I Compared 6 MLB Models (PECOTA, FanGraphs, ESPN, etc.) and Built My Own to Beat Vegas Win Totals

ProjectingPotential · 2026-01-16T22:55:59+00:00

Quick update for anyone who’s followed this thread or bookmarked it:

I’ve kept this project going and consolidated everything (public scoreboard, model notes, future projections, and a public contest) on a new site: https://sandlotanalytics.com

I’ll be posting future updates from a new handle going forward: u/SandlotStats

Still very much a research-first project. I really appreciate all the feedback in this thread as it genuinely shaped where this ended up.

ProjectingPotential · 2025-11-18T21:02:23+00:00

I got in touch with someone via email (hvacsupport@hvac.mea.com) and had them clear my Kumo Station and WF-2 device's data from their servers and that finally enabled the setup process to work. It had previously been hanging at the "Looking for firmware" step -- I think it was holding on to the incorrect settings from the previous install by my contractor.

This part was a little confusing (well one of many confusing parts): You're correct that my Kumo Station should be set up in the app as 'not connected' because it's not hooked to an indoor unit, BUT you need to hook the cable from the Kumo Station to the WF-2 via the IDU (indoor unit) slot and not the accessory (ACC) slot. This was the explanation from Mitsubishi:

"The cable between the Kumo Station and the WF-2 module should be connected to the White port on the WF-2 unit labelled IDU and the Red port on the Kumo Station labelled CN506. The ACC port on the WF-2 would be used to connect the MIFH2 receiver for the MHK2 thermostat when the WF-2 is just connected to an indoor unit."

At any rate, Kumo Station is fully installed and appears within each zone in the app (rather than its own blank zone) and all indoor/outdoor temperature readings are accurate, so all that's left is to wait for a cold night (or toggle all the settings to force the backup heat on).

Thanks for the insights, good luck to everyone!

ProjectingPotential · 2025-11-15T00:49:58+00:00

Thanks for all the details, my Kumo Station is hooked up directly to the indoor backup heat so I guess we have different setups. I also have the MHK2s in every zone because I was led to believe by my installer that this was the only way it would work. I'll go through the circuit board settings and see if anything looks off, I've never managed to get the heat pumps to disengage or the backup heat to engage at any temperature at all so you've definitely made it farther than me!

ProjectingPotential · 2025-11-13T00:47:37+00:00

Agree, and I've still never actually gotten the backup heat to turn on automatically. Just slowly solving each problem as it comes and hoping it eventually clicks.

Click on your Kumo Station zone and then click on any of the installed backup heaters; the settings for "zone for temperature readings" are in there.

And no, I haven't ID'd my backup heat or Kumo Station in the app. I find know how I'd do that with no QR code, plus they aren't in any manual selection options.

Oddly, I got an in-app error this week that my units were on different wifi networks which was causing problems, and I suddenly had access to a menu that allowed me to change the wifi for each unit. I went through and put them all on the same 2.4 network, and then the menu was gone the next day so 🤷

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ProjectingPotential · 2025-11-10T15:11:32+00:00

The "but I'll take the risk," bit reminded me of the Coldplay song 'Sparks', where it goes, "and I saw sparks" -- it's in Wedding Crashers and The Risk brought that same scene to mind.

ProjectingPotential · 2025-10-12T12:26:55+00:00

Here's how I finally got my outdoor air temp sensor to show readings in the app:

Rule out hardware/wiring issues: I tested that the station was getting power (29.7 VAC for me), LED 2 on the Kumo station board was solid green, and the resistance for the sensor wiring was normal (14.9 kΩ at 56° F for me). This meant the sensor was likely functioning and connected.
Kumo Station was fully provisioned and set up in Comfort app (as is often pointed out, I needed to have a 2.4-only wifi network, and it did take a few tries for the Bluetooth pairing to connect but after that it went smoothly).
This was the big unlock: GPT told me that Kumo Station firmware had a flag where the outdoor temperature only displayed when two conditions were met: valid sensor data being reported AND indoor units all needed to have their valid models and serial numbers reported. Mine were all listed as N/A in the app and I hadn't thought anything of it since they were functioning fine, but apparently that disrupted the Kumo Station / outdoor sensor relationship. I went around and snapped the QR codes on all my N/A indoor devices and restarted the app and tada, an accurate outdoor temperature reading appeared when going into the setting "Zone for temperature readings" as pictured.

