The Official (And Hopefully Not Too Cringe) 2024 ZiPS Projections

After all the rumors and money and projections, here we are, back at 0-0, with every team having at least some theoretical level of hope for the coming season. Beginning Thursday, actual games will turn these projections to shreds, but this is the best algorithmic projection I have the ability to make for 2024. Just a note that I have not committed an act of decimal cheating; ZiPS does not know that the Padres and Dodgers are 1-1.
The methodology I’m using here isn’t identical to the one we use in our Projected Standings, meaning there naturally will be some important differences in the results. So how does ZiPS calculate the season? Stored within ZiPS are the first- through 99th-percentile projections for each player. I start by making a generalized depth chart, using our Depth Charts as a jumping off point. Since these are my curated projections, I make changes based on my personal feelings about who will receive playing time as filtered through arbitrary whimsy my logic and reasoning. ZiPS then generates a million versions of each team in Monte Carlo fashion (the computational algorithms, that is — no one is dressing up in a tuxedo and playing chemin de fer like James Bond).
After that is done, ZiPS applies another set of algorithms with a generalized distribution of injury risk that changes the baseline PAs/IPs for each player. Of note is that higher-percentile projections already have more playing time than lower-percentile projections before this step. ZiPS then automatically (and proportionally) fills in playing time from the next players on the list to get to a full slate of plate appearances and innings. The model’s had a lot of updates since the pre-spring projections, so probabilities may have moved slightly more than you might have expected from the changes in wins.
The result is a million different rosters for each team and an associated winning percentage for each of those million teams. After applying the new strength of schedule calculations based on the other 29 teams, I end up with the standings for each of the million seasons. This is actually much less complex than it sounds.
The goal of ZiPS is to be less mind-blowingly awful than any other way of predicting the future. The future is tantalizingly close but beyond our ken, and if anyone figures out how to deflect astrophysicist Arthur Eddington’s arrow of time, it’s probably not going to be in service of baseball projections. So we project probabilities, not certainties.
Over the last decade, ZiPS has averaged 19.7 correct teams when looking at Vegas preseason over/under lines. I’m always tinkering with methodology, but most of the low-hanging fruit in predicting how teams will perform has already been harvested. With one major exception, most of ZiPS’ problems now are about accuracy rather than bias. ZiPS’ year-to-year misses for teams are uncorrelated, with an r-squared of one year’s miss to the next of 0.000562. Now, correlations with fewer than 20 points aren’t ideal, but the individual franchise with the highest year-to-year r-squared is the Mariners at 0.03, which isn’t terribly meaningful. If you think that certain franchises have a history of predictive over- or underperformance, you thought wrong, and I’d bet it’s the same for the other notable projection systems.
If you want to check out the pre-spring projections, which talk about the biggest things to happen up to that point, here are the links to the AL and NL projections. Since it has been requested, for these official 2024 projections, I’ve also added 80th and 20th percentile win totals to the standings tables.
| Team | W | L | GB | Pct | Div% | WC% | Playoff% | WS Win% | 80th | 20th |
|---|---|---|---|---|---|---|---|---|---|---|
| Baltimore Orioles | 91 | 71 | — | .562 | 37.2% | 34.8% | 72.1% | 8.8% | 99.0 | 82.2 |
| New York Yankees | 87 | 75 | 4 | .537 | 24.1% | 35.2% | 59.3% | 5.2% | 95.8 | 78.7 |
| Toronto Blue Jays | 87 | 75 | 4 | .537 | 22.4% | 35.9% | 58.3% | 5.0% | 95.3 | 78.7 |
| Tampa Bay Rays | 83 | 79 | 8 | .512 | 11.9% | 29.2% | 41.1% | 2.3% | 91.1 | 74.4 |
| Boston Red Sox | 77 | 85 | 14 | .475 | 4.4% | 17.5% | 22.0% | 0.7% | 85.9 | 69.2 |
Since the last set of projections, the movement here can mostly be attributed to starting pitching. Corbin Burnes provides a huge boost to the Orioles, but some of the benefit of his addition is negated because of less optimistic innings totals for the injured John Means and, more significantly, Kyle Bradish. The injury to Yankees ace Gerrit Cole diminishes their outlook a bit, though they still have the American League’s third highest playoff probability. Lucas Giolito wasn’t expected to pitch the Red Sox to the postseason, but his injury makes a Boston playoff berth even less likely.
