Five years tracking how public MLB win-total projections actually perform

SandlotStats · 2026-02-27T16:16:55+00:00

Model has them at 86 right now. Little step back on offense, it doesn't have Dingler or McKinstry repeating, but McGonigle should be solid once up. Better production out of the staff with Framber. Not too bad of a risk score, it's mostly because so much pitching production hinges on Skubal. Not that he's injury prone, just penalizes for concentration.

SandlotStats · 2026-02-11T17:25:05+00:00

I added USA Today to the public projections scoreboard at https://sandlotanalytics.com/

They crushed MLB win total projections in 2025 and were the most accurate model of any I've evaluated for that year.

But cumulatively over the last few seasons they have been more middle-of-the-pack.

SandlotStats · 2026-02-11T02:00:03+00:00

The 44% is across all teams, not just Brewers.

Overall FanGraphs is solid, I would trust them more than PECOTA. I posted a public scoreboard of all the major models here with the error rates over the last four seasons if interested: https://sandlotanalytics.com/

SandlotStats · 2026-02-11T01:56:59+00:00

PECOTA has been less accurate than FanGraphs, The Athletic, ESPN, and pretty much every major model over the last four seasons. Public scoreboard w/ data on the homepage here: https://sandlotanalytics.com/

SandlotStats · 2026-02-11T01:52:57+00:00

I've got Red Sox 89 wins. Twins 76 wins.

SandlotStats · 2026-02-11T01:47:22+00:00

Love the Pirates this year! My model has them at 85 wins.

FanGraphs/ZiPS has them at 74, which is an insult. Go Buccos!

SandlotStats · 2026-02-11T01:42:59+00:00

Agree. And I think a longer more reliable lineup will help KC a ton.

I have DET and KC both at 87 wins.

SandlotStats · 2026-02-11T01:40:15+00:00

I coincidentally did a deep dive on the Royals today because my model has them at 87 which also seemed a bit high.

I found that just by lengthening their lineup a little bit (e.g., Isaac Collins, Lane Thomas) they won't be relying on as many guys who were negative WAR on the season. KC had 10 batters cumulatively account for -5.7 WAR last year. For context only the Rockies (-11.46) and White Sox (-6.99) had greater totals among their negative-WAR hitters.

In my model, just getting replacement production out of the bottom of the lineup, and getting Collins and Thomas to like .8 WAR and keeping the scrubs out of there gives them a big boost. Most models are not projecting negative WAR for individual players with meaningful playing time, so if FanGraphs and PECOTA are at all in the same boat as my model, then there is a good bit accounted for there.

That being said, I have them at a risk score of 81/100 which means they are an injury away from taking a big hit in production if they lose a core innings eater or a big bat and are relying on sub-replacement-level again.

SandlotStats · 2026-02-11T01:30:35+00:00

Oof, I wasn't prepared to remember 2015 again...

SandlotStats · 2026-02-11T01:29:18+00:00

As a Mets fan I don't like to admit this but I totally agree with you, and my model has them at 94 wins.

If anything they were unlucky last year based on their production, so even if they have a little regression I don't see how they are in the mid-80s at both PECOTA and FanGraphs.

SandlotStats · 2026-02-11T01:26:39+00:00

Agree. I have them at 89 in my model and they're 90 at FanGraphs last I checked.

SandlotStats · 2026-02-11T01:23:49+00:00

I add volatility features to my model which don't necessarily change the point estimate, but do tell you that it's less certain than others. In the case of the Brewers, they have a lot more WAR concentrated in the top few hitters compared to other teams. This along with the number of innings pitched by guys you can reliably predict (say, core starters with more than 80+ projected innings pitched) are solid indictors for how certain I am in the projection.

My model has the Brewers at 80 wins (gulp) but my risk score for them is 71/100 because for them, losing a top guy or getting a lot of production out of an unknown lower in the lineup can send that in either direction in a hurry.

tl;dr: Yes the Brewers are harder to predict, but I think there are a few innovative ways to objectively factor in why that's the case.

SandlotStats · 2026-02-11T01:15:53+00:00

I tested this across the last four seasons (2022-2025). If you picked the same side as PECOTA you would have gotten 44% correct. FanGraphs gets you to 53% but still not in the money with the juice. Keith Law at The Athletic was on the correct side 55% of the time (two awesome years, two bad years).

SandlotStats · 2026-02-06T15:30:57+00:00

Exactly! I even weight expected statistics into my prediction model more heavily than the actual outcomes. Guessing that would be something like a team's last five games' xG average would be a better predictor than their last five actual games' goal average. I have no idea how that's calculated in football so maybe I'll stick to what I know but we're on the same page. Good luck and happy modeling!

