The baseball season will soon be upon us, which means it’s time for an age-old question: How long until the best hitter’s reign ends? This year, and seemingly every year of late, that means Aaron Judge. You can quibble about who the best overall player is, but Judge is pretty clearly the best offensive player on the planet. Over the last four years, he has a composite 204 wRC+, miles clear of the competition, and he just put up that exact number in 2025. In 2026, we think he’s going to be the best hitter again, obviously.

Will we in 2027, though? It depends, of course. If Judge looks like his usual self this year, it’s hard to imagine anyone taking the crown. I wanted a little bit more rigor than that, however, so I dusted off the Marcel projection methodology. Marcel is what Tom Tango dubbed the minimum sufficient projection system. It’s as simple as taking the last three years of performance, weighting them, and tossing in some league average.

Let’s take Judge’s last few seasons as an example. I grabbed his wOBA and plate appearances for 2023-2025 and threw them into a table. Then I calculated league average across those three years (the exact calculation uses some weighting to match Judge’s playing time by season). That looks like this:

Turning those into a Marcel projection is simple. Multiply the most recent year’s plate appearances by five, the next-most-recent year’s by four, the next by three, and the league average by two. Take a weighted average of these new values. The result is Judge’s 2026 Marcel projection – which works out to a .440 wOBA. That tracks logically, which is the point of Marcel. It’s really close to what you and I would think about a player’s skill. Post a wOBA above .450 for two straight years, and I’ll expect you to come back to the pack a little but still do something outrageous the next year.

Using this methodology, here are the top projected hitters for 2026:

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This offseason, I’ve taken high-level looks at the offseason decisions made by the New York Mets and the Boston Red Sox. It’s been a popular series, so today, I’m going to use the same framework to offer a holistic evaluation of the Brewers. As a refresher, here’s how I’ve been thinking about the exercise:

“How should we evaluate a front office, particularly in the offseason when we don’t have games to look at? I’ve never been able to arrive at a single framework. That’s only logical. If there were one simple tool we could use to evaluate the sport, baseball wouldn’t be as interesting to us as it is. The metrics we use to evaluate teams, and even players, are mere abstractions. The goal of baseball – winning games, or winning the World Series in a broad sense – can be achieved in a ton of different ways. We measure a select few of those in most of our attempts at estimating value, or at figuring out who “won” or “lost” a given transaction. So today, I thought I’d try something a little bit different.”

I won’t be offering a single grade. Instead, I’m going to assess the decisions that Matt Arnold and the Brewers made across three axes. The first is Coherence of Strategy. If you make a win-now trade, but then head into the season with a gaping hole on your roster, that’s not a coherent approach. It’s never quite that simple in the real world, but good teams make sets of decisions that work toward the same overarching goal. Read the rest of this entry »

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Tyler Rogers makes me happy that I’m a baseball analyst. Not in the same way that Shohei Ohtani does, of course. Not in the same way that Tarik Skubal does, or Bobby Witt Jr., or any other number of superstars. Those guys are great because they do the obviously good baseball things, like running fast and throwing hard and hitting balls far. Rogers looks like an accountant who was hurriedly inserted into the game as a last resort. He also just threw 77 1/3 innings with a 1.98 ERA last season. His career ERA is 2.76 over eight seasons. I don’t know about you, but something about that tickles me endlessly.

Rogers’ superpower is his command. Last year, he walked only seven batters, a 2.3% rate. But that command can be hard to pin down. For instance, take a look at the 26 pitches Rogers threw in three-ball counts:

As you can see from the overlaid PitchingBot command grades, these locations are nothing special. There are too many crushable cookies, too many non-competitive pitches, and not enough action on the fringes of the strike zone. It’s a 42 command grade all in, nothing to write home about. In fact, Rogers walked more batters than league average per three-ball pitches thrown (in a tiny sample, to be clear). When batters got to this point in the count against him, they had a decent chance of reaching first for free. How, then, did he post the second-lowest walk rate in the majors?

To understand that, we’ll have to rewind the count. Walks require three things: a three-ball count, a pitch outside the strike zone, and no swing from the batter. Rogers cuts things off with item number one. Look at how he started batters last year:

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If you spend some time poking around the nooks and crannies of FanGraphs, you’ll eventually encounter one weird thing. Go to our Depth Charts Team WAR Totals page, and you’ll see all 30 teams arranged by the amount of WAR we project them to accrue this season. Go to our Projected Standings page, and you’ll see the winning percentage we expect for each team. Sometimes, those two pages seem to be displaying the exact same information. Sometimes, they don’t quite line up.

Take right now, for instance. We project the Padres for 40.8 WAR, the Giants for 38.7 WAR, and the Diamondbacks for 38.2 WAR. Look at the projected standings, however, and we have the Padres down for a .490 winning percentage, the Giants at .504, and the Diamondbacks at .501. That doesn’t feel right. Shouldn’t the team with the most projected WAR also project for the best record? Well, buckle up, because to explain how this works, we’re going to have to do some math.

