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 Toronto Blue Jays.
Batters
None of the projections here will blow your socks off, but on the plus side, very few of them will get your socks wet (and then you’re walking around all day with your feet wet and clammy, and every time you step it makes a little squishing sound, and you think there’s a bit of dirt in there and you’d like to take off your shoes, but that’s why you’re not allowed in Kroger anymore, so you can’t). If social media is any indication, Matt Chapman might be underrated at this point. He was terrible in the second half, but I haven’t found that to be very predictive, and overall, he still gave the Blue Jays about four wins that they now have to replace. Right now, third base, along with left field and designated hitter, looks to be an amalgamation of role players, though I don’t expect that situation to persist through Opening Day, if for no other reason than active rosters aren’t large enough for Toronto to start the season with all three of those unwieldy chimeras. 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 next team up is the Washington Nationals.
Batters
Let’s get the bad news out of the way first. One isn’t the loneliest number on this depth chart, which features the most ones you’ll see outside of a singles mixer. CJ Abrams is the only player to get a two-win projection, and as for any threes, fours, or even something spicier (read: higher)? Well, they’re not invited to this party. We can also dispense with the other bit of bad news: ZiPS is actually more optimistic about a lot of these players than Steamer is.
I was thinking about just ending this writeup there, but that’s too cruel even for me. The good news is that even though the Nationals are unlikely to be very good in 2024, they’re not really supposed to be, and much of this roster is made up of players either still at the start of their career or who nobody in the organization expects to be playing an important role by the time the team is good again. Lane Thomas was a great story this year, as was Joey Meneses in 2022, but does anyone really expect either to be a big part of Washington’s 2027 World Series championship, should that come to pass? The Nats are using this time wisely, and it’s better to look at interesting minor league veterans than washed-up 35-year-olds for the spots you don’t have better prospects to fill. 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 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.
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.
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.
