zone. As a result, accuracy shot up specifically on pitches outside the zone, even more specifically, on pitches just above the top of the zone, causing pitchers and catchers to complain that they were losing the high strike.

This graph reminds us of a couple facts that might just be so obvious that we rarely think about them. First, the vast majority of takes come on pitches outside the strike zone. Of course they do; those are the pitches you’re not supposed to swing at. This year, for example, 68% of the calls umpires had to make came on pitches outside the strike zone. Second, it’s easier to identify balls than it is to identify strikes. Of course it is; the area outside the zone is a lot bigger than the area inside the zone. Read the rest of this entry »

I was in Hawaii this past weekend, taking a nice vacation to wind down from the end of the baseball season, when I found myself thinking about intercept points. Weird? Overly baseball obsessed? Maybe. But in my defense, a kid at the pool kept swinging at a Wiffle ball almost hilariously late, spraying it “foul” every time. “Oh look, the next Luis Arraez,” I thought, before going back to my umbrella-adorned drink. But that stuck with me, and when I got home, a database query leapt out of my head fully formed, like Athena after Zeus’ headache.

Where is the optimal place to make contact with the ball? It depends on who’s swinging. Statcast measures every single swing’s contact point relative to a hitter’s center of mass, and that data clearly shows that there are many ways to succeed. That’s always stymied me as I’ve looked into swing path data. But that small child gave me an idea when he got off the best swing I’d seen all day, a Wiffle ball line drive that would have been a screamer down the left field foul line (he was batting lefty). Because his normal swing was so late, his best contact was ever so slightly less late. What if I bucketed hitters based on their own swings to look for swing timing clues?

I took every batter who produced 300 or more batted balls (foul balls or balls in play) in 2025. For each of those hitters, I took aggregate statistics for all of their results, then also split their batted balls into three groups: deepest contact point, middle contact point, and farthest forward contact point. You can think of it as late, on time, and early, adjusted for that player’s swing. The later you start your swing, the more you “let it travel,” the deeper your contact point relative to your center of mass. The earlier you start, the more you “get out in front,” the farther forward you make contact. Read the rest of this entry »

In just about any sport you can name, offense is king. If you’re the one who scores the goals, the points, the runs, the whatever they call it in polo – the biscuits, maybe? – you’re going to get the plaudits. Who’s the greatest defenseman in the history of hockey? It’s Bobby Orr, of course, because he was the first great offensive defenseman. This pattern very much holds when it comes to baseball.

Among other things, the sabermetric revolution helped us codify the value of hitting relative to the other facets of the game. To wit, according to weighted runs above average – and we’re using that particular stat because, like standard baserunning and defensive metrics, it’s a counting stat that compares a player to the performance of an average player – the most valuable hitter during the 2025 season was one Aaron Judge. Judge created 82.5 more runs than the average hitter. That’s 21 runs more than any other player, and an astonishing 36 more than any other player not named Shohei Ohtani. Judge was the best offensive performer in the game by a mile, which makes him the frontrunner for the American League MVP award, even though he put up negative value as a baserunner and, depending on which metric you trust, his defense graded out somewhere between pretty good (DRS, FRV) and really bad (DRP). The best defender was Patrick Bailey, who put up 30 fielding runs, and the best baserunner was Corbin Carroll, who finished with a measly 10.3 baserunning runs. Offense is just more valuable than defense and baserunning. Here’s the distribution of values for the three portions of the game:

Read the rest of this entry »

This June 25, the Dodgers and Tigers both played their 81st game of the season. Both teams finished the day 50-31, sharing the best winning percentage in baseball at .617. The Tigers got there with a slightly better run differential, though; their Pythagorean winning percentage was a cool .608, while the Dodgers checked in at .595. Pythagorean record is implied by runs scored and allowed, and broadly regarded as a more stable measure of talent than simple wins and losses. Since that day, though, the Tigers have gone 35-40 (.467 with a .483 Pythag), while the Dodgers have gone 38-37 (.507 with a .556 Pythag).

