u/Character_Pie_277

Here is the current public record for the default model: 24 confirmed all-leans bets, 79.17% accuracy, +4.07u profit, 16.96% ROI. 

https://preview.redd.it/zl8yklp0rw1h1.png?width=1366&format=png&auto=webp&s=894a8e11be4bb3e030088e7bdd4e29ba10665bc0

Four more results have now been added to the fitequant default model log. I’m continuing to post these updates here so the model’s behaviour can be judged against a visible, timestamped record rather than retrospective claims.

As usual, I’m treating these posts as a public timestamped model log rather than asking anyone to take claims on trust. The relevant matchup/results/backtesting pages are public, so if you want to inspect the record quietly, ignore it, disagree with it, or come back to it later, all of that is fine. My aim is just to keep logging the model behaviour clearly enough that the record can be judged over time.

One clarification on the dataset/audit path: the screenshots are not meant to be the dataset. They are summaries. The public audit path is through the UI: result pages link through to the individual bouts, fighter profiles, model predictions, implied odds, outcomes, and model configuration. 

Anyone looking to audit these results for themselves: You do not need to sign up to inspect the core public results/log, or the linked fighter, matchup, odds, prediction, model, and result pages behind it. The dataset is intentionally exposed through the public UI so the record is easy to inspect.

Auth only required for backtesting and saving user model configs.

Pending(cancelled draw etc) rows stay visible for prediction data reference, but headline metrics only use confirmed win/loss results. 

default model results link: https://fitequant.com/results

Quick UX note…

Avg diff vs implied shows the average gap between the model’s win probability and the market’s implied probability. Positive means the model saw value; negative means the strategy included leans the model did not think were good prices. 

Accuracy = did the lean win; diff vs implied = did the model like the price. 

Part of the reason I’m logging this on Reddit each week is that the posts themselves create a public timestamped audit trail. FiteQuant has the deeper UI links, but the basic record is being built here in public: predictions before results, then results afterwards. I can’t quietly rewrite that history later without damaging the credibility of the whole model log.

The easiest audit is to clone the default model, tweak one assumption, and track whether your version beats it over future locked results.

Clone it. Tweak one assumption. Let the time-safe log decide.

Default model:https://fitequant.com/models

No account is needed to inspect the public log. Sign-in is only needed for backtesting or saving model configs.

As ever, verify any data that matters to you against sources you trust.

Any questions at all, just ask. I would love to be able to increase comprehension around what i’ve built here.

Thanks,

Dan

Hi everyone,

This week there is no value shown overall across a very very slow boxing weekend.

All bouts tracked this weekend are big favourites, so as expected the model doesn't see any value this weekend.

https://preview.redd.it/h9hhmmyvna1h1.png?width=1385&format=png&auto=webp&s=941776ff06aa40951199ce20119c2753bb7f58d2

Fitequant default model: https://fitequant.com/upcoming
Algobetting model: https://fitequant.com/upcoming?model_id=21

So i thought this weekend would be a good time to review the data so far on the fitequant model with a “bet every lean” strategy.

What i’m doing here is showing what has happened if you bet on the fighter the model thinks will win in every bout so far, regardless if it thinks there is any value or not.

This shows how even a selective model like the one i’m tracking here, on quite a “slow” domain like boxing can still produce interesting and valuable results each weekend.

https://preview.redd.it/eygrlgvxna1h1.png?width=1385&format=png&auto=webp&s=1744879e7a4e092f15914044ee26fb6eda8bdc9c

Fitequant default model results:
https://fitequant.com/results?prediction_strategy=all_leans&period=all&per_page=20

Algobetting model results: https://fitequant.com/results?prediction_strategy=all_leans&period=all&per_page=20&model_id=21

The “only value picks” strategy that ive logged so far will continue to be logged in a new betting strategy summary field in screenshots, and also as default in UX.

But i think if we adopt the bet every model lean strategy, for this model log,  we can see some really interesting data already.

I’m now confident that the model avg vs implied (edge) is long term approx 20% (using value picks only) and long term ROI also as expected with this all leans strategy approx 20%.