| Team | W | L | GB | Pct | Div% | WC% | Playoff% | WS Win% | 80th | 20th |
|---|---|---|---|---|---|---|---|---|---|---|
| Minnesota Twins | 86 | 76 | — | .531 | 41.8% | 15.7% | 57.5% | 4.5% | 94.1 | 77.0 |
| Cleveland Guardians | 85 | 77 | 1 | .525 | 38.4% | 16.6% | 55.1% | 3.9% | 93.3 | 76.7 |
| Detroit Tigers | 78 | 84 | 8 | .481 | 13.2% | 11.6% | 24.8% | 0.8% | 85.8 | 69.3 |
| Kansas City Royals | 73 | 89 | 13 | .451 | 5.9% | 6.5% | 12.5% | 0.2% | 81.4 | 65.0 |
| Chicago White Sox | 63 | 99 | 23 | .389 | 0.6% | 0.8% | 1.5% | 0.0% | 71.5 | 54.8 |
People might still be shocked to see the White Sox with a 1.5% chance of making the postseason, but one of the things I’ve learned after doing this for 20 years is that people – even the most sophisticated ones – tend to underrate how often improbable things happen. Luckily, with so many years in the books, I’ve had the ability to do a lot of calibration! In most simulations, the division features a fairly tight race between the Twins and Guardians for the title and the Tigers finishing third. And because the Central is relatively weak, a Royals playoff appearance would be unlikely but not unreasonably so.
| Team | W | L | GB | Pct | Div% | WC% | Playoff% | WS Win% | 80th | 20th |
|---|---|---|---|---|---|---|---|---|---|---|
| Houston Astros | 88 | 74 | — | .543 | 37.0% | 26.2% | 63.2% | 6.3% | 96.5 | 79.4 |
| Texas Rangers | 86 | 76 | 2 | .531 | 28.4% | 27.0% | 55.5% | 4.5% | 94.4 | 77.6 |
| Seattle Mariners | 86 | 76 | 2 | .531 | 27.4% | 27.3% | 54.7% | 4.3% | 94.0 | 77.6 |
| Los Angeles Angels | 77 | 85 | 11 | .475 | 6.9% | 14.7% | 21.6% | 0.7% | 85.6 | 68.7 |
| Oakland A’s | 63 | 99 | 25 | .389 | 0.2% | 0.9% | 1.1% | 0.0% | 71.6 | 54.7 |
The big change here is a slightly more negative distribution of the innings for Astros pitchers, narrowing their lead over the Rangers and Mariners. I appreciate ZiPS’ bringing the M’s just that much closer to the Seattle Mariners .540 meme. The A’s now project to finish a fraction of a win ahead of the White Sox in the AL basement, which is some kind of victory, I guess.
| Team | W | L | GB | Pct | Div% | WC% | Playoff% | WS Win% | 80th | 20th |
|---|---|---|---|---|---|---|---|---|---|---|
| Atlanta Braves | 95 | 67 | — | .586 | 62.6% | 21.4% | 84.0% | 15.2% | 103.3 | 86.0 |
| Philadelphia Phillies | 85 | 77 | 10 | .525 | 17.9% | 33.4% | 51.2% | 3.7% | 93.3 | 76.7 |
| New York Mets | 83 | 79 | 12 | .512 | 12.9% | 28.2% | 41.1% | 2.3% | 91.2 | 74.0 |
| Miami Marlins | 79 | 83 | 16 | .488 | 6.3% | 20.2% | 26.6% | 1.0% | 87.1 | 70.4 |
| Washington Nationals | 66 | 96 | 29 | .407 | 0.3% | 2.0% | 2.3% | 0.0% | 74.1 | 57.4 |
ZiPS does give the Braves a 1% chance at winning 116 games! Atlanta lost a bit in the probabilities because of some changes in the generalized playing time model that fills in the backups. Even if ZiPS sees the playoffs as a bit less certain for this team than it did six weeks ago, the Braves still have the highest projected win total in the majors. The Marlins took a sizable hit after some negative injury news, a pretty big deal for them since the pitching staff is their source of upside. It sure ain’t the hitting!