We’ll break this one down into two parts. First, what does a team WAR projection mean? Most basically, it’s the sum of each player on that team’s WAR projection, but we’ll have to get more specific than that. Our projection systems can spit out a WAR, but that’s not their real output. They project actual on-field baseball results. Manny Machado’s Depth Charts projection is for 644 plate appearances, 28 doubles, 26 homers, 127 strikeouts, and so on. The WAR part of it gets calculated after the fact. Read the rest of this entry »

As I write this, I’m watching a spring training game on my other monitor, which is a good reminder that another season of baseball will soon begin. Forty-eight of the Top 50 free agents of the winter have signed, with Zack Littell and Lucas Giolito the lone holdouts. That means it’s time for my annual review of contract predictions, mostly mine and the crowd’s.

I like to evaluate my own predictions so that I can get better at making them in the future. I like to evaluate your crowdsourced predictions because it’s fun, and because everyone likes hearing how smart they are. Our crowdsourced predictions have been consistently excellent, arguably better than any industry expert, and that makes displaying them particularly enjoyable.

To evaluate our accuracy, I broke the signings down into three categories: hitters, starting pitchers, and relievers. I also examined the entire Top 50, without positional separation. I used a formula that I discussed earlier this winter as my chief metric of accuracy, but I also checked how close we came on average annual value, total guarantee, and number of years. I looked at how the predictions matched the overall amount of money spent in the market, and also considered how close each individual prediction came. That way, I was able to evaluate two things: Who did the best job predicting the broad market, and who predicted what each free agent would get with the greatest accuracy. Read the rest of this entry »

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Earlier this week, I wrote about the cost of a win in free agency. I loved seeing the discussion of that article online and in the comments section, so I thought I’d set aside some time to consider a few of the questions readers had. Here are my answers to those questions.

What if We Used More Tiers?
If three tiers is good, would four be better? Five? Six? In my initial analysis, I ran all these variations in the background and decided that three was optimal for presentation and clarity. I also determined that the sample sizes would get vanishingly small as we expanded to more and more tiers. But as several readers asked for more granular looks, why not? Here is a four-tier version:

And a five-tier version:

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What are teams paying for a win in free agency? Earlier this month, I answered a FanGraphs Weekly Mailbag question about that very issue, outlining a rule I’ve been using in formulating my contract predictions. I left my explanation loose and vague because it was one of four questions in a mailbag, but to give you the general gist, I think about free agent salaries on a graduated scale, with role players being paid less per win above replacement than superstars. Today, I’d like to back up my argument with a bit more mathematical rigor.

One of the benefits of writing for FanGraphs is that smart baseball thinkers read the site. I woke up last Monday to a direct message from Tom Tango, MLB’s chief data architect. Tango had a few suggestions for further research, a method for adjusting past years of data for current payroll situations, and even a link to a discussion of the cost of a win with Sean Smith. Smith, better known as Rally Monkey, is the creator of Baseball Reference’s calculation of WAR – when you see rWAR, that actually stands for Rally WAR, not Reference WAR. In other words, I got help from some heavy hitters.

With Smith’s excellent article on free agency as a guide, I built my own methodology for examining the deals that free agents receive and turning them into a mathematical rule. I took every starting pitcher and position player (relievers are weird and should be modeled differently due to leverage concerns) and noted their projected WAR in the subsequent season, as well as the length and terms of their contract. I excluded players who signed minor league deals, were projected for negative WAR, or whose contract details were undisclosed. To give you a sense, applying this approach to the 2025-26 offseason leaves us with 89 players, from Kyle Tucker all the way down to Jorge Mateo. Read the rest of this entry »

I’ve been playing around with the new FanGraphs Lab tools a lot recently. At first, it was bug testing, but it pretty quickly turned into fun. One minute, you’re making sure that sliders show up correctly. Next minute, you’re wondering about Logan Webb’s backwards slider. See, Webb throws a big-bending sweeper instead of a gyro slider, but it doesn’t behave at all how you’d expect: It’s good against lefties and bad against righties.

In 2025, Webb put up 5.5 WAR, a career-high mark and his fifth straight season of four or more wins. He used his sweeper a lot to get there. Webb was one of the most frequent right-on-left sweeper users in the majors, and also one of the best. Measured by run value added per 100 pitches, he was 11th in baseball among all righties who threw even 100 such sweepers – and he threw 400 of them. He was 15th in whiff rate for good measure. He was as effective as Paul Skenes was in this situation while going to the pitch three times as often.

But while he was lights out with the pitch against lefties, it fared quite poorly against righties. He was below average, and by a lot. Ninety-one pitchers threw 100 or more right-right sweepers; Webb finished 75th in run value added (or lost, in this case) per 100 pitches. While the league gets about 25% more whiffs with the platoon advantage, his whiff rate with his sweeper was the same against righties and lefties. This all sounds very strange. But when I dug into it, I got some answers. Read the rest of this entry »