But the really key thing to keep your eye on each week is the “profit” data.

My expectations are for profit to go up overall over time, after each weekend. As you can see even with the likely sub optimal “bet every lean” strategy profit is already very healthy after 20 bets.

It stands to logical reason that the ROI for the model using the  “only value picks” strategy should be a bit higher, right now 26% ROI  looks reasonable but i think still too early to tell on that, i’d probably want at least another 10 “picks” before making any kind of longer term ROI prediction on “value only” strategy

For this weekend in particular, id expect a return less than 20% across 4 bets on every model lean strategy,  as in the best case all big favorites win on not very appealing odds. 

The figure im actually most interested in on the above results screenshot is the all model leans accuracy, this defines how “good” the model is at just predicting the correct winner. In boxing fights are rarely 50/50 so the current rate of 75% on all model leans is i think expected for a strong boxing model.

As always i’m more than happy to clarify anything to try and increase clarity, if anyone has any questions. 

Thanks,
Dan

Hello everyone — useful weekend from a modelling point of view.

I have 3 out of 4 possible confirmed results for this update. One bout was cancelled after a fighter missed weight, so it does not resolve.

Here are the latest public results for the FiteQuant default model I’ve been logging here over the past few weeks:

https://preview.redd.it/5i2tnsy4ci0h1.png?width=1402&format=png&auto=webp&s=d70a98be17100257516d6b9b7a098e7bef51031f

Audit link: https://fitequant.com/results

ROI assumes 1u only on rows where the selected value strategy would act. 

I’ve also been tracking a separate algobetting-focused iteration of the default model. It is off to a promising start, but the sample size is still extremely small, so I would treat it only as an early experimental configuration for now: 

https://preview.redd.it/gphy0u36ci0h1.png?width=1402&format=png&auto=webp&s=4402d04b89378bc5ec07551799b838f3ecbe0535

Audit link: https://fitequant.com/results?model_id=21

Something interesting

Something useful for audit purposes:

The simplest way to scrutinize the system for yourself is probably to clone the default model, adjust the configuration if you want, and then track how it performs over time.

That is available on the models page:

Audit link: https://fitequant.com/models

Importantly i’d always recommend checking any data against objective external sources you can trust.

A quick note on time safety, because the distinction can be confusing at first:

Public results are logged predictions resolving forward. Those are the cleanest public audit trail.

Strict backtests are historical simulations. They use only time-safe fighter ratings and synced odds data, so later bout results cannot enter the model inputs. 

Backtest outputs can still change over time if model configurations are changed, fighter data is refreshed, or data quality is improved. That is expected behaviour, but it means historical backtests should not be confused with the exact public prediction shown at the time of a bout.

Audit link: https://fitequant.com/testing

Looking forward to this weekends boxing results with the model im tracking here, i thought i’d start with a bit of backtesting i did on last weeks data.

Now that i’ve got 17 fully time safe bouts, i can start to make real use of the time safety modes on my backtesting to provide valuable data faster.

First I wanted to check the actual impact of objective stats, i thought there was a non trivial chance that with the novel modelling environment + boxing domain might mean there might be signal in strict objective data only.

To test this I created an objective stats only model.

Auditing: objective only model link: https://fitequant.com/models?model_id=19

I also wanted to test opponent rating derived objective stats so I created a second opponent rating derived model, focusing on JUST the two opponent rating stats in the modeling environment. 

Auditing: opponent derived model link: https://fitequant.com/models?model_id=20

In summary I discovered that objective stats do indeed have signal a bad one. In this screenshot you can basically, i think, see “naive bets” 

https://preview.redd.it/zx5c2pt6lwzg1.png?width=1397&format=png&auto=webp&s=dce2934e71c9cde9304fff1b7528b9c3e607f827

I also tested the opponent derived model using the non strict time safety option in backtesting to allow bouts that have potentially non time safe opponent ranking derived stats in sample. 

