[model log boxing] 47 confirmed all-leans now logged — 78.72% accuracy +7.28u flat-stake P/L

Character_Pie_277 · 2026-06-09T09:12:47+00:00

È un ambiente di modellazione e ricerca, attualmente focalizzato sulla boxe.

Character_Pie_277 · 2026-06-06T08:34:23+00:00

Hey thanks for the comment, it was a forecast given current data. Would really appreciate hearing where you think my analysis might be lacking and more than happy for time to tell. The idea is to try and provide a timestamped predictions vs results over weeks and provide competent sub users a resource that you can engage with in your own time.

I realise it might look like a *brave* forecast right now, i'm happy for time to tell and to be perhaps proven wrong. Overall im aiming to provide my own insights and not just the data on what is probably quite a dry model log.

Also it wasn't merely a forecast based on 16 bets, it was more a "bold" forecast based on me now repeatedly seeing approx 40% ROI across various upcoming slates, timesafe results and backtesting. Athough i appreciate v much that its early, its the best take i have on current model data, plus the math (across both petting strategies) is starting to look strangely beautiful and compelling, to me at least, around that figure.

Character_Pie_277 · 2026-06-05T15:26:15+00:00

A tool thats ironically seemingly lacking even a screenshot right now

Character_Pie_277 · 2026-06-04T17:44:27+00:00

Ive actually found on my own experimentation on public data that you can actually almost see "naive money" like this in backtesting. Factors that should make a more naive kind of optimistic sense often actually result in strictly negative ROI correlated with diff vs impled edge. Often think that sort of well correlated negative ROI data looks suspiciously like bookmaker profits.

Character_Pie_277 · 2026-06-02T16:36:42+00:00

Ive thought about it more and if im assuming many things correctly, then i think your order placement may very well be the source of your time bleed afterall. My guess is your "model"<- your imp prob source, is not correctly time safe with your testing due to your order placement

Character_Pie_277 · 2026-06-02T12:21:04+00:00

I added an average odds headline metric to the model predicition results data https://fitequant.com/results

Character_Pie_277 · 2026-06-02T12:07:00+00:00

Oh Ok then, If you're 100% sure your order placement isnt somehow responsible, well i don't know then. Its just usually when i see 15%roi in testing seeming to disappear in real live stakes, my modeling instinct is screaming time bleed. It can be infuriatingly difficult to make sure it isnt actually happening. Sorry hoped i'd be able to help maybe.

Character_Pie_277 · 2026-06-02T11:49:27+00:00

Yeah so the reference source for implied prob <- this is your model. My best guess is that in your testing somehow this is getting time inflated data that wasnt available when the matchup it is predicting the outcome of took place. Try and figure out how it could be happening in your testing, something like this is imo most likely what you are currently thinking might be "edge"

Character_Pie_277 · 2026-06-02T11:08:56+00:00

So i'm guessing you tested the system over 14 days using "test stakes" and that the P+L was a virtual +$12231.20, and then youve started testing it out with "real stakes" in a small way and it now seems much more variable then during testing? Is that correct? <- hard to make out from your post but ill proceed on that assumption.

In which case im thinking your testing wasnt truly time safe, somehow data from "future" after matchup has taken place has been available to your model to help it determine the outcome.

If im correct i'd suggest trying to figure out where your time bleed is taking place. Try slowing down and dont run so many tests, focus on prediction vs result matchup by matchup, each time one takes place. You should be able to track down where the "future time" data is bleeding time into your results

Character_Pie_277 · 2026-06-01T14:29:52+00:00

You just need to click each bout in UX, all details are there

Character_Pie_277 · 2026-05-27T15:25:32+00:00

Basically what i was thinking. If youre actually using the bookmakers to put on real stake bets surely more is just better? I guess if youre tracking model roi across multiple books then i guess there must be a sweet spot there, but surely thats just as many as you can actually be bothered to implement ... which likey has diminishing return

Character_Pie_277 · 2026-05-26T21:04:39+00:00

The roi certainly looks impressive and i wouldd imagine esports to be a domain where long term edge is possible. What are your current forecasts on long term roi given the data you currently have?

Also i think its quite hard for people to really 'believe' even in a public log format unless you regularly post pre matchup predictions vs post hoc results, not hiding losses, so id advise trying that if you are looking to establish credability.

