I can’t tell you why the structure of the big leagues seems to bend towards close races. On August 31, the teams on the right side of the playoff cut line were all at least 2 1/2 games ahead of their nearest competitors. All baseball has done since then is collapse into chaos. The Mets have gone 8-13 to fall into peril, though that’s nothing compared to the Tigers, who have gone 5-15 over the same stretch. The Guardians are an absurd 18-5. Playoff fortunes have ebbed and flowed mightily.

Which players have featured most prominently in those games? There are any number of ways of answering that question. You could look for the highest WAR among contenders, the worst ERA or the most blown saves. You could use fancier statistics like Championship Win Probability Added, which accounts for how much each game mattered to each team. You could use the eye test, honestly – I can tell you from how frustrated Framber Valdez has looked this month that he’s not helping the Astros.

But the closest thing to matching the way I experience baseball, as a fan, is regular ol’ win probability added. My brain doesn’t distinguish between the importance of games when a team is in the pennant race. They’re all important. You can’t lose a random game on September 7 when you’re three back in the standings; you never know when the team you’re chasing will lose five in a row. Championship Win Probability Added distinguishes between whether your team is a long shot or playing from ahead, but when I’m rooting for one of the teams jockeying for a playoff spot, I don’t do that. I don’t think I’m alone in this, either. If every game in the race is equally important to you, Win Probability Added will measure the players who have mattered most in that framework. So I’ve compiled a list of notable players from the past month in each playoff race – and as a little bonus, I threw in a few players who have either stymied contenders or gotten trounced by them. Read the rest of this entry »

He did it! Cal Raleigh launched his 60th home run of the season last night, joining a rare club of elite sluggers. In hitting 60 so quickly, he’s left himself with a chance to match Aaron Judge’s American League record of 62, or perhaps even surpass it. With four games left in the season, how likely is it? I fired up my computer to ask. As a refresher, last week I modeled Raleigh’s home run hitting talent, the parks he’ll play in, and his scheduled opposition to work out which side of the plate he’ll hit from and how likely he is to hit a home run in any given plate appearance the rest of the way. Then I simulated the season a million times to work out the probability of each milestone. Read the rest of this entry »

The Nationals will remember 2025 as a gap year, if they’re lucky. The 2023 and 2024 teams, invigorated by many of the prospects acquired in the Juan Soto trade, each won 71 games, dragging Washington out of the bottom-of-table ignominy that it had occupied since winning the World Series in 2019 and then blowing up the roster. This year’s squad is going to finish with a win total in the 60s and some developmental hiccups, a step backward from the recent past. But lost in the broadly disappointing year is one bright shining beacon: Daylen Lile might just be a keeper.

Lile, a high school draftee in 2021, missed all of 2022 rehabbing from Tommy John surgery, then spent the next two years methodically climbing through the minor league ranks. He started 2025 hot, with a .337/.383/.509 line in his first 40 games in the minors, and got his first taste of the majors when Jacob Young briefly hit the IL. Lile struggled during that first stint but landed in the majors for good a few weeks later when the Nats overhauled their bench. By the All-Star break, he’d carved out a role as a rotational right fielder.

That’s the boring part of this article. The exciting part? As Lile settled into big league life, opportunity beckoned. Young scuffled. Alex Call got traded. Dylan Crews was still out with injury. Lile? He just kept hitting. By August, he was locked in as a starter, and why not? Since the break, he’s hitting a sensational .323/.371/.552 for a 153 wRC+, and turning heads with his aggressive approach and hair-on-fire baserunning. Move over, other baby Nats – there’s a new top youngster in town.

Lile’s game is built around a sensational feel to hit. He regularly ran gaudy contact rates in the minor leagues, and his zone contact rate in the majors is above 90%, squarely in the upper echelon of the league. Like many hitters who make a ton of contact, Lile likes to swing. Unlike those peers, though, he’s done a good job of avoiding the over-chase downward spiral that traps so many singles hitters into lunging at sliders off the plate. 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 »

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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 »

Cal Raleigh is hot. Thumping three homers in a span of two days has put Big Dumper at 56 on the season with 11 games left to play. That binge gives him a realistic shot at hitting a nice round 60 on the season, a threshold that only an elite few sluggers have ever reached. He’s doing it as a catcher, which is absurd. He left the old single-season home run record for catchers in the dust a long time ago.

As I learned all the way back in first grade, 62 is only two more than 60. Given Raleigh’s predilection for blasting bombs in bunches – he hit six in six games earlier this year, and nine in a separate 11-game stretch – Aaron Judge’s single-season AL home run record (and for some people, though not me, the “true” home run record) is definitely in play.

As is tradition at FanGraphs, when someone goes for a home run milestone, we forecast when it might happen. Whether it’s Judge’s quest for 62, Albert Pujols’ push for 700, or Shohei Ohtani’s bid for 50/50, it’s fun and useful to predict when the actual milestone game will occur. I’ll start with the methodology, but if you’re not into that, there are some tables down below that will give you an idea of when and where Raleigh might hit either his 60th, 62nd, or 63rd homer.

I started with our Depth Charts projection for Raleigh’s home run rate the rest of the way. That’s based on neutral opposition, so I also accounted for park factors and opposition. Since Raleigh is a switch-hitter, I used the specific pitchers the Mariners are expected to face to determine whether he starts each game batting lefty or righty, and also used those pitchers’ home run rate projections to determine opponent strength. I used a blend of projected starter, home run rate, and observed bullpen home run rate to come up with a strength of opposition estimate. That let me create a unique home run environment for each game. I also told the computer to randomly select how many plate appearances Raleigh receives each game, with an average of five most likely but some chance of four or six. 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 »

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Welcome to another edition of Five Things I Liked (Or Didn’t Like) This Week. That title is quite a mouthful. Every time I submit it, our helpful back end interface informs me that it is “far too long.” You’re not wrong, WordPress. But I lifted the title and the inspiration for this column from Zach Lowe’s basketball feature of the same name, and every time I consider removing the parenthetical part of it, I remember that the frustrations and failures of the game are part of what makes baseball so compelling. If you never disliked anything about sports, they wouldn’t be so fun to follow. So while every item this week involves something I liked, they also all contain an element of something I didn’t care for. Missed plays, bobbled balls, artificially abbreviated outings, below-average defensive units, lengthy injury recoveries – there are things to dislike in each of these. They all brought me extreme joy anyway, though. Let’s get going.

1. Relatable Frustration
Mike Yastrzemski has been everything the Royals could have hoped for since he joined the team at the trade deadline. He leads off against righties, gets platooned against lefties, and plays his habitual right field. He’s been the team’s second-best hitter behind Bobby Witt Jr., a huge boon as they chase slim playoff odds. Also, when he goofs something up, his reactions are very relatable:

You can see what happened there right away. Yaz’s first step was in, but the ball was actually over his head, and tailing towards the foul line so strongly that he couldn’t reach it. Sure, it was only his second start of the year in left field. Sure, he hasn’t played left for more than a handful of games since 2019. And sure, the ball had plenty of slice on it. But he’d probably tell you the same thing you’re thinking: Major league outfielders, particularly solid ones like Yastrzemski, should make that play. Read the rest of this entry »