The emergence of Statcast (and similar types of tracking data) over the last decade-plus has revolutionized many parts of baseball analysis. A big category that didn’t really exist prior was the notion of “expected” stats. Up until then, numbers were all tallies of results, and proto-expected metrics, like Bill James’ Component ERA, were derived from the classical array of stats. But tracking data opened up new opportunities in this area, allowing us to more closely look at home runs and strikeouts, and see the underlying processes and skills that made those results. While the past is always the past, expected stats are useful when talking about the future.
As someone who made the odd decision to work with baseball projections for half his life, I have a vested interest in finding the best use of this kind of information when predicting the future. Like the Statcast estimates (preceded with an x, as in xBA, xSLG, etc.), ZiPS has its own version, very creatively using a z instead. zStats do have some correlation with xStats, but not a perfect one, as ZiPS uses things like spray data, sprint speed, and plate discipline metrics in its estimates.
It’s important to remember these aren’t predictions in themselves. ZiPS certainly doesn’t just look at a pitcher’s zSO from the last year and say, “Cool, brah, we’ll just go with that.” But the data contextualize how events come to pass and are more stable than the actual stats are for individual players. That allows the model to shade the projections in one direction or the other. Sometimes that’s extremely important, such as in the case of homers allowed for pitchers. Of the fielding-neutral stats, homers are easily the most volatile, and home run estimators for pitchers are much more predictive of future homers than are actual homers allowed. Also, the longer a player “underachieves” or “overachieves” in a specific stat, the more ZiPS believes the actual performance rather than the expected one. Call this the Rule of Isaac Paredes, in honor of a player who constantly stymies zHR. In some ways, we’re projecting how cruel regression toward the mean will be.
More information on accuracy and construction can be found here.
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 »
Last night, my wife’s friend Paula texted me to make sure I’d seen the below play. Paula moved back home to Minneapolis from Brooklyn a few years ago, and we head out to visit her each summer. We do jigsaw puzzles and go to Minnesota State Fair together. It is a lovely tradition. Paula is more of a basketball fan than a baseball fan, but sometimes she’ll reach out to me when the Twins do something surprising. It’s a sweet way of trying to connect with someone who’s important to her dear friend. Last night, however, she just needed to share what she’d seen, because, frankly, it was bit hard to believe. Here are the Royals turning a swinging bunt into a Little League home run via three errors and at least that many terrible decisions:
If you’ve seen this play, you have thoughts. You can’t help but have thoughts. That’s why Paula sent me the video in the first place. When you watch something like this, the thoughts start bubbling up inside you so rapidly that if you don’t find a safe place to vent them, your brain will explode. This play is the baseball equivalent of microwaving a potato. So let’s get to some thoughts.
1. Poor Seth Lugo.
Let us spare a thought today for Seth Lugo, who got dinged with an error and three unearned runs. This would not be Lugo’s finest outing. He would go on to give up six more runs, all of them earned, which means that both his ERA and his RA9 WAR took a beating. After starting his night like this, it’s hard to blame him. But I hold that Seth Lugo was nigh blameless on this play. I avow it with vigor. As such, please find below a list of things that Seth Lugo did right on this pitch:
He got Carson Benge to chase a two-strike fastball that was a good six inches above the zone.
He induced contact so weak that Statcast measured the ball as traveling 0 feet in the air with an indeterminate exit velocity.
He sprang off the mound like a cat who knows how to field groundballs.
He fielded the ball cleanly. Seriously, form this pure would make your Little League coach break down and cry:
He made a quick, off-balance throw to first base. That throw was perfectly fine.
Yeah, you heard me. It was a good throw. It bounced about 12 feet from the bag, giving Jac Caglianone plenty of time to adjust and catch the ball. It would have been easier to field had it been a foot or two farther to Caglianone’s right, but it was by no means offline. I understand that when the ball bounces, the first baseman is absolved from all blame, so the error has to go to Lugo, but that doesn’t mean we can’t be honest with ourselves.
Yup, we’re still on the throw. I realize this bullet point and the last bullet point should be sub-bullet points, but I don’t want to format them that way, and more importantly, I don’t know how to format them that way. Point is, the throw was good! I watched it zoomed in on super slow-motion, so I can tell you that the throw actually brushed the tip of Caglianone’s glove. You could reasonably argue that Lugo should’ve eaten this ball, but his throw was more or less on target and it got there in plenty of time to beat the runner. Good throw. Do your worst, haters.
