For the 20th consecutive season, the ZiPS projection system is unleashing a full set of prognostications. For more information on the ZiPS projections, please consult this year’s introduction and MLB’s glossary entry. The team order is selected by lot, and the next team up is the Tampa Bay Rays.

Batters

With the sudden collapse of the Cardinals, the Rays are arguably the holders of the crown in the solid-but-not-spectacular department. With one problematic exception, just looking at the depth chart, you see a whole bunch of twos and threes but no obvious MVP candidates, unless Yandy Díaz finds yet another extra gear in him. But there’s also almost nowhere on the diamond where you expect a huge collapse if forced to find an emergency fill-in. Manuel Margot can capably take any outfield job handily, and elsewhere, Curtis Mead, Vidal Bruján, Junior Caminero, and Jonathan Aranda can cover most anything needed outside of catcher. Read the rest of this entry »

For the 20th consecutive season, the ZiPS projection system is unleashing a full set of prognostications. For more information on the ZiPS projections, please consult this year’s introduction and MLB’s glossary entry. The team order is selected by lot, and the first team up is the New York Mets.

Batters

At the very least, there’s a good sense of clarity when looking at the Mets depth chart. Francisco Lindor and Brandon Nimmo are plug-and-play options — just stick them in, and if they’re your biggest problem, you’re doomed anyway. Pete Alonso qualifies as that as well, though the Mets are not long from having to make a decision on whether it’s better to offer him a potentially ludicrous contract extension or find a new polar bear (they’re endangered after all!). Jeff McNeil will likely have a season somewhere in between his 2022 and 2023. Francisco Alvarez has a hold on the starting catching job now that he’s outlasted his predecessor (Tomás Nido) and the guy who really, really liked playing said predecessor (Buck Showalter). It’s also obvious that this is a crucial season for Mark Vientos and Brett Baty; the Mets don’t have infinite patience. There likely need to be better solutions in left field, and it’s time to start thinking about a post-Starling Marte right field.

While people are looking for the Mets to make big, splashy signings, it’s also a team that could use an extra bat or two in reserve. With questions at third and in left and right, it’s really hard from a roster standpoint to keep a platoon DH with no defensive value hanging around unless he absolutely crushes his side of the platoon (and Daniel Vogelbach really doesn’t). Eduardo Escobar was traded for good reasons, but he was handy to have around. The fixes to the offense might be low-key because of the need in the next section of this article. There aren’t any young phenoms really threatening to seize a roster spot from any of the offensive stragglers; the Mets have four offensive prospects in our Top 100 who are about a year away from making a real impact in the majors. The fifth, Ronny Mauricio, certainly has upside at second, but then the question becomes how much value McNeil really has as a corner outfielder.

Pitchers

There’s a lot of work to be done here. Kodai Senga is written in with permanent marker, and while ZiPS is very lukewarm — to be nice — about José Quintana, he’s certainly going to be in the rotation as well. After that, the Mets have a deep stable of just-a-guy types; the rotation is probably the biggest hurdle preventing the team from having a nice little bounce-back season. Not to pick on Mike Vasil, who I think will be a serviceable fourth/fifth starter for a while, but if Mike Vasil is this high in the projections, you’ve got some slots to fill. The Mets may have been relieved of the worry of Max Scherzer and Justin Verlander’s inevitable aging curve cliffs, but even if their time with the team didn’t go exactly to plan, without them, the rotation looks like a smoking crater. The good news is that while the free agent market has a real lack of impact bats, starting pitching is well-stocked, even before you consider the availability of Yoshinobu Yamamoto and Shōta Imanaga.

A healthy Edwin Díaz is a boon for the bullpen, though he’s not enough to single-armedly make this a plus unit. The relief corps is less of a smoking crater than the rotation, and ZiPS is more or less is cool with the rest of the group in Brooks Raley, Drew Smith, Trevor Gott, and Phil Bickford. ZiPS sees Josh Walker as a pretty decent swingman option, if a very low-ceiling one. Still, there’s room to improve. I don’t think the starters who fail to make the rotation next spring have electric enough stuff to be overly enthused about their bullpen chances, so the Mets will likely need to find an arm or two here. It doesn’t have to be a Díaz-like arm — good luck finding that — but a couple of mid-tier relievers might keep the wheels from coming off this apple cart.

