Every July, we release our annual Trade Value Series highlighting the top 50 players in baseball, taking contract status and performance into account. For the past five years, I’ve been in charge of that exercise, with liberal amounts of help from the rest of the FanGraphs staff and some contacts on the team side. Last year, we added a new evaluator: You.

Today, we’re excited to announce the return of our crowdsourced trade value tool, which can be found here. Let’s review how it works, just in case you didn’t spend last year’s All-Star break furiously clicking through it when you should have been working. The tool, created by Keaton Arneson, and developed by Keaton and Sean Dolinar, aggregates simple “Which of these two players do you prefer?” questions to create a composite ranking. Using the tool is simple. When you pull it up, you’ll be presented with two players and asked to choose which one you think has a higher value in trade:

Ah, but what does “higher value” mean? Sometimes the simplest questions are the toughest. Having a higher value in trade isn’t the same as being better, or being younger, or having a more team-friendly contract. It might be some combination of those things, of course, and of other factors as well. In the real world, players have differing levels of value to teams based on a host of considerations, from how well they plug a hole left by a recently injured star to where the team finds itself in the playoff race. A promising prospect might mean more to a rebuilding club, just as a proven difference maker might move the needle for a team with October ambitions. We can’t tell you how to weigh these factors, which is part of the fun of constructing a trade value list in the first place. What we can do is provide some data that we consider useful in making such determinations and let you decide how to apply it. Read the rest of this entry »

It’s generally bad process to evaluate a player based on a hot or cold streak. Everyone has them, and if you only look at a guy’s best or worst stretches, you’re liable to see things that aren’t really there. That’s just how baseball works; no one plays at the same level all the time. Sometimes the ball looks like a grapefruit, sometimes it looks like a grape. Sometimes pitchers dot the corners with aplomb; sometimes their 3-0 offerings fly wide. No one’s ever as good as they look when they’re on top, or as bad as they look when things aren’t landing. But just because hot streaks are resistant to analysis doesn’t mean they aren’t fun. And for my money, there’s no player who’s more enjoyable to watch when he’s firing on all cylinders than Juan Soto.

In the aggregate, Soto is on track for another successful year, with numbers that look roughly in line with his career marks. His .414 OBP is a hair lower than his career number, but he’s hitting for a bit more power than normal, and striking out less, hence a .570 slugging percentage that would be one of the highest of his career. An early-season injury means he won’t hit his normal 700 plate appearances, and of course the Mets are a dumpster fire, but if I put a bunch of years of Soto’s rate statistics up, you’d struggle to separate this season’s numbers from the pack. That’s basically the idealized pitch for Soto: He can roll out of bed and post a 160 wRC+ with a .400 OBP.

That’s just in the aggregate, though. In the last 30 days, he’s batting .325/.472/.578, good for a 190 wRC+, and walking nearly three times as often as he strikes out. Are these arbitrary endpoints? Of course, and Soto’s not even the best hitter in baseball over that stretch. Batters can do almost anything for a month at a time. Pete Crow-Armstrong is slugging nearly .800 over the last 30 days. Heck, Soto is flanked by Luis García Jr. and Kyle Karros on the wRC+ leaderboard over the last month. It’s not about the raw production. But the way he does it? Man, I can’t get enough. Read the rest of this entry »

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Today at FanGraphs, we’re introducing an updated approach to prospect valuation. You can read the announcement here, and also see the new Farm System Rankings for 2026 on The Board. This post is a detailed methodological examination of how we’ve produced our new estimates. It goes over each step of the process in order, and concludes with a sensitivity analysis. If you’re interested in the broad strokes of our new approach, the introductory post will likely suffice. But if you want to see how the sausage is made, read on.

Prospect Classes
We began with Baseball America’s annual Top 100 prospect lists for each year from 2005-2016, plus FanGraphs’ lists for 2017 and 2018. The BA lists serve as a publicly accessible bridge to the current era of FanGraphs prospect writing, and provide a nice through line with Craig Edwards’ earlier research. We took all instances of a prospect being ranked, including duplicates of the same prospect in multiple years. We converted those ordinal rankings into Future Value grades using a two-step process. First, we separated the rankings into pitchers and hitters and created two separate ordinal lists for each year. Second, we adjusted those ordinal rankings between years by a regressed factor based on that class’ major league production. This allowed us to differentiate between classes – without some type of delineation between years, every top overall hitter would receive the same grade, which is contrary to the way we grade prospects.

This method introduces some potential bias. Judging prospects based on how they turned out inherently brings some information from the future into the mix. We decided that this was the best possible way to systematically introduce varying year-over-year quality to an otherwise ordinal-only set of values, and that it also did a good job of replicating the way that grades might have actually been assigned in the past. The top pitching prospect on the 2010 list was Stephen Strasburg. The top pitching prospect on the 2011 list was Julio Teheran. It’s important to differentiate between the likely grade that they would have received. There’s some volatility in relative value assignment at the very top end of the scale based on this methodology, which is addressed in the sensitivity analysis. Read the rest of this entry »

Seven years ago, Craig Edwards published a landmark study on prospect valuation. Craig’s work built on previous studies by Victor Wang, Scott McKinney, Kevin Creagh, Steve DiMiceli, and our own Jeff Zimmerman, as well as a few prior ad hoc attempts here at FanGraphs; subsequent work on the subject was done by the team at Driveline Baseball. These studies have been hugely important both for FanGraphs’ own evaluation of prospects — among other things, Craig’s work has helped to feed the Farm System Rankings over on The Board — and for the broader public study of the minor leagues.