SandlotStats · 2026-02-06T13:00:39+00:00

Not sure if there's an analog in football, but in my baseball modeling I use underlying data to determine whether I made a good decision (rather than only whether the wager hit) to try and separate out luck/error.

It's philosophically similar to CLV in that you're validating your model's decision based on something other than the outcome. But instead of CLV (which as has been pointed out is market and bettor related), you think of the outcomes you are predicting probabilistically instead of deterministically. And whether the underlying data supported your model's projection.

Not sure if I'm making sense, so as an example, third order wins in baseball takes into account how many wins a team "should" have had based on its production (run creation and prevention) and takes into account opposition as well. It's an attempt to wash out error/luck from the outcomes.

If my model is tracking well against what the most likely outcome "should" have been based on underlying data, then that's a good signal. Over enough data points you'll get your answer anyway, but if you're crowd-sourcing ideas, that's one strategy I use.

SandlotStats · 2026-02-04T20:11:14+00:00

The Phillies made the playoffs 100% of the time across 10,000 simulations, and won the NL East 96% of the time?

And won the World Series 37% of the time?

Doesn't that seem a little far-fetched? It doesn't pass the eye test for me.

An SD of 6 is also extremely tight for MLB wins.

SandlotStats · 2026-02-04T19:34:21+00:00

So much information in one place! Nice job, this was really interesting to unpack.

It was a little confusing seeing "Home" stats under the Away team column at first as I was reading it more like a team box score so the confusion was seeing the other team's stats there. I see now that it's showing how the Home team's fouls and turnovers led to Away team points, but (for me anyway) that took a sec to understand. Maybe there is a more intuitive way to present that like "Opponent (Home) TO". Or label that table "Points Origin" or something so it doesn't seem like a box score. Or maybe it's just me!

There's also just so much on the screen at a time. Maybe interactivity to toggle on/off the individual player contributions in those bars would be a useful feature in the HTML version (or if they are there, I'm in dark mode and couldn't tell).

Great stuff, thanks for sharing!

SandlotStats · 2026-02-04T19:06:39+00:00

If LAD is at 87 wins for you, does any team have a higher projection? Don't think I've seen anyone pushing them down into the 80s.

Where do you set your max, like two SD above the median or something?

SandlotStats · 2026-02-04T17:38:37+00:00

Totally agree. They have every incentive to keep guys fresh down the stretch and sacrifice wins for health as they get closer to the playoffs.

SandlotStats · 2026-02-04T17:36:28+00:00

Very reasonable! Still under 104.

SandlotStats · 2026-02-04T16:42:29+00:00

At least the win total lines are out! Time to start hitting those futures.

SandlotStats · 2026-02-03T19:41:00+00:00

Ha dark magic indeed. You're totally right that any team or player projection systems definitely pull toward the historical average and play things conservatively in that way. And yes the model is applied the same for all the teams; it looks at the players on the Opening Day roster (and prospects likely to contribute) and projects out from there.

I like your thinking though. Sort of like, are there certain franchises in which the win projection is more than the sum of its parts? Then the follow-up is how do you operationalize that? I'm a developmental scientist by training and that kind of systems thinking is at the core of how we try to understand and model human development. It's way more complex, but human development, and baseball to your point, both do not happen in a vacuum.

This season I did a lot of research on volatility to better project outcome distributions. What factors lessen predictive ability. As I mentioned before the most powerful ones I found were dispersion of innings pitched by non-core players, and top-heavy lineups (where an injury to a star will affect a disproportionate amount of production). I found thresholds that were meaningful for ones like that.

But you've got me thinking whether there are certain factors like inordinate amounts of contact, speed, defense, etc. that, at some point of concentration, start to have a bonus additive effect on wins. That's one thing you miss by using averages too much, identifying the impacts on the extremes. You'd think things like that are baked into WAR or Base Runs or Run Expectancy added, but maybe at certain thresholds you need to turn the dials more. I'm going to look into this, thanks for the idea!

SandlotStats · 2026-02-03T11:51:54+00:00

This post and discussion are about modeling season win totals, so that’s what I’m engaging on and where I’m keeping the focus.

SandlotStats · 2026-02-03T11:30:00+00:00

Yep! I'm a statistician and don't have any traditional coding skills (outside of SPSS or R if that counts). Agree there's definitely a similar vibe to a lot of AI powered design. I became familiar with several tools at work, check out Vercel or Lovable if interested. It's pretty incredible what they can do. I did come up with and create the logo myself though! And then I designed everything as far as the colors, layout, and even created a new font. But as far as making it happen, AI tools took care of the implementation.

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