Ballpark graphic courtesy Eephus League. Depth charts constructed by way of those listed here.

Players are listed with their most recent teams wherever possible. This includes players who are unsigned or have retired, players who will miss 2024 due to injury, and players who were released in 2023. So yes, if you see Joe Schmoe, who quit baseball back in August to form a Belgian Death Metal Skiffle Band that only plays songs by Franz Schubert, he’s still listed here intentionally. ZiPS is assuming a league with an ERA of 4.33.

Hitters are ranked by zWAR, which is to say, WAR values as calculated by me, Dan Szymborski, whose surname is spelled with a z. WAR values might differ slightly from those that appear in the full release of ZiPS. Finally, I will advise anyone against — and might karate chop anyone guilty of — merely adding up WAR totals on a depth chart to produce projected team WAR.

As always, incorrect projections are either caused by flaws in the physical reality of the universe or by the skillful sabotage of our friend and former editor. You can, however, still get mad at me on Twitter.

Once again, it’s time for me to fire up my computer and crank out the yearly team-by-team ZiPS projections. This is where I’d normally do my shtick, but we have a lot to get to, so imagine a quote from a 19th century personality, an allusion to a 13th century battle, and a 1980s pop culture reference, and then cram them all together for your own haute couture Szymborski pablum! We’ve got business to take care of, so no time for shenanigans.

ZiPS is a computer projection system I initially developed in 2002–04. It officially went live for the public in 2005, after it had reached a level of non-craptitude I was content with. The origin of ZiPS is similar to Tom Tango’s Marcel the Monkey, coming from discussions I had in the late 1990s with Chris Dial, one of my best friends (my first interaction with Chris involved me being called an expletive!) and a fellow stat nerd. ZiPS quickly evolved from its original iteration as a reasonably simple projection system, and now does a lot more and uses a lot more data than I ever envisioned it would 20 years ago. At its core, however, it’s still doing two primary tasks: estimating what the baseline expectation for a player is at the moment I hit the button, and then estimating where that player may be going using large cohorts of relatively similar players.

So why is ZiPS named ZiPS? At the time, Voros McCracken’s theories on the interaction of pitching, defense, and balls in play were fairly new, and since I wanted to integrate some of his findings, I wanted my system to rhyme with DIPS (defense-independent pitching statistics), with his blessing. I didn’t like SIPS, so I went with the next letter in my last name, Z. I originally named my work ZiPs as a nod to CHiPs, one of my favorite shows to watch as a kid. I mis-typed ZiPs as ZiPS when I released the projections publicly, and since my now-colleague Jay Jaffe had already reported on ZiPS for his Futility Infielder blog, I decided to just go with it. I never expected that all of this would be useful to anyone but me; if I had, I would have surely named it in less bizarre fashion.

ZiPS uses multi-year statistics, with more recent seasons weighted more heavily; in the beginning, all the statistics received the same yearly weighting, but eventually, this became more varied based on additional research. And research is a big part of ZiPS. Every year, I run hundreds of studies on various aspects of the system to determine their predictive value and better calibrate the player baselines. What started with the data available in 2002 has expanded considerably. Basic hit, velocity, and pitch data began playing a larger role starting in 2013, while data derived from StatCast has been included in recent years as I’ve gotten a handle on its predictive value and the impact of those numbers on existing models. I believe in cautious, conservative design, so data is only included once I have confidence in improved accuracy; there are always builds of ZiPS that are still a couple of years away. Additional internal ZiPS tools like zBABIP, zHR, zBB, and zSO are used to better establish baseline expectations for players. These stats work similarly to the various flavors of “x” stats, with the z standing for something I’d wager you’ve already guessed.