So basically what you are seeing here is either edge or more likely post hoc calibration of opponent ranking by me. I do think the opponent derived data is calibrated well across the DB, but it seems more likely to me that this is in fact just a small amount of expected time bleed.

https://preview.redd.it/jveh4d6blwzg1.png?width=1393&format=png&auto=webp&s=377a12f188baa978fd02f337ebefaf7d4a92ec15

Anyway i decided it was enough to go on to switch all objective stats apart from opponent derived as ignore, as they all appear to be actively worse when used in a naive way for roi

So at this point let me introduce… ta da… the algobetting model

https://preview.redd.it/lbx8iraa5xzg1.png?width=1386&format=png&auto=webp&s=4a5365cfe21b9bd42f257b44c9262257f7c0f5c5

Ill get to the options in a sec, but the reason the algobetting model exists is so i can edit factor weightings and log them publicly here, whilst still tracking the unedited default model

Auditing: algobetting model link: https://fitequant.com/models?model_id=21

Auditing: default model link: https://fitequant.com/models

The algobetting model is just a “cloned” iteration of the default model, but it has objective stat weightings as described above.

I backtested weighting subjective stats higher on this model but results on strict timesafe data were as follows.

https://preview.redd.it/o8yrrbmflwzg1.png?width=832&format=png&auto=webp&s=4fdbd92744fe2a235a5c97d0469fa0c6f4c3f009

The good news is small sample strict time safe backtesting looks pretty good so far for the algobetting model

https://preview.redd.it/rhdbkb5i4xzg1.png?width=838&format=png&auto=webp&s=796f04c676bb857ee6d4cf406758006e0268d04e

Just to quickly explain how the backtesting actually works around time safety. Fighter ratings are syncd with closing odds when closing odds available, so theres no actual possible way for the llm inference to bleed time on “subjective” inference.

Auditing link: https://fitequant.com/testing

From a boxing POV a pretty slow weekend indicated on locked predictions as of today.

https://preview.redd.it/7wzyvh2slwzg1.png?width=1388&format=png&auto=webp&s=db1c8036802caceab0a58ce7a88db3c84c48d895

Both default and algobetting models have just 1 pick in total, and im unfortunately only expecting 4 results on a very slow weekend modelling wise. I’m just hoping that i get all four results confirmed as expected.

The pick is interesting, Mosely Jr is clearly the worse fighter on “subjective stats” but has several matchup factors in his favour. Both the models weight matchup factors low overall (id discovered that just turning them off seems to make picks worse on current data) 

But when several line up strongly like this it can still generate a pick. Which is expected behaviour for the model, the key thing is its only just a Mosely Jr pick, so the disagreement vs market feels appropriate

https://preview.redd.it/1s23kd7ylwzg1.png?width=1401&format=png&auto=webp&s=417d816b1e2cca117cf588cfa69d6b26155f4e23

Auditing link: algobetting model Mosely Jr. matchup link:
https://fitequant.com/compare/382-shane-mosley/398-serhii-bohachuk?bout_id=191&model_id=21

Good weekend overall from a modelling pov this weekend,

I got 7/9 of the confirmed results I was expecting. So now in total users have access to 17 bouts in “strict time safe only” mode backtesting. (after removing cancelled /no winner bouts) 

I also now have what I'm grandly calling my “time safe data pipeline” working well enough that i can expect a similar amount of new bouts to be added to the system, and be available to users automatically each week. 

This means its now possible for users to begin getting more valuable data more quickly (from backtesting) 

The really disappointing thing was the bout i’d previously had trouble with and even logged here previously as an example, the benavidez bout, i had to essentially end up throwing away.

Here’s what I initially thought was the locked prediction result data for the user model im logging here...

https://preview.redd.it/ldl6n3p714zg1.png?width=1600&format=png&auto=webp&s=c41d7c1b3931b0f6f86b10bce381bff38a55c1ea

The really annoying thing is that although I do think this data is accurate. I know that the UX was showing the benavidez bout as a ‘zudro’ sanchez underdog pick previously.

