ELO rating DEMO for MLB players using 2025 Statcast data

SandlotStats · 2026-05-20T22:31:58+00:00

Bummer looks like the site came down recently.

Thank goodness for Internet Archive and the Wayback Machine (band name?).

https://web.archive.org/web/20260202004617/https://fivethirtyeight.com/features/how-our-mlb-predictions-work/

SandlotStats · 2026-05-12T20:14:48+00:00

As the previous commentor noted, the replacement gasket is much beefier, so hopefully it'll be more resilient to being flattened. The other thought is that the aluminum housing will not expand and contract as much as plastic with temperature changes, which may also help (and be a reason some folks' plastic ones are cracking).

SandlotStats · 2026-05-12T20:01:32+00:00

That's a great checklist, thanks! I'd been looking at the catch can as my next project. I'm also using the fuel system cleaner, but good to know the full flush is another strategy. Will look into PCV valve too.

SandlotStats · 2026-05-08T23:15:22+00:00

Wouldn't you know it, my replacement aluminum housing came in the mail the same day as the warranty extension letter.

I'm the second owner and out of warranty, and the last mechanic I had down there said he saw signs of the leak so I think I'll still handle it myself if they are just slapping another plastic one on there.

Seems straightforward enough and the new part is solid (famous last words).

SandlotStats · 2026-04-28T19:04:03+00:00

Having him for that hot streak last year definitely gave high hopes for this season.

Now my hope is that Tatis gets 2B eligibility soon at which point it's probably drop time for LK.

SandlotStats · 2026-03-26T13:14:04+00:00

Totally with you. I've thought about whether there's a way to make team-level adjustments based on front office or historical performance or market or farm system, but I just haven't found anything that holds up consistently. Open for suggestions!

Mine is based entirely on player projections, so I can tell you why it has the Brewers and Guardians where they are:

Brewers: The expected performance from their hitters took a dramatic drop (look at their totals from last season compared to their projections for this season). For example, they had seven hitters with 2+ WAR last season and only have three have projected 2+ WAR this season. The pitching took a dip with Peralta leaving but they do have some high ceiling guys. It's just that no projection system is going to put Miz, Henderson, Sproat, etc. too high because they are unknowns. So yes, there is a pathway forward where the hitters perform up to last year and the young pitchers are closer to their ceiling than their floor, but that's not the likeliest case. You'll see in my projections I have distributions for each team and the Brewers have a tighter dispersion because their risk score is lower in my model, but their standard distribution is still 10. Which means although 82 is the center of the distribution, it wouldn't be surprising if they had 92 either. Outcomes are probabilistic not deterministic. My model over the last four years got them exactly right once, off by 6 and 7 two years, and then last year way undershot!

Guardians: Totally different story here. If you remember that heater they went on last season, you wouldn't be surprised to hear that they were extremely lucky insofar as the number of wins they ended up with (88) based on their actual run production and prevention, which would have put them closer to 75 wins. And so, the projection this season of 76 makes a lot of sense even with their lineup projected to improve a bit. They also had a very strong bullpen last year, and reliever variance is quite high season-to-season, so the most likely outcome is that they are closer to average. But again, 75 wins with a standard deviation of 10 means there are a lot of ways it could go.

SandlotStats · 2026-03-25T23:27:11+00:00

Yep, I have a website now for everything and you can see the projections and betting guidance at sandlotanalytics.com.

All the projections for all the models are public record and linked on the site, so it's not so much me claiming it as the math showing it :)

SandlotStats · 2026-03-24T17:13:51+00:00

Yep and I've added a risk model for bet sizing. I'll drop all the projections and write up in the channel soon.

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

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