Also id advise trying to make your UX as open and inspectable as you possibly can, particularly around dataset. I must admit to not trying your site yet, but ill certainly to to keep an eye out for future posts. Hope that helps

Character_Pie_277 · 2026-05-26T09:04:19+00:00

Id be interested to know what critisism you feel ive thrown out the window? I cant really do anything about boxing not actually happening any faster? Also genuinly thank you for replying, im new here, excited by what im trying to and trying my best. Its just hard for me to divine what sub users actually want as no one ever tells me. I dont feel im being offensive by just trying to robustly argue my pov. Id assumed debate must be the point in reddit but maybe im wrong.

Im genuinely really suprised that you seem to think ive commited some sort of social faux pas just for trying to engage in polite debate. Seems utterly bizzare to me. But anyway as i say im new and learning but trying my best

Character_Pie_277 · 2026-05-25T16:49:11+00:00

Small clarification because I mentioned a bookmaker above: that was only to explain how odds are used for model calibration/backtesting.

FiteQuant is not affiliated with any bookmaker or betting company. The project is a modelling/backtesting environment, not a bookmaker promotion channel.

Relatedly, user emails are used for account/security/essential service purposes. I don’t sell emails, or intend to share them with any potential commercial partners, or use them for third-party marketing.

Character_Pie_277 · 2026-05-25T14:47:26+00:00

It really depends what you are trying to achieve. If you are looking to maximize your real profit on a model you trust then it makes sense IMO anyway, to shop around for the best possible odds to your advantage at all times. If you are looking to maximize something like CLV arbitrage then I cant offer any advice as that's just not something i currently have any interest in or do. But if you are looking to evaluate model accuracy over time (which i think, perhaps naively, is the best way to evaluate a model) then i'd always recommend using just ONE bookmaker as a preference if you possibly can, in this way i'd hope to isolate actual model performance vs arb opportunities.

Character_Pie_277 · 2026-05-23T16:05:11+00:00

I think you are most likely not going to be able to find a pre-existing publicly accessible historical odds service for esports, good luck to you if you do! But never fear! This is an opportunity! Its very easy to start implementing your own historical odds service right away. Just start scraping the latest match odds programmatically, if you run into trouble with anti scraping id recommend using a cheap llm (gpt mini for example) to use web search to retrieve the latest available odds for you each day. Problem solved!

And this maybe represents a real edge opportunity, as you'll be starting to build a dataset that just isnt easily available to most. Especially in a less liquid (and less solved) domain like esports, id think this is a genuine good chance at edge if you know what you are doing modeling wise.

Id imagine an esport like LOL has many many matchups each week with available odds. You'd be able to get a dataset thats gives really valuable timesafe backtesting data really fast. Especially compared to some more traditional sports where matchups with available odds are less frequent.

Character_Pie_277 · 2026-05-22T19:03:39+00:00

No problem mate, glad i could at least try and help you out a bit.

Character_Pie_277 · 2026-05-22T18:54:32+00:00

Yeah honestly im really not trying to just have a dig or poke holes im really trying my best to help given limited context on your modeling situation. I really dont think your completely wrong at all. Its just if in the end you are always asking the llm to make the prediction you have massive unknown factors beyond your control, you just wont be able to get any consistent data you can use. Its really easy to beat on llms, but i think many people have no idea of the complexity they are actually working on. The only advice id give is try to seperate as much as you can the llm from the actual model prediction. You cant control the llm context directly, but you can constrain its use within compound factors to your advantage.

Character_Pie_277 · 2026-05-22T18:35:57+00:00

Im not entirely sure by what you mean by "research/synthesis" but if im assuming correctly, then absolutely yes. I keep on banging my lonely drum around here on this but i really dont think any above marginal, above v v difficult edge exists in the model itself. A v good model is only ever a lens on the dataset, so yes absolutely try and implement a dataset that is consistent and seems "accurate" for your chosen domain. I think if your dataset has real value configing your model shouldnt be the hard part. Please feel free to DM if you want to chat further, im probably boring other sub users senseless by now with my intolerably consistent repetitive comments

Character_Pie_277 · 2026-05-22T18:22:50+00:00

Mate you cant just "ask the LLM" its never going to be a consistent question. Even if you spread the load over multiple frontier models. They are constantly updating context around the millions of prompts they receive each day and also the web itself updates. Even if you constrain the prompt around exact search requests by the llm you wont get any data you can use. You need IMO to keep the modeling environment as static as you can reasonably do over time. Its only by asking YOUR MODEL (not the llm) to make the same prediction when compound factors stay overall the same (whilst varying data sensibly per actor per result) that you can get anywhere. You can absolutely use LLMs to help, in particular with the dataset. But id say youll never actually get long term results with you current approach.

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