This had been a really unexpectedly troublesome bout for me, as previously logged.. It’s doubly annoying as id actually checked this bout quite carefully and thought it was a surprising pick for the model but also concluded it was working as expected.

I did my best to manually verify the early bouts before I even started posting here, and I am confident that these predictions are actually correct as per the timestamps indicated for each. 

But this is the risk of publicly logging a brand new data system: sometimes the remaining edge cases only become obvious once its too late.

In the above I’ve screenshotted the incorrect state, corrected public stats and tracking downward, and excluded that bout (plus one more that possibly showed incorrect prediction data) from the user-facing confirmed results / strict time-safe backtesting UX so no user will unknowingly include it.

So this will be the state of play for this log and this model’s results in ux going forward...

https://preview.redd.it/14lqjyqs14zg1.png?width=1513&format=png&auto=webp&s=c6bf4c43fc0429ea11690d4571ae22977641c7fd

I’m genuinely annoyed, because this is exactly the class of thing i’d worked so hard on, with the data pipeline, to prevent happening. But I know how it happened, and I’m adding a guard so this specific issue cannot silently recur.

The good part is that the pipeline still added 7 new strict time-safe confirmed results this weekend. That is the thing I’m actually most pleased about. The value here is not picks; it is the fact that the system is now producing (at a satisfying rate) a growing, timestamped, falsifiable dataset that users can backtest against without relying on memory or retrospective inference.

Something interesting.

Something further i’d like to touch on here might seem at first quite unrelated.

But its actually something that I don’t think is talked about enough in modelling. UX

When attempting to create a novel multi-user, multi-model, modeling and backtesting environment an unexpected challenge i faced was actually, believe it or not, UX.

In attempting to resolve this I decided to adopt an approach where the UX is effectively through the “lens” of the currently selected model, for example the default one (if no user models exist)

The idea is that the user can easily access any relevant data on any fighter, result, prediction etc without having to constantly think about what model they are using.

I appreciate this is not entirely specific to this model exactly, but i’d intended this log to be a demonstration of system behaviour. 

Its just genuinely something i’d really appreciate some feedback on. 

My real hope of joining this forum was to generate some feedback and maybe help identify issues, as well as get some new ideas. So any help would be massively appreciated.

fitequant.com

Thanks,
Dan

Hello, yes me again I'm afraid…

Turns out im a bit of an idiot, i should have waited until Friday before logging model predictions as I always get a few more through on the days leading up to the fight, and these “smaller” undercard fights are usually where the model sees value (as you’d expect)

So ill just keep the predictions for Friday in future, if anyone still wants to read it by then.

But you might actually find this post interesting…

One quick thing before I proceed. Im pretty sure that at least some of you think what i’m doing is essentially creating rich context llm prompts. And i’m sure you might think you’ve seen this movie before. I’m not. 

The only real LLM use was to create a structured new data source that might not be fully accurately expressed in the odds, especially in a sport like boxing, where no decent stats app even exists.

I don’t want to make this post go on forever (hah!) but if anyone would like to know a bit more about this just ask..

But just to be clear. The user makes the model. I created the modelling + backtesting environment with all matchup logic etc

https://preview.redd.it/4z4ghusxwiyg1.png?width=1465&format=png&auto=webp&s=2851b584a8acf801b20b20bab10a6cf5aca9d0cd

So very excitingly 9 total new time fully time safe bouts which will take me up to 20 in total assuming all results are confirmed. I’m thinking I might be able to start seeing what longer term ROI might begin to look like at that stage.

There’s a good overall selection of picks i think. This is a pretty fair representation of how aggressive vs conservative I wanted the model to be. Boxing is quite slow, and im encouraged by it mostly seeing value in smaller fights.

