Here Come the 2024 ZiPS Projections!

Matt Kartozian-USA TODAY Sports

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:

Top 50 ZiPS Offensive Comps – Corey Seager
Player Year
Anthony Rendon 2017-2020
Cal Ripken Jr. 1988-1991
Buddy Bell 1979-1982
Edgardo Alfonzo 1999-2002
Troy Tulowitzki 2010-2013
Xander Bogaerts 2018-2021
Robinson Canó 2010-2013
Nomar Garciaparra 1999-2002
Sid Gordon 1945-1948
George Brett 1980-1983
Tony Cuccinello 1935-1938
Rich Aurilia 1998-2001
Kyle Seager 2013-2016
Brooks Robinson 1964-1967
Mike Lowell 2001-2004
Sal Bando 1970-1973
Aramis Ramirez 2006-2009
Neil Walker 2011-2014
Edgar Martinez 1990-1993
Alan Trammell 1985-1988
Robin Ventura 1994-1997
Lou Boudreau 1945-1948
Miguel Tejada 2001-2004
Charlie Gehringer 1931-1934
Hanley Ramirez 2011-2014
Scott Rolen 2003-2006
Chase Utley 2006-2009
Joe Sewell 1925-1928
Whitey Kurowski 1943-1946
Manny Machado 2018-2021
Jose Vidro 2001-2004
Davey Johnson 1968-1971
Justin Turner 2013-2016
Aubrey Huff 2001-2004
Richie Hebner 1973-1976
Eric Soderholm 1974-1977
Barry Larkin 1990-1993
Josh Donaldson 2012-2015
Don Money 1974-1977
Lou Whitaker 1983-1986
Jim Ray Hart 1967-1970
Alex Bregman 2018-2021
Felix Mantilla 1961-1964
Jed Lowrie 2010-2013
Robin Yount 1981-1984
Chipper Jones 1997-2000
John Valentin 1994-1997
Zeke Bonura 1934-1937
Matt Carpenter 2013-2016
Joe Torre 1968-1971

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.

Dan Szymborski is a senior writer for FanGraphs and the developer of the ZiPS projection system. He was a writer for from 2010-2018, a regular guest on a number of radio shows and podcasts, and a voting BBWAA member. He also maintains a terrible Twitter account at @DSzymborski.

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6 months ago

Have you studied whether more contemporary comps are at all better? There are all kinds of nutrition, training, etc sports science things that are available to modern players that were not available to older players.

I wonder if it some point you would underweight a 1950s comp relative to a 2010s comp.

6 months ago
Reply to  Dan Szymborski

It’s often interesting to note how little the pitching staffs of great teams resemble stereotypical notions. The 1939 Yankees had no starters who threw 30 starts, and just one who threw more than 200 innings. The 1953 Yankees (the winningest of the five consecutive WS winners, by a pip) were very similar, differing in this framing only in that Whitey Ford got to 30 starts on September 27th.

6 months ago
Reply to  sogoodlooking

The Cy Young Award was created in 1956. I wonder if that led to establishment of the stereotypical 5-man rotation by (e.g.) increasing the perceived value of pitcher-wins? Just a guess.