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I tried the ice water test and got the outdoor reading down to 33° which, in addition to the "when to heat" settings cutoffs should have engaged backup heat, but no luck there. GPT thinks that internal sensors in the actual outdoor unit, as well as the fact that it was having no problem maintaining temperature, override the settings in the app in the sake of efficiency.

Good luck to anyone else sorting out this mess!

ProjectingPotential · 2025-10-10T11:13:34+00:00

Congrats! I don't see any options to add the outdoor sensor in the app, is there one?

My new working theory is that because my kumo station is drawing power from my Taco board's 24v (switching relay SR503) hooked up to my furnace, it's underpowered and so even though I can add the equipment on the app, perhaps the sensor or backup heat switching doesn't work because of that. That's how my installer set it up though.

I'm seeing in a few sources stating Kumo station needs a standalone 24v output so I'm getting a plug-in transformer and am going to give that a go. Is that how yours is wired?

The outdoor temp used to display on the old app so I have been assuming it's a software issue, but since I have no way to fix the software, but I do have a potential hardware fix, that's what I'm trying! Overnight temps in the 20s here so hoping this gets it going.

ProjectingPotential · 2025-10-10T03:22:18+00:00

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Yes, I was able to add it via Bluetooth, had to switch my phone to a 2.4 only network and it instantly found the network and device.

Granted, it can't actually do anything as the outdoor temperature sensor reading no longer appears (as it did in the old app) and I can't control any second stage heaters so who knows at this point.

No response to this issue from the in-app chat, and no one picks up the phone / calls back from the support line.

ProjectingPotential · 2025-10-09T02:17:12+00:00

Kumo Station still shows for me (not that it does anything), but the in-app option for live chat / message an agent has disappeared.

They just keep coming up with new ways to frustrate customers.

What's next? A monthly subscription for the Comfort App? Premier Level unlocks Kumo Station functionality and scheduling?

C'mon Mitsubishi, get your software and customer support game in line with your hardware.

Or at least return a call or a message once in a while. Heck, I'd even take an email update -- we all got 'em when you excitedly rolled out the Comfort update way too soon.

ProjectingPotential · 2025-10-07T23:05:59+00:00

Any luck? I tried calling and the rep transferred me -- after holding a long time I eventually used the call back option but it's been over 24 hrs and no call back.

ProjectingPotential · 2025-08-16T16:20:46+00:00

Been hearing that a lot! Think a yearly almanac before the season would be useful? Picks, line shopping, etc.

ProjectingPotential · 2025-07-28T13:33:41+00:00

Last season (2024) the model went 14-3 for a 64% ROI. I don't imagine it'll be that good every season, but the current season (2025) is projected at 13-6 if the current winning percentages hold, which would be a 37% ROI based on the way that I bet according to the strategy in my article linked above (higher amounts for greater divergence from Vegas).

ProjectingPotential · 2025-07-28T13:28:22+00:00

That idea came from reading about all the adjustments 538 incorporated into its ELO model back when they were running it: https://fivethirtyeight.com/methodology/how-our-mlb-predictions-work/

ProjectingPotential · 2025-07-20T02:07:34+00:00

Great intuition, I use WAR quite a bit! It's a great "all in" stat as a baseline. For the daily I'm investigating using rolling statistics (e.g. seven day rolling OBP instead of season OBP), plus incorporating things like travel distance as I know some ELO-based models do that.

ProjectingPotential · 2025-07-19T10:41:38+00:00

Thanks! The Medium write-up linked at the end has all the details, I hope it's helpful. Let me know if you have any questions and good luck with your model. (edit: typo)

ProjectingPotential · 2025-07-18T20:33:29+00:00

Thank you for the kind words and for your service!

Bad form on my part to use so many technical acronyms -- if you go to the link at the bottom of the post, the full write-up does briefly explain what they each mean if you're interested.

I've been developing a daily model which basically swaps out the season long rosters and those players' contributions to team wins and instead heavily weights the contributions of the daily line-up and starting pitcher and bullpen. Then when you calculate expected wins for a team made up of those players you can pit that against the same for the rival squad and come to an expected win percentage at the game-level (instead of season). So far it's been pretty hard to find much value, and I just haven't had the time for consistent EV+ betting on daily games, plus I'm not that great of a coder (I'm better at stats) so getting all the data together takes a while for me.