| Team | W | L | GB | Pct | Div% | WC% | Playoff% | WS Win% | 80th | 20th |
|---|---|---|---|---|---|---|---|---|---|---|
| St. Louis Cardinals | 83 | 79 | — | .512 | 27.8% | 16.0% | 43.8% | 2.6% | 90.7 | 74.4 |
| Chicago Cubs | 82 | 80 | 1 | .506 | 27.9% | 15.6% | 43.5% | 2.5% | 91.0 | 74.2 |
| Cincinnati Reds | 80 | 82 | 3 | .494 | 20.8% | 14.3% | 35.1% | 1.6% | 89.0 | 71.6 |
| Milwaukee Brewers | 78 | 84 | 5 | .481 | 14.7% | 12.6% | 27.3% | 1.0% | 86.8 | 70.0 |
| Pittsburgh Pirates | 75 | 87 | 8 | .463 | 8.9% | 9.0% | 17.9% | 0.5% | 83.7 | 67.3 |
ZiPS loves Pete Crow-Armstrong and is suspicious of Cody Bellinger matching his 2023 numbers, but bringing him back was still enough to push the Cubs into a near-statistical tie in what was already projected to be a very close race. The Brewers took a hit with the loss of Burnes, and as a result, they slightly boosted the projections for the other four teams in the division.
| Team | W | L | GB | Pct | Div% | WC% | Playoff% | WS Win% | 80th | 20th |
|---|---|---|---|---|---|---|---|---|---|---|
| Los Angeles Dodgers | 93 | 69 | — | .574 | 49.3% | 29.7% | 79.0% | 11.9% | 101.1 | 84.2 |
| Arizona Diamondbacks | 86 | 76 | 7 | .531 | 20.5% | 34.9% | 55.5% | 4.4% | 94.4 | 77.8 |
| San Francisco Giants | 85 | 77 | 8 | .525 | 17.2% | 32.1% | 49.4% | 3.4% | 93.2 | 76.1 |
| San Diego Padres | 83 | 79 | 10 | .512 | 12.7% | 28.5% | 41.2% | 2.3% | 91.3 | 74.0 |
| Colorado Rockies | 67 | 95 | 26 | .414 | 0.2% | 1.9% | 2.1% | 0.0% | 74.5 | 59.0 |
The NL West contenders fighting with the Dodgers – which means the three other teams that are not the Rockies – all received a boost because, since the pre-spring projections, they each added one of the top starting pitchers available, either in free agency or via trade, this offseason. The Diamondbacks, Giants, and Padres are better after having acquired, respectively, Jordan Montgomery, Blake Snell, and Dylan Cease, but the moves haven’t changed the relative positions of these teams in the projected standings. Even so, these deals — along with San Francisco’s signing of Matt Chapman — have created more scenarios in which the Dodgers can be bested for the divisional title, though they remain the favorites.