Obviously the big underdog pick is interesting but I dont want to go on forever in this post (hah!) so instead i’ll just focus on this pick…

https://preview.redd.it/a1thzurywiyg1.png?width=1488&format=png&auto=webp&s=2f830ab2c121fe5cd64be6172a37aef01784c858

This is interesting, its an underdog pick in a big title fight. Its also structurally very interesting. I had noticed that to my horror the weightclass hadnt been resolved correctly for this bout only due to a very temporary issue with my import system (now resolved)

Anyway when i sorted out the weightclass that was enough to move a close overall benavidez pick to a sanchez (underdog) pick, looking at the matchup data, it does make sense, although benavidez is “stat” wise the better fighter he did have multiple matchup factors against him, and with benavidez now being correctly identified as going up a weightclass and taking a corresponding disadvantage, the pick is now behaving as expected.

The matchup data is really interesting on this one. Ill forgo pasting any links, but it can be very easily accessed if anyone is interested.

Also on this one the height vs reach delta matchup is against Benavidez.

I’ve found that to be a successful factor previously, especially in less “big fights” its often had good results. 

You can also see this in the big underdog pick for this weekend. 

https://preview.redd.it/20v4nspaxiyg1.png?width=1481&format=png&auto=webp&s=e4ccfcf241a882c70bf9aea3e9b4247ce7c8a33a

Something interesting. 

I thought it might explain what ive created a little better (i appreciate it is a bit weird) if you looked at the model config tool. 

All users can create their own model using this, with custom weightings in a fixed factor model universe. All models start with the current default model config (the one im tracking here), so if anyone wants to “play along”, theres no actual cost to do that. I’m NOT trying to basically just sell dressed up LLM inference here.

https://preview.redd.it/gygrcicexiyg1.png?width=1462&format=png&auto=webp&s=43d8587faa7744fb7f19811088a289a65361481b

Anyway take a look at these backtesting results. What i was doing here was testing different variations of the default model to check how much weighting should actually be appropriate for the ai confidence factor.

In my modelling environment all “subjective values” from the llm, punching power, stance advantage etc, have an associated confidence value from the llm, and you can choose how much or little to weight it for your model. 

For the default model I had set it to low, because I expected it make the model overall more or less conservative. When the llm is confident im expecting it to agree with market sentiment more. I thought switching off would be too reckless, but any higher than low and it wouldnt make enough picks.

Turns out I might have been correct. I did some backtesting, this is using some non time safe bouts so ROI is highly inflated, but thats not even the real story. 

Instead you need to look at profit. ROI and accuracy were not affected as much as anticipated even with batch runs as ive done here. I think the reason for this, is mostly only ranked fighters in my DB so far, so little actual variation in ai confidence, but as new unranked fighters are now coming in via upcoming bout coverage, i expect the ai confidence factor to have much more relevence.

But its clear if you average the highly inflated profits then low looks the best way to go given the data I currently have. 

But the exciting thing from my POV is 9! New time safe bouts hopefully by Sunday. That will take me up to 20 and although not ideal, and I have to be extremely careful about overfitting in backtesting, i think it i can finally start getting some clearer data.

Oh BTW just wanted to mention you can all use the backtesting ive made if you want to (even on your own model if you wish)

Only thing to remember with the UX (it was a difficult UX problem) is its all through the lens of the currently selected model, which will be the default one normally. 

Thanks,
Dan

Frist of all an apology if i’ve overposted so far, i can see why you’d think that. I’m new and learning. And don’t worry there will be no more of these agonising essays going forward. Just model results and hopefully discussion but…

I think I may have framed FiteQuant badly in earlier posts.

Maybe I focused too much on early results. Maybe it came across too “marketing”. If so, fair enough. I’m new to Reddit in general, and very new to what I’m now assuming is the current commonplace CLV/arbitrage-style sports betting modelling approach.

Honestly, my mind just doesn’t work that way.

I respect what people in this space have already accomplished. I know this is a non-trivial problem. I also know it probably looks like I don’t know what I’m doing because I don’t have the perfectly correct local vernacular yet.

But I am really disappointed that no one has tried to engage, even on an intellectual level beyond: “well, you are obviously bleeding time through subjective LLM inference.”

I know.

That is the very reason I created strict time-safe vs non-time-safe modes in the first place. It is also the reason I’m posting a timestamped public log of the default model rather than just showing retrospective screenshots.