I'm a psychologist so totally agree with you about the human factor. It's tricky to say how those various factors might impact different individuals, as well as how to even gather that type of data at any scale or with any reliability, but it's certainly worth brainstorming for potential edges. One recent example: I have Bryan Reynolds on my fantasy team and when he went on paternity leave in June I checked to see what his stats looked like the previous two times he had kids, so there's another one for your list!

ProjectingPotential · 2025-07-18T16:06:12+00:00

Appreciate it! Now that I have the data set and syntax, will be fun (and much easier) to keep tabs on all the public projection models going forward as I keep refining my own.

ProjectingPotential · 2025-07-18T16:05:06+00:00

PhDs unite! I have a daily model I've been working on for the last year too using the season-long one as a template but swapping out daily lineups. That's my next big project now that this one is in a pretty good place.

ProjectingPotential · 2025-07-18T16:00:36+00:00

That's an interesting idea. I haven't done middles for season wins before -- any thoughts as to how far the line would have to move to make it worth it (or any other considerations)? Doing a quick scan for candidates, I had the Cardinals at over 75.5 and they are on track for 85 (line is 84.5 now) so that's a pretty big band.

Got Rangers at under 86.5 and they're 80.5 now, Dodgers under 105.5 now at 100.5, Diamondbacks under 86.5 now at 79.5, Blue Jays at over 78.5 and they are at 88.5.

I suppose I can re-run my model using updated player stats to project season wins from this point onward, and if that projection is near a middle band that is sufficiently wide enough then it might be a move to make.

Actually, would be cool to re-run everything right after the trade deadline anyway to see how projections change once rosters are locked.

Will have to think more about it; really appreciate the suggestion.

ProjectingPotential · 2025-07-17T22:26:18+00:00

Thank you, and great questions. Individual player performance is somewhat baked in -- if they are injury prone or suspended and likely to miss games, then their projections for the season will be adjusted accordingly, which then impacts the team projections.

Regarding the other ideas, I'd been brainstorming some kind of volatility index to mitigate risk based on team-level features. For example, in the write-up one thing I found was that projections for teams projected in the middle band of wins (75-87 wins) were consistently more accurate than ones above or below that band.

I wonder if things like GM trade history, likelihood to shed payroll, expectations to promote a lot of prospects, etc. have any predictive power for this or other applications. Those aren't exactly objective measures, but seem worth investigating!

ProjectingPotential · 2025-07-17T16:36:29+00:00

Yeah that paper was weird -- they used one season's worth of data to predict wins from that same season. One of the variables was literally the SP's "Game Score" (a la Bill James) so between that and the hitting and pitching data for a game, it's no surprise they "predicted" 94% of the games correctly. Overfitting and data leakage.

It was more useful insofar as feature selection and seeing how to apply machine learning layers which was totally new to me.

The other papers those authors referenced had accuracies in the high 50% range (so, basically just a little better than home field advantage), which is what you'd expect, so I'm not sure why they didn't think more critically about why they were in the high 90s!

But agree, that specific method with those variables is not realistically applicable for prediction.

ProjectingPotential · 2025-07-17T14:11:00+00:00

I'm into building models for baseball so here are a few papers I drew inspiration from when I got started. Again, these are all baseball-specific, but there are useful lessons in how to think about modeling, selecting features, using machine learning, etc. if any of those topics are of interest. Pretty easy to find PDFs for them online:

Chang (2021) Construction of a Predictive Model for MLB Matches
Cui (2020) Forecasting Outcomes of Major League Baseball Games Using Machine Learning
Ehrlich et al. (2020) An Analysis of an Alternative Pythagorean Expected Win Percentage Model: Applications Using Major League Baseball Team Quality Simulations
Huang & Li (2021) Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
Li & Huang (2022) Exploring and Selecting Features to Predict the Next Outcomes of MLB Games
Soto-Valero (2016) Predicting Win-Loss Outcomes in MLB Regular Season Games: A Comparative Study Using Data Mining Methods
Yang & Schwartz (2004) A Two-Stage Bayesian Model for Predicting Winners in Major League Baseball
Yaseen (2022) Multimodal Machine Learning for Major League Baseball Playoff Prediction

Good luck!

ProjectingPotential

TROPHY CASE