One thing you see a lot on social media, especially from sites that repost these projections, is outrage that “the best team will only have X wins.” The Orioles are projected to have the best record in the AL, at 91-71, but that doesn’t mean that ZiPS projects 91 wins to lead the AL. Those 91 wins represent Baltimore’s 50th percentile performance in those million simulations, and it is astronomically unlikely that all 30 teams hit their 50th-percentile projections. On average, you should expect three teams to hit their 90th percentile, six to hit their 80th, nine to hit their 70th, and so on and so forth. But again, it’s rarely going to be that neat. So here’s the percentile matrix for the number of wins it would take to secure each of the six playoff spots.
| To Win | 10th | 20th | 30th | 40th | 50th | 60th | 70th | 80th | 90th |
|---|---|---|---|---|---|---|---|---|---|
| AL East | 89.3 | 92.0 | 94.0 | 95.8 | 97.4 | 99.1 | 100.9 | 103.0 | 105.9 |
| AL Central | 83.0 | 86.0 | 88.1 | 90.0 | 91.8 | 93.6 | 95.6 | 97.9 | 101.1 |
| AL West | 86.6 | 89.4 | 91.5 | 93.4 | 95.1 | 96.8 | 98.7 | 100.9 | 103.9 |
| To Win | 10th | 20th | 30th | 40th | 50th | 60th | 70th | 80th | 90th |
| AL Wild Card 1 | 87.4 | 89.2 | 90.6 | 91.8 | 93.0 | 94.2 | 95.5 | 97.0 | 99.2 |
| AL Wild Card 2 | 84.1 | 85.8 | 87.0 | 88.0 | 89.0 | 90.1 | 91.2 | 92.5 | 94.4 |
| AL Wild Card 3 | 81.6 | 83.1 | 84.3 | 85.3 | 86.2 | 87.1 | 88.2 | 89.4 | 91.1 |
| To Win | 10th | 20th | 30th | 40th | 50th | 60th | 70th | 80th | 90th |
| NL East | 88.3 | 91.4 | 93.6 | 95.6 | 97.5 | 99.4 | 101.5 | 104.2 | 107.8 |
| NL Central | 83.5 | 86.2 | 88.1 | 89.8 | 91.4 | 93.0 | 94.8 | 96.8 | 99.6 |
| NL West | 88.8 | 91.7 | 93.8 | 95.7 | 97.5 | 99.2 | 101.1 | 103.3 | 106.4 |
| To Win | 10th | 20th | 30th | 40th | 50th | 60th | 70th | 80th | 90th |
| NL Wild Card 1 | 87.4 | 89.3 | 90.6 | 91.8 | 93.0 | 94.2 | 95.5 | 97.1 | 99.2 |
| NL Wild Card 2 | 84.0 | 85.7 | 87.0 | 88.0 | 89.0 | 90.0 | 91.1 | 92.4 | 94.2 |
| NL Wild Card 3 | 81.5 | 83.1 | 84.2 | 85.2 | 86.2 | 87.1 | 88.1 | 89.3 | 91.0 |
Dan Szymborski is a senior writer for FanGraphs and the developer of the ZiPS projection system. He was a writer for ESPN.com 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.
Being a nitpicker, it seems to me that your last sentence (about the percentile matrix) means something other than what you intended. To me “the number of wins needed to secure” a division title means the number needed to be ahead of the second place team, rather than the number of wins for the first place team. So needing 97.4 wins to secure the AL East means that the second place team has 97.3 – the first place team needed 97.4 to finish ahead of them and secure the title. I would go with something more like “number of wins for the team in each playoff spot” for what (I think) you intend to say.
Economists say this all the time about their models, but unironically.
I would trust ZiPS to be more “correct” over a large sample than even the “best” economists. For one thing economic predictions are based on many more variables with much greater variability. The outputs are also numerous. It’s hard to be even sort of close over 200 countries. While a particular Black Swan event is rare, the odds of “any” unprecedented or highly unlikely event is every few years.
Excellent, excellent, excellent observation. Deserving of many more upvotes. (except all the Commie pinkos on here hate us economists for being right about how awful socialism is)
(said tongue in cheek; mostly)
Of course, the problem is not the getting it wrong, it’s the insistence that the model was right all along. (Obviously this is not all economists but it pops up enough that it’s practically a meme)
Ya they shouldn’t say that. Their models can’t possibly take all the potentially relevant variables into account. They should state the limitations of their models up front. Maybe they do and it’s drowned out by the headline writers.