How does ZiPS project future production? First, using both recent playing data with adjustments for zStats, and other factors such as park, league, and quality of competition, ZiPS establishes a baseline estimate for every player being projected. To get an idea of where the player is going, the system compares that baseline to the baselines of all other players in its database, also calculated from whatever the best data available for the player is in the context of their time. The current ZiPS database consists of about 140,000 baselines for pitchers and about 170,000 for hitters. For hitters, outside of knowing the position played, this is offense only; how good a player is defensively doesn’t yield information on how a player will age at the plate.

Using a whole lot of stats, information on shape, and player characteristics, ZiPS then finds a large cohort that is most similar to the player. I use Mahalanobis distance extensively for this. A CompSci/Math student at Texas A&M did a wonderful job showing how I do this, though the variables used aren’t identical.

As an example, here are the top 50 near-age offensive comps for World Series MVP Corey Seager right now. The total cohort is much larger than this, but 50 ought to be enough to give you an idea:

Ideally, ZiPS would prefer players to be the same age and position, but since we have about 170,000 baselines, not 170 billion, ZiPS frequently has to settle for players nearly the same age and nearly the same position. The exact mix here was determined by extensive testing. The large group of similar players is then used to calculate an ensemble model on the fly for a player’s future career prospects, both good and bad.

One of the tenets of projections that I follow is that no matter what the projection says, that’s what the ZiPS projection is. Even if inserting my opinion would improve a specific projection, I’m philosophically opposed to doing so. ZiPS is most useful when people know that it’s purely data-based, not some unknown mix of data and my opinion. Over the years, I like to think I’ve taken a clever approach to turning more things into data — for example, ZiPS’ use of basic injury information — but some things just aren’t in the model. ZiPS doesn’t know if a pitcher wasn’t allowed to throw his slider coming back from injury, or if a left fielder suffered a family tragedy in July. I consider those sorts of things outside a projection system’s purview, even though they can affect on-field performance.

It’s also important to remember that the bottom-line projection is, in layman’s terms, only a midpoint. You don’t expect every player to hit that midpoint; 10% of players are “supposed” to fail to meet their 10th-percentile projection and 10% of players are supposed to pass their 90th-percentile forecast. This point can create a surprising amount of confusion. ZiPS gave .300 batting average projections to three players in 2021: Luis Arraez, DJ LeMahieu (yikes!), and Juan Soto. But that’s not the same thing as ZiPS thinking there would only be three .300 hitters. On average, ZiPS thought there would be 34 hitters with at least 100 plate appearances to eclipse .300, not three. In the end, there were 25; the league BA environment turned out to be five points lower than ZiPS expected, catching the projection system flat-footed.

Another crucial thing to bear in mind is that the basic ZiPS projections are not playing-time predictors, at least with players without firm possession of a full-time job in the majors. By design, ZiPS has no idea who will actually play in the majors in 2024. ZiPS is essentially projecting equivalent production; a batter with a .240 projection may “actually” have a .260 Triple-A projection or a .290 Double-A projection. But telling me how Julio Rodríguez would hit in a full-time role in the majors in 2022 was a far more interesting use of a projection system than it telling me that he would only play a partial season (in the end, quite obviously, he played a full year). For the depth charts that go live in every article, I use the FanGraphs Depth Charts to determine the playing time for individual players. Since we’re talking about team construction, I can’t leave ZiPS to its own devices for an application like this. It’s the same reason I use modified depth charts for team projections in-season. There’s a probabilistic element in the ZiPS depth charts: sometimes Joe Schmo will play a full season, sometimes he’ll miss playing time and Buck Schmuck has to step in. But the basic concept is very straightforward.

What’s new in 2024? Outside of the typical calibration updates, there’ll be an extra table in this year’s projections. Don’t worry, the 80/20 splits are returning, but I’m adding split projections into the team-by-team rundowns as well. Usually I create these for the benefit of companies using my projections for their baseball games and calculate it sometime in February. But this year, I successfully integrated that model into ZiPS and, after repairing all the things I broke doing so, platoon splits are now being spit out with the usual array of numbers.