When everything went pear-shaped, Lugo hustled back behind home plate to back up the play. That’s just good fundamental baseball in the midst of one of the least fundamentally-sound plays you’ll ever see.
He tried to prevent the third error of the play. If you watch the video, you’ll see Lugo shouting and pointing, trying to get Nick Loftin to throw the ball to third base rather than home. I don’t know if that was necessarily the right call, but it certainly couldn’t have gone any worse than the throw to home.
He kept his composure and ended the inning on the very next pitch. Sure, everything kept falling apart for him the rest of the night, but for at least one more moment, Lugo put his head down and retired the batter in front of him.
2. Poor Jac Caglianone.
I feel bad for Lugo because he did pretty much everything right here. I feel bad for Jac Caglianone for the opposite reason. While I stand by my assessment of Lugo’s throw, I don’t mean to say that it was an easy play for Caglianone. It was a tough throw to field cleanly. But he still made a couple tactical errors. He would have been better served waiting back for the hop rather than trying to stretch and pick this ball. He absolutely should have prioritized knocking the ball down over going for a clean catch. But regardless of who was to blame, everybody who’s ever played baseball knows what it’s like to have to turn around and chase down a ball that you failed to catch. It’s a lonely feeling, even when you’re being observed by 32,734 screeching New Yorkers. It can make you do some things you’ll regret. Speaking of which…
3. Where was Caglianone trying to throw the ball?
I’m not just asking for me. I’m asking for everyone on the internet too:
This ball traveled right between third base and home plate. In fact, it went right toward Lugo, backing up like a champ, except 10 feet over his head. Maybe Caglianone was trying to decide between third base and home plate, and he split the difference? Maybe this is just the major league translation of Caglianone’s 6.4 BB/9 as a collegiate pitcher. The most likely answer, though, is that Cags had no idea where he was throwing this ball either.
4. Or maybe Seth Lugo is a sleeper agent.
Hear me out. Lugo spent seven years with the Mets, and five more years in their minor league system. Maybe he engineered this play on purpose. Maybe Lugo has spent the past four years pitching well for the Padres and the Royals as part of a long-term mole operation, waiting all that time for this moment when he could hand the Mets a game on a silver platter. All it takes is one properly-timed, improperly-placed throw, plus six more earned runs. Will the Mets still lose the game? Of course they will.
5. Advertisements on the pitcher’s mound are a blight on the game.
The beauty of the playing field is one of the best things about baseball. That feeling of walking through the tunnel and emerging into a green cathedral is what makes even non-baseball fans keep coming back to the ballpark (well, that and the soft serve in the little souvenir helmets). Every square inch of the stadium is covered in advertisements. They put advertisements on the players’ jerseys. They put advertisements on the players’ heads. They will soon find a way to put advertisements on the players’ faces. That garish black gash on the back of the mound, the focal point of the entire field, is a slap in the face to anyone who cares about baseball.
6. Create your own luck?
While we’re complaining about the advertisements, let’s also note that the company advertising on the back of the mound has an ad behind home plate as well. I’d never heard of this company before, but everything I can find about them on the internet makes them sound like they treat their customers abysmally. But also, they seem to have repurposed the mantra of the villain in Titanic and made it their slogan. So that’s a choice.
7. Poor Keith Hernandez.
Hernandez was in the booth for SNY last night. It must be a unique form of torture for arguably the greatest defensive first baseman of all time, a guy who is constantly harping on the need for good fundamentals, to watch a play like this. Here are the two things Hernandez said during this debacle. He said, “Ohhhhh.” Then he said, “Oh my God.” He wasn’t wrong.
8. Poor Some Other Guy.
The broadcast booth always houses a couple people whom we never see. Producers, researchers, stat people, I don’t know who they are. But they’re there to help out the people who narrate the game for us, and they normally keep quiet. Keeping quiet is part of the job. On this play, though, right when Loftin’s throw went awry, just before that “Ohhhhh” was forcibly torn from Hernandez’s thorax and/or soul, somebody else in the broadcast booth couldn’t help himself. He shouted, “Oh my—” and then remembered himself and cut the exclamation short. Who could blame him? (I suppose it’s possible that this was Hernandez himself, that he had his mic muted but could still be heard through play-by-play guy Gary Cohen’s microphone. But either way, this exclamation was not meant for public consumption.)