I’m bringing this up – last data project for a while, incidentally, I just had a bunch of things in my queue and couldn’t resist tackling them all – because “how good is that team, anyway?” has been a hot topic this year given the various surprising teams who have, at times, taken up the mantel of “hottest in baseball.” Versions of this question – “This team is doing well/poorly now, what does that mean for next month?” – have been both interesting and top of mind in 2025. The Tigers and Brewers played so well for so long that they each crashed the best-team-in-baseball debate. The Mets did their hot-and-cold thing. The Dodgers have endured multiple fallow stretches. Sometimes, teams felt like they were getting very lucky or unlucky relative to their run differential. But what does any of that even mean? Read the rest of this entry »

Author’s note: “Five Things I Liked (Or Didn’t Like) This Week” is taking a short break, but will return next Friday for the end of the regular season.

Earlier this week, I did the sabermetric equivalent of eating my vegetables by testing the accuracy of our playoff odds projections. I found that our odds do a pretty good job of beating season-to-date odds (particularly late) and pure randomness (particularly early, everything does pretty well late). It’s good to intermittently check in on the accuracy of our predictions. It’s also helpful to build a baseline as a benchmark to measure future changes or updates against.

Those are a bunch of solid, workmanlike reasons to write a measured, lengthy article. But boring! Who likes veggies? I want to beat the odds, and I want to flex a little mathematical muscle while doing it. So I goofed around with a computer program and tried to find ways to recombine our existing numbers to come up with improved odds built by slicing up existing ones. It didn’t break the game wide open or anything, but I’m going to talk about my attempts anyway, because it’s September 19, there aren’t many playoff races going on, and you can only write so many articles about whether the Mets will collapse or if Cal Raleigh will hit 60 dingers.

What if you just penalized extreme values?
I first tried to correct for the fact that early-season projection-based odds (which I’m calling FanGraphs mode for the rest of the article) seem to be too confident and thus prone to large misses. I did so by applying a mean reversion factor that pulled every team’s values toward the league-wide average playoff chances (i.e. how many teams made the playoffs that year). This method varies based on the current playoff format; we have 16-team, 12-team, and 10-team samples in the data, and I adjusted each appropriately. I set the mean reversion factor so that it was strong early in the year and decayed to zero by the end of the season. Read the rest of this entry »

Last week, I dug into the data a little to see if there was any empirical basis to the suspicion that the Brewers lineup might not be cut out for October. The result was a new metric, if you want to call it that, called MeatWaste%. This number — the percentage of pitches that end up either in the dead center of the strike zone or out in Baseball Savant’s Waste region — I used as a proxy for pitcher quality. MeatWaste pitches are gifts to the batter, the kind of offering that produces an instant swing decision and either an easy take or a full-force swing.

I found two things: First, that the Brewers are better, relative to the league, on these two pitch locations than they are on the whole. And second, that these easy opportunities come around often in the regular season, but disappear in close playoff games. Simple enough, though there are limits to what this finding allows us to infer about the Brewers’ future. It’s why they play the games, after all. Read the rest of this entry »

Look, I get it. You keep refreshing FanGraphs, and it keeps saying that the Mets are 99.9999% likely to make the playoffs (okay, fine, 79.4%). You’ve seen the Mets play, though. They stink! They’re 32-48 since June 13. The White Sox are better than that! We think they’re going to make the playoffs? These Mets?! What, do we not watch the games or something?

Well, to be fair, our models don’t actually watch the games. They’re just code snippets. But given how the Mets’ recent swoon has created the most interesting playoff race in baseball this year, and given that our odds keep favoring them to pull out of a tailspin, the time is ripe to re-evaluate how our playoff odds perform. When we say a team is 80% likely to make the playoffs, what does that mean? Read on to find out.

In 2021, I sliced the data up in two ways to get an idea of what was going on. My conclusions were twofold. First, our model does a good job of saying what it does on the tin: Teams that we give an 80% playoff chance make the playoffs about 80% of the time, and so on. Second, our model’s biggest edge comes from the extremes. It’s at its best determining that teams are very likely, or very unlikely, to make the playoffs. Our flagship model did better than a model that uses season-to-date statistics to estimate team strength in the aggregate, with that coverage of extreme teams doing a lot of the work. Read the rest of this entry »