Have any questions, suggestions, or concerns about ZiPS? I’ll try to reply to as many as I can reasonably address in the comments below. If the projections have been valuable to you now or in the past, I would also urge you to consider becoming a FanGraphs Member, should you have the ability to do so. It’s with your continued and much appreciated support that I have been able to keep so much of this work available to the public for so many years for free. Improving and maintaining ZiPS is a time-intensive endeavor and reader support has enabled me to have the flexibility to put an obscene number of hours into its development. It’s hard to believe that ZiPS is now 20 years old. Hopefully, the projections and the things we’ve learned about baseball have provided you with a return on your investment, or at least a small measure of entertainment, whether you’re delighted or enraged.

The first of MLB’s major awards to be announced for 2023, the Rookie of the Year awards, were given out Monday evening, with Arizona’s Corbin Carroll and Baltimore’s Gunnar Henderson taking the laurels in the NL and AL races, respectively.

Getting inappropriately annoyed with year-end awards — more specifically in 1995, the year Mo Vaughn beat Albert Belle in the AL and Dante Bichette confusingly finished second in the NL — was one of the things that got me reading Usenet. A high schooler at the time, I had little idea that it was the start of a surprising career path. And even back then, I was frustrated that the writers who voted for these awards didn’t always make convincing arguments about their picks and, occasionally, offered no justifications at all.

I still believe that this kind of transparency is crucial for the legitimacy of any type of award. This is ostensibly an expert panel; if it’s not, there’s no purpose for the award to exist. As such, a secret ballot is not appropriate the way I believe it is for, say, a presidential or parliamentary election. So, as usual, this is my explanation (or apologia depending on your point of view) of why I voted the way I did. I don’t expect 100% of people to agree with my reasoning, which I doubt has happened for any opinion I’ve expressed ever, but that doesn’t mean I don’t owe you, the reader, the details of my vote.

This is my fifth Rookie of the Year vote. Previously, I gave my first-place votes to Spencer Strider, Trevor Rogers, Pete Alonso, and Corey Seager. This year, my ballot, starting at the top, was Carroll, the Mets’ Kodai Senga, and the Reds’ Matt McLain. Let’s start at the top. I’m also including preliminary 2024 ZiPS projections because, hey, why not? (They didn’t have any bearing on my vote, nor did the preseason projections.)

The Easy Part: Corbin Carroll

My last two first-place votes were close for me, and it took a while to decide on them. But this one was the easiest since Seager in 2016 (and I’m not forgetting Alonso versus Michael Soroka). Everyone expected Carroll to steamroll the league, and that’s just what he did. And while he didn’t have a Mike Trout-esque rookie season, who does?

For much of the season, Carroll logically was part of the MVP discussion, though by the time September rolled around, Ronald Acuña Jr. and Mookie Betts had an obvious advantage, with Freddie Freeman and Matt Olson being clearly superior, too. But if I had voted for the NL MVP, Carroll would have still landed somewhere in the back of my ballot. He hit .285/.362/.506, clubbed 25 homers and stole 50 bases, and played all three outfield positions at least respectably. He is the type of player for whom the phrase “speed kills” makes sense, because his skill set is broad enough that he can actually weaponize that speed. For the season, he was seventh in sprint speed, had dominating baserunning numbers beyond stolen bases, and in 90-foot splits, he was bested only by Elly De La Cruz.

The Still Pretty Easy Part: Kodai Senga

I’m inclined to like Senga considerably more than his WAR simply because he has a significant history of outperforming his peripherals in Japan as well, so there’s more basis for believing in his ERA than for the typical pitcher in this position. Because of that, I’m closer to bWAR on Senga (4.4) than I am to fWAR. If forced at gunpoint to name the Dan’s Brain WAR for Senga, I’d probably put him at 3.8–4.0 or so. Also, that’s a very weird use of a firearm.