9. Poor Tyler Tolbert.
Statcast makes these cool diagrams where they track the movement of the ball and every player on the field. The moment I saw this play, I thought about the movement tracker. I tried to picture what it would look like in my mind’s eye. How far would the center fielder move on a play like this? Who ended up moving the most? I borrowed this one from Anthony DiComo’s MLB.com article about the play:
It’s a lot to take in. Caglianone ran every which way. Right fielder Tyler Tolbert hilariously ended up with the ball about 40 feet from home plate. Do you know how wrong things have to go for a tapper back to the pitcher to end up with your right fielder in foul territory, right near home plate, and in possession of the ball? Tolbert picked up the ball barehanded on the run like a third baseman charging a bunt. And then he realized it was too late. It was all over. There was nothing left to do but turn the ball over to the proper authorities and make the 200-foot jog back out to his natural habitat.
10. Poor Carter Jensen.
You know who moved the least? Catcher Carter Jensen. The rookie just had to stand there like a Walmart greeter as the Mets whipped by him. He stepped out in front of the plate when Benge tapped the ball back to Lugo. He moved to the left side of the plate when Caglianone’s throw went rogue. He stepped even farther out to give Loftin a clear throwing lane outside the base path. When Loftin decided that clear throwing lanes are for suckers and threw the ball directly at the runner, Jensen trotted 15 feet over toward the right side, then retreated back to home plate. But he never made it more than a step or two onto the grass in any direction. This whole play was an elaborate form of bear-baiting, and Jensen was the bear, staked to home plate, beset on all sides by jubilant Mets, with nothing to do but watch helplessly as wild throws zipped by him in every direction.
For the second time in his four-month-old major league career, Konnor Griffin is headed to the injured list. On June 26, the Pirates shortstop returned from a month-long IL stint due to a low-grade flexor strain in his right forearm. He didn’t miss a beat, running a 115 wRC+ and recording four two-hit games in an eight-game span.
But the triumphant return was short-lived. On Sunday’s game against the Nationals, Griffin hurt his glove hand trying to make a diving stop on a Keibert Ruiz grounder up the middle. He told reporters after the game that he was fine, but imaging revealed a tear in the sagittal band of his left ring finger. The team expects him to miss eight to 10 weeks, which would put him on track for a return around early September. This is a major bummer for Griffin, whose career is off to a brilliant, but sputtering start. It’s a major bummer for the Pirates, who sit just three games out of the final Wild Card spot. And it’s a bummer for baseball, as Griffin is one of the game’s most exciting and promising young players.
The emergence of Statcast (and similar types of tracking data) over the last decade-plus has revolutionized many parts of baseball analysis. A big category that didn’t really exist prior was the notion of “expected” stats. Up until then, numbers were all tallies of results, and proto-expected metrics, like Bill James’ Component ERA, were derived from the classical array of stats. But tracking data opened up new opportunities in this area, allowing us to more closely look at home runs and strikeouts, and see the underlying processes and skills that made those results. While the past is always the past, expected stats are useful when talking about the future.
As someone who made the odd decision to work with baseball projections for half his life, I have a vested interest in finding the best use of this kind of information when predicting the future. Like the Statcast estimates (preceded with an x, as in xBA, xSLG, etc.), ZiPS has its own version, very creatively using a z instead. zStats do have some correlation with xStats, but not a perfect one, as ZiPS uses things like spray data, sprint speed, and plate discipline metrics in its estimates.
It’s important to remember these aren’t predictions in themselves. ZiPS certainly doesn’t just look at a pitcher’s zSO from the last year and say, “Cool, brah, we’ll just go with that.” But the data contextualize how events come to pass and are more stable than the actual stats are for individual players. That allows the model to shade the projections in one direction or the other. Sometimes that’s extremely important, such as in the case of homers allowed for pitchers. Of the fielding-neutral stats, homers are easily the most volatile, and home run estimators for pitchers are much more predictive of future homers than are actual homers allowed. Also, the longer a hitter “underachieves” or “overachieves” in a specific stat, the more ZiPS believes the actual performance rather than the expected one. Call this the Rule of Isaac Paredes, in honor of a player who constantly stymies zHR. In some ways, we’re projecting how cruel regression toward the mean will be. Read the rest of this entry »
Each offseason, we fill up these pages with transaction analysis. We dive into trades and free agent signings, qualifying offers, DFAs, non-tenders, Rule 5 selections, and minor league deals, and we even spare some time for manager hirings and firings. Rarely do we devote much space to the comings and goings of coaches. Even the most-prominent, big-name hitting coaches, pitching coaches, and bench coaches do their work almost entirely out of the spotlight. It’s nearly impossible to know what effect they have, if any, and because their heads are the first to roll when things aren’t going right, they come and go with alarming frequency. So when the Mets let Antoan Richardson, their acclaimed first base coach, walk to the division rival Braves in November, I never wrote anything about it. I had thoughts about the development, but not enough analysis to fill a thousand-word article. I settled for a 10-word skeet, and I wrote about Salvador Perez’s contract extension instead. Now that the Mets have been without Richardson, and the Braves have been with him, for half a season, let’s remedy that oversight.