If you read the stuff in original journal form, they’ll state that they’re assuming an oxygen atmosphere. Assumptions up the wingwang.
That would be explosive! And it would certainly make any models involving the behavior of living humans inaccurate.
Economists aren’t idiots.
Typically you are correct. They are mostly self-assured to the point of believing themselves infallible.
I might have screencapped this comment and will use it in the future, often.
I’m surprised that all the teams seem to have similar differences between 20th and 80th. For example. Depth should matter for that, no?
It does but even with a methodology that varies things, replacements tend to suck.
And if you want to see a difference, look at the Cubs and Cardinals, the Cardinals have the better 50% and 20% projections, but the Cubs have the better 80% projection.
I was looking at the Orioles and Phillies each being 17 games different but I suppose the Orioles really depend on Burnes and Rutschman despite their vaunted depth in IF and OF
Higher upsides on Bellinger and Armstrong-Crow might explain it.
So really the 80th % is 8.5 wins better than mean and the 20th % is 8.5 below.
I’m surprised the better teams have so much upside and the worst teams have so much downside.
Is my projection system so out of touch!?….No, it’s reality that’s wrong.
I may be dumb, but you said “With one major exception, most of ZiPS’ problems now are about accuracy rather than bias”; what’s the exception? (Bias against the favorite team of the reader?)
I suspect inability to account for mid-season trades. Which is a big deal, and tends to make better/overperforming teams even better and worse/underperforming teams worse.
A computerized, generalized trade predictor strikes me as non-trivial.
Excellent, excellent, excellent point. Comment, Dan?
I would like to see an actual analysis of this but my gut tells me its not that big of a deal. Generally speaking teams that acquire talent at the dead line are already pretty high on the win curve which means even getting a really good player isn’t going to move the needle much. And vice versa for teams that trade away talent.
the Royals actually got better last year after the trade deadline thanks in large part to acquiring Ragans and Witt having a monster second half (and also them being almost unsustainably bad the first 3-4 months) so while “Bad team trades away talent = bad team gets worse” might seem like a no brainer I suspect its not quite that simple.
most of the time it is…. but every once in a while…
Every once in a while you get a Rick Sutcliffe or Doyle Alexander.
Truly excellent piece. Thank you.
Dodgers:
2023
Zips 89 wins
Actual 100 wins
2022
Zips 93 wins
Actual 111 wins
2021
zips 99 wins
actual 106 wins
2020
zips 38 wins
actual 43 wins
2019
zips 93 wins
actual 106 wins
Maybe going back a full decade, before almost any of the current FO’s were in place and when many of the best current players were in middle school, obscured the fact that over the last half a decade ZIPs has underestimated the Dodgers by double digit wins (or its equivalent for 2020) literally every year.
Given enough teams and enough 5 year samples (or 3 year samples or 7 year samples. What’s so special about 5 years, one of which was just 60 games?), some teams are going to outperform expectations for 5 consecutive years.
But that’s not what Dan said…he said:
“If you think that certain franchises have a history of predictive over- or underperformance, you thought wrong”
The numbers above on the Dodgers clearly refute this.
I mean over history. Over the 20 year period, the Dodgers have missed quite a ton. Over a short period, you expect more clustering.
In this case there’s a cause: between midseason moves and me just apparently doing a crappy job reading the tea leaves, the Dodgers have had players/distro playing time 5.7 wins per year better than the preseason estimates.
Is that 5.7 win average with 2020’s 60 game schedule?
Midseason moves crossed my mind but it’s a pretty big gap even accounting for that.
Also regarding the 20 or 10 year period, the problem is that franchises aren’t the ones making moves and developing players, front offices are.
