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During the championship game of the World Baseball Classic, a Nolan McLean sweeper made me jump off of my couch. It wasn’t even a strike; it just moved so much from such an innocuous starting point that I reacted instinctively. I clearly wasn’t alone; Davy Andrews wrote about how nasty McLean’s pitches look last week. “Dang,” I thought to myself after I’d calmed down. “It’s too bad someone hasn’t gotten PitchingBot to take a look at that one.”

Then I thought about that slightly longer and chuckled. That someone is me. PitchingBot lives in the cloud, but I have a duplicate copy isolated in a sandbox on my computer. MLB records Statcast data for WBC games. I have a machine that ingests Statcast data and turns it into pitch modeling grades. This wasn’t rocket science (give or take how you feel about the machine learning algorithms powering the model) – I took the data, fed it into the machine, and tinkered with the exact settings until I got model grades to come out.

The tournament features a wide variety of skill levels, from Paul Skenes down to semipros and high schoolers. Setting the population average equal to the average quality of WBC pitching would mean that the grades aren’t comparable to the ones we’re all used to looking at. Thus, I ran the PitchingBot model for every pitch in the WBC, but instead of using the WBC average to mean a 50 grade, I used the 2025 MLB average. That means the model is calibrated to how you’d expect the pitches thrown in the WBC to perform against average major league opposition. Read the rest of this entry »

I’ve been doing a lot of looking at depth charts this week. All of us FanGraphs writers have – these positional power rankings don’t write themselves. When you look at the majors through this lens, you’ll naturally do a lot of thinking about floor and ceiling. The Yankees are playing who at third base? The Brewers are getting how much WAR by avoiding weak spots? The Red Sox have that many outfielders?

I’ve written some team overviews this winter. In them, I make the following claim: “Building a team that outperforms opponents on the strength of its 15th to 26th best players being far superior to their counterparts on other clubs might help in the dog days of August, when everyone’s playing their depth guys and cobbling together a rotation, but that won’t fly in October.” The converse of that claim – that stars matter disproportionately in October – is part and parcel of this depth argument. But is that true?

Some might say that the best time to answer this question is when the playoffs are just around the corner. I’d counter that those people haven’t just spent seven hours staring at a pile of acceptable-but-not-overwhelming third base and starting pitcher options and trying to write something about each one. So in the spirit of doing anything other than looking at power rankings, I decided to test out this assumption. Read the rest of this entry »

Third base is undergoing a generational change at the moment. The late 2010s and early 2020s were dominated by a group of five superstars: Nolan Arenado, Alex Bregman, Matt Chapman, Manny Machado, and José Ramírez. All five are still in the majors, and you’ll see each of them on today’s ranking. But while Ramírez is still the best third baseman in baseball, and while you’ll also find Bregman and Chapman near the top, there’s a new group of stars breaking in at the hot corner. Junior Caminero is only 22. Maikel Garcia is a threat to embark on a decade-long string of defensive awards, and he turned into a great hitter in 2025 to boot. Bo Bichette and Carlos Correa have joined the party from shortstop. Kazuma Okamoto hit NPB pitching so well that he immediately helps Toronto project in our top 10. The top of this list has changed meaningfully in the last few years, and I expect more of that to come.

That said, there’s a shortage of great third base prospects poised for big league action. Colt Emerson is the best prospect with meaningful playing time projected here, and he’s a shortstop playing out of position. There were only five third basemen listed on our preseason Top 100, and two of them (Kevin McGonigle and Sal Stewart) are starting at different positions in the majors this year. Two of the others have ETAs of 2029, with the third set for 2027. In other words, the veterans will probably have a bit more time in the top half of the rankings even as they decline, because Caminero and Garcia aren’t set to be joined by a broad cohort of young stars. Read the rest of this entry »

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 »