In 2025, the Mets were successful on 89% of their stolen base attempts. That wasn’t just the best mark in baseball. The difference between the Mets in first place and the Cubs in second place was bigger than the difference between the Cubs and the Astros, who finished in 25th. Four teams racked up more than New York’s 147 steals, but because the Mets almost never got caught, Baseball Prospectus still credits them with putting up more base-stealing value than any team in the game. 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 »
Paul Skenes turned in his last truly dominant start on May 12 at home against the Rockies. He threw eight shutout innings, allowed just two hits and no walks, and notched 10 strikeouts. Since then, Skenes has posted a 5.36 ERA over 47 innings. He’s gone fewer than six innings in six of his last nine starts, and seven of his 11 home runs allowed on the season have come during that same span.
It’s safe to say Skenes has been missing a certain je ne sais quoi on the mound over the last month and a half. And I say je ne sais quoi, which literally translates to, “I don’t know what,” because there are competing theories about what, if anything, is actually wrong with him. Some blame poor defensive play behind him and point to his expected stats as proof of unfortunate batted-ball luck. Some say it’s a mechanical issue, while others fear he’s pitching through a physical ailment.
It’s true that Skenes has been on the mound for several obvious defensive gaffes. Everyone remembers Opening Day and the bases-loaded fly ball that center fielder Oneil Cruz lost in the sun. The misplay turned a would-be sac fly into a bases-clearing triple. Fast forward to last week, when third baseman Nick Gonzales allowed two runs to score after his throw home on a bases-loaded grounder hit the runner, and it’s pretty easy to assume the defense behind Skenes has been letting him down all season. The numbers support the impression left by those glaring blunders. Overall, Pittsburgh’s defense has been worth -18 fielding runs (fifth worst in the majors). With Skenes on the mound, the Pirates have posted -7 FRV, which is tied for the worst defensive showing behind any pitcher this season. Moreover, most of that negative defensive value (-5 FRV) has come during his skid. Read the rest of this entry »
That was my reaction on Saturday after seeing the American League’s pitching staff for the 2026 All-Star Game. Sure, I’m generally aware of this collection of pitchers, and yes, there are deserving All-Stars here. But without Tarik Skubal, Garrett Crochet, and Max Fried, the AL lacks the star power of year’s past, especially in comparison to the National League.
Today, I want to briefly walk through the names on the roster so far, why they’re here, and who might join them should the opportunity arise over the next week: Read the rest of this entry »
With just a week of play left before the All-Star break, the playoff pictures in both leagues still look crowded. That should make for a particularly interesting trade deadline. There aren’t many definite sellers, and the two weeks of games after the midseason break could have a huge impact on who is buying and who might be looking to reset for next year.
Our power rankings use a modified Elo rating system. If you’re familiar with chess rankings, you’ll know that Elo is an elegant ranking format that measures teams’ relative strength and is very reactive to recent performance. To avoid overweighting recent results during the season, we weigh each team’s raw Elo rank using our coin flip playoff odds. (Specifically, we regress the playoff odds by 50% and weigh those against the raw Elo ranking, increasing in weight as the season progresses to a maximum of 25%.) The weighted Elo ranks are then displayed as “Power Score” in the tables below. As the best and worst teams sort themselves out between now and October, they’ll filter to the top and bottom of the rankings, while the exercise remains reactive to hot streaks and cold snaps. If you’re looking for a visual representation of the ups and downs of your team throughout the season, look no further than the brand new Power Rankings Board in the FanGraphs Lab.
First up are the full rankings, presented in a sortable table. Below that, I’ve grouped the teams into tiers with comments on a handful of clubs. You’ll notice that the official ordinal rankings don’t always match the tiers — there are times where I take editorial liberties when grouping teams together — but generally, the ordering is consistent. One thing to note: The playoff odds listed in the tables below are our standard Depth Charts odds, not the coin flip odds that are used in the ranking formula. Read the rest of this entry »