The Friedman front office has brought in different people than who the Dodgers had 10 or 20 years ago so there isn’t any real reason to expect that results from that time are relevant to current day. I’m not claiming some metaphysical explanation that the Dodgers inherently win more games, but maybe their current guys have found something out that we don’t know about.
Per 162
History is longer than 5 years..
Assuming the chance of dead on is negligable, then out of 30 teams, you’d expect someone to be running a 6 year streak of consistent misses in the same direction most of the time.
Yes, .5^5 * 30 is approx 1, but the odds of a 93 win team hitting 111 wins aren’t 50%. The Dodgers have for 5 consecutive seasons hit probably 95%+ outcomes.
And some teams are going to outperform expectations for 10 consecutive years. What’s so special about 10 years?
I understand what p-hacking is and that stuff but the Dodgers are a very smart, distinctive, and successful franchise and I wouldn’t rule out that there’s something that leads to them outperforming projections. There’s some possibility that they’re a false positive but there’s some possibility they aren’t.
Also outperforming expectations for 5 consecutive years is distinct from outperforming expectations by about 10 games for 5 consecutive years. I don’t understand why so many people on this site don’t understand effect size and only focus on sample size.
Counting 2020 is silly. If 2020 went against your theory, you’d throw it out in two seconds and (somewhat correctly) call anyone that questioned that an idiot. Just because it happened to go your way doesn’t mean you should still include it.
I’d just add that you probably couldn’t even retrofit a model that both projects the Dodgers for close to the wins they got and also functions with regard to other teams in a halfway reasonable manner. Do you think the model should just arbitrarily give the Dodgers several extra wins?
Lol, what do you mean “go my way”? I just noticed that the Dodgers have clearly been an outlier in zips accuracy.
And I’m not throwing 2020 out b/c the odds of the Dodgers outperforming by 5 games over a 60 game season were incredibly low. Obviously 2020 was probably more likely than 2022 93 vs 111 but it was still highly improbable in the same direction as everything else. Maybe the weirdness around 2020 made zips less reliable than it usually is which further increases the probability of a team diverging from expectations but it was still pretty unlikely and supports the idea that zips is underrating the Dodgers.
And no, I don’t think zips should arbitrarily give the Dodgers extra wins. I think Dan & others should look into whether the Dodgers do anything differently in terms of roster construction, platooning, reliever usage, or whatever and test to see if those factors make a difference generally and whether models that account for these factors outperform zips moving forward.
I get that you can’t just look at past misses and retrofit a model to hit them, but you also shouldn’t ignore significant errors in a model, which the Dodgers clearly represent for whatever reason.
I want you to imagine that you didn’t know the Dodgers win 105 games a year and look at their past rosters and say that those teams should, on average, win 108 games per 162. You wouldn’t do it.
I want you to know that the Dodgers averaged 106 wins in 2019 & 2021-23 and tell me you think they ran out 94 win teams those years.
The season looks to be nice and competitive.
Yay!
Then the injuries will crop up.
(Boo!)
Breakouts, too.
Hopefully nobody fall out too early.
The Rockies, Nationals, and A’s are already out on winning the World Series and the season has not yet started. That is early.
Hey man, I’m a Nats fan too, but think about it – only three teams are really out of contention this year? That’s pretty great by MLB standards. And only one team with a greater than 50% shot at its division? Also pretty great. All six divisions look like wire-to-wire slugfests between at least two, sometimes four rivals. It’s gonna be fun as heck. I’ll just make sure to mentally play the Howie Kendrick homer “bonk” sound effect every week or so.
until zips and other fg models use statcast data it will always be behind
They don’t?
Guru, why make such a definitive statement when you clearly don’t know what you are talking about?
Read this, THEN comment
https://blogs.fangraphs.com/here-come-the-2024-zips-projections/
glad to see some of it is finally being incorporated.
Fantastic. My question: Was AI used in the creation of these projections? And when can I expect ZIPS to begin incorporating AI-generated imagery to accompany the stats?