There’s always a writer or two who complains about Japanese players being eligible for the RoY award, but I think the idea that they shouldn’t be is preposterous. Nippon Professional Baseball appears a bit closer to the majors than Triple-A ball in the U.S. is — something like Triple-A 1/2 — but it’s a very different kind of league. While Triple-A hitters may be easier than NPB hitters, you’re also facing a rather different style of play and plate approaches, and now that some of the recent rule changes have hit in the majors, Triple-A ball is roughly a not-as-good MLB.

Despite facing different types of hitters, a spate of different rules, and against the backdrop of New York pressure and a collapsing team behind him, Senga was one of the few players who could really be counted on there. He had some issues with walks early on, and to his credit, he adjusted. But it wasn’t actually his control that was the issue; he actually threw more strikes earlier in the season! Instead, the issue was that after putting up an out-of-zone swing rate above 30% in each of his last two seasons in Japan, he was down in the low-20s early on with the Mets. As time went on, he got a better feel on how to lure MLB batters to their doom; in the second half, his 31.1% out-of-zone swing rate was right where it was in Japan.

The Excruciating Part and the Fifth Wheel: Matt McLain versus Nolan Jones versus James Outman

I don’t see Rookie of the Year as necessarily meaning Most Valuable Rookie, but as Best Rookie. As such, in a kind of small-scale examination of Hall of Fame candidates’ peak versus career numbers, I don’t necessarily think measures against replacement are as important as in the MVP voting, which has directions that more strongly imply an emphasis on quantity.

Outman was probably the most valuable of the three hitters I listed above, but he also got a lot more playing time, winning the job from the start. Both McLain and Jones out-hit him from a quality standpoint, with a 128 wRC+ from McLain, a 135 from Jones, and a 118 from Outman. I might discount this if there were evidence from their minor league time that the major league time was flukier, but both played in Triple-A just about how you’d expect from their actual major league performances. Outman was an excellent player and a big part of why the Dodgers survived the loss of a lot of players, but I would have him fifth in a larger ballot because he wasn’t quite as good as McLain or Jones. Per WAA on Baseball-Reference, both McLain and Jones were well ahead of him.

McLain versus Jones was very difficult for me, and I went back and forth on it the entire Sunday I made my vote (the last day of the season). And it still wasn’t an obvious result, more a 51%–49% judgment; if asked on a different day, I might have said Jones instead of McLain. But at the end of the day, I had to pick one. McLain hit almost as well as Jones did and played the hardest non-catcher defensive position. I don’t like deciding based on small things, but it’s inevitable if the big things can’t settle the score. The slight nudge to McLain comes on the balance of having the more valuable defensive versatility (2B/SS for him versus 3B/OF for Jones) and the fact that he played for a team that was playing higher-leverage games all season, with a deep roster of prospects that could push him off a job at any time. The Rockies, meanwhile, were a basement dweller without a lot in the cupboard.

Jones may have just missed my ballot, but it’s no negative reflection on what was an excellent season. I was quite perturbed that he didn’t start the season in Colorado, with the Rockies apparently deciding that Mike Moustakas was nine years better in age than Jones, but they at least weren’t stubborn after he crushed pitchers in the Pacific Coast League. That wRC+ of 135 was an OPS+ of 138 if you like the simpler approach, and both numbers are park-adjusted, so he was Actual Good, not merely Coors Field Good.

Short on Pitching: Bobby Miller, Eury Pérez, and Andrew Abbott

Outside of Senga, no pitcher was close to making my ballot, though these three came closest. The Dodgers should be greatly pleased about having Miller’s services, but his numbers weren’t enough to balance out a rather low innings total. Pérez not being called up until May was a handicap, and while the Marlins being cautious with his workload to the extent of giving him a bit of a mini-vacation in July may be good for his future, it’s hard to give a Rookie of the Year vote to someone who threw less than 100 innings. Abbott’s mid-rotation performance was absolutely needed by the Reds, but again, not quite enough.