The reasoning behind these studies is clear and simple. If you want to evaluate a prospect-for-big-leaguer trade, you’ll need to know the expected value of the prospect in the trade. If you want to evaluate how much help is waiting in a given team’s farm system, a quantitative assessment of the talent there is necessary. Even if you’re just wondering how likely your team is to find the next big thing, again, you’ll need some type of framework to understand how often that’s happened in the past.

The previous studies of prospect valuation are still excellent, but they’re all very much of their time. Since Craig published his study in November 2018, the league has changed significantly. The COVID-abbreviated 2020 season changed minor league timelines across the board. The league contracted the number of minor league franchises significantly in 2021. A new CBA, signed before the 2022 season, changed compensation structures and competitive balance tax levels, and introduced the Prospect Promotion Incentive. The cost of a win in free agency has skyrocketed; league-wide payrolls are up more than 30%, and free agent salaries are up by more than that. Read the rest of this entry »

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Right off the bat, I have to tell you that I don’t love the name I gave the statistic I created last week. RE-RA9 doesn’t exactly roll off the tongue. The concept – adjusting run-scoring statistics to account for inherited runners – is easy to get your head around, and I think it’s clearly interesting. But while I had fun writing that article, I wasn’t quite happy with where I left off, either on the name front or on the analysis front.

The name thing probably can’t be fixed. I’m not a great namer of things, historically, and I don’t think that’s going to change today. But while I can’t do anything about that, I quickly expanded my coverage from 2026 to, well, as much of baseball as I could. If this statistic is interesting, it’s interesting as much for its application throughout history as for who’s good and bad at it this year. So with the help of the FanGraphs play-by-play database, which stretches back to 1974, I built RE-RA9 for the vast majority of the era where there were enough relief appearances for this statistic to even make sense. Forget Grant Anderson and Chase Silseth, the two poster boys from my first article. Let’s get some famous guys and seasons in here.

For example, here are the 10 pitchers who have done the most to prevent inherited runners from scoring (RE-RA9 lower than actual RA9), minimum 1,000 innings pitched:

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To repurpose an old quote about the weather, if you don’t like the identity of the best catcher in baseball, just wait a year and you’ll have a new best catcher in baseball. It’s almost uncanny. Every year, there are two contenders for best catcher in baseball. And every year, one of them falls to the wayside, to be replaced by someone new. It’s like clockwork:

Last year was an exception to the rule in that Cal Raleigh was a lot better than Alejandro Kirk, but for the most part, the top two catchers in the game have put up similar WAR. It’s hard to stand out all the way at catcher, and it’s also hard to stay near the top for long. Truthfully, that isn’t all that surprising. Catching is phenomenally difficult on the body, and WAR is a counting stat. The best catchers play a lot, and they wear down. Before long, they’re either playing less often or playing less effectively.

With that backdrop, you might expect one of Kirk or Raleigh to be clinging to the top spot while a new contender appears. But that hasn’t happened. They’ve both been hurt – Raleigh missed a month on the IL, and Kirk missed more than two. And they’ve both played poorly when healthy – both are off to the worst offensive starts of their careers, though Kirk’s is in a tiny sample. There’s still more than half a season to play, and it’s reasonable to think that the two of them might end up posting solid numbers the rest of the way, but they’ve racked up a combined 0.5 WAR so far this year. It’s safe to say that there will be some new faces at the top. Read the rest of this entry »

On April 21, Grant Anderson inherited a hot mess. With the Brewers ahead 3-0 in the fourth inning, starter Kyle Harrison lost his feel. He walked Riley Greene and Spencer Torkelson in two uncompetitive plate appearances, then gave up a rifled line drive on one of his slowest fastballs of the day, a center-cut cookie to Hao-Yu Lee. Pat Murphy called Anderson in from the bullpen to face the bases loaded with no one out.

Anderson delivered nearly flawlessly. He got Javier Báez to ground into a first-pitch double play, then struck out pinch-hitter Kerry Carpenter to escape the inning with only a single run allowed. That run, of course, went on Harrison’s ledger. Anderson got credit for a scoreless inning, no more or less.

On May 16, Chase Silseth tried to pull off the same trick. José Soriano fought through five strong innings against the Dodgers, but he didn’t have it in the sixth. After an inning-opening groundout, he walked four of the next five batters and hit the fifth, driving in two runs and leaving the bases loaded. Silseth came in to put out the fire – but he might as well have poured kerosene on it. He hit the first batter he faced, then gave up a two-run single immediately after, pushing the score to 6-0. He finally got the last two batters of the inning – which meant that in the game’s official log, he pitched two-thirds of an inning and didn’t allow a run.

These two pitching performances went quite differently. Anderson had a tougher task and performed better. But the two of them each got credit for a clean sheet. This is far from the only problem with the way we calculate ERA, but it’s one that stands out to anyone following. Anderson and Silseth didn’t deserve the same counting statistics there. Likewise, Soriano got tagged for three runs, while Harrison got tagged with only one. But that didn’t reflect what happened to them – both of them lost it and had to be removed from the game because of all the runners they’d allowed. Read the rest of this entry »

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