I have extremely limited interest in odds or “picks”  as the main object here. What I think I’ve built is a credible time-safe modelling system and testing environment that happens to be on boxing right now.

I chose boxing because I have personal domain knowledge, which helps with verification; 

But also because it is a messy sport with poor public tooling; and because it has lots of repeated, consistent language around fighters and styles, which makes it a potentially interesting domain for structured LLM-assisted modelling. For these reasons I think if there’s going to be realistic edge in any sport with this approach, boxing is probably the most obvious example.

I’d define what i’m trying to do more like:

A modelling and testing environment where a potentially novel llm derived analysis system can be turned into something falsifiable, timestamped, and backtestable.

For me, “edge” isn’t the main goal. It’s a useful calibration point with reality 

I’m not selling anything right now. I literally have nothing for sale.  I’m genuinely trying to understand whether the idea is less interesting than I think, whether I’ve communicated it badly, whether I’m missing something obvious , or whether people are just assuming it’s a picks/AI-slop project before engaging with the actual system.

So, assuming predictions are timestamped and locked before results, and assuming strict time-safe vs non-time-safe separation exists:

What is the strongest objection beyond “LLM leakage exists” or “boxing is slow”?

I know both of those things. They are design constraints, not surprises.

If there’s a deeper flaw, I’d really really like to hear it.

Thanks, Dan

Latest timestamped predictions for the weekend upcoming (for the boxing model i'm logging here)…

https://preview.redd.it/ey82wza70zxg1.png?width=1485&format=png&auto=webp&s=f3bc4a52fcd4ec074afa1ca8889ea0e9d440029d

From a boxing perspective this is an exciting week or two coming up, lots of really big title fights, but from a modelling POV, not so much. Annoyingly the model only sees one value pick in the bouts that have made it through data quality checks so far.

I’ve found, that in boxing, smaller undercard bouts (that pass checks) often appear in the immediate run up to the fight, so I might still get a few more next week, but as for this weekend it seems a bit dull.

Still this does make sense as the model usually only finds value in these “smaller” fights. And tbh i’d be worried if it was picking really big title fights often.

I think with this weekend having a lot more in terms of “big fights” than usual, even relatively stacked undercards, there genuinely may not have been much value available.

That said, if the model continues to be this conservative, I may test a more aggressive variant separately. But I don’t think it makes sense to alter the logged model just because one week looks quiet, so for now I’m leaving it alone. 

https://preview.redd.it/bitbu62a0zxg1.png?width=1509&format=png&auto=webp&s=d2932821a49fb6b077795d4fc75169ccdd9f72ed

I thought I'd highlight this bout in particular as it’s a title bout and also an underdog pick. I’ll refrain from posting any direct links to the matchup data for this but it’s a pretty interesting example at this stage.

The model im using is “subjective stat” heavily weighted, and on this bout matchup factors are not at all a strong advantage either way, so this is a pretty pure example of a subjective stat scoring on a “big fight”, my guess is that it may well get this pick incorrect as my experience so far has shown that it struggles most often in exactly this situation (as expected). 

But as i say i don’t want to be too conservative. I never actually intended this model to be anything but demonstration of system behaviour, it was just the initial results seemed so promising that i thought it might warrant its own model log. So on that basis i’d say im flagging this as a potential bad pick, but not intending on making any edits to model config as a result.

Please let me know if anyone has any feelings on the edit vs do not edit issue for this model going forward.

Something interesting to try.

Separate but related thought on backtesting while the time-safe sample is still small: 

I feel like i may have been working under an incorrect assumption that people would recognise why well structured non time safe data, in sufficient quantity, can actually be quite useful and informative.

Particularly if you were awaiting verification for results of a particularly interesting model on slowly accumulating sports data.

 I can now see why it might be the case that someone could think of non time safe data as useless noise by definition, but im not sure thats strictly true.

I’d say you can infer very useful things if you have confidence the backtesting system you are using seems “correct” and seemingly able to see runs perform as expected given backtest options. 

You might be able to infer some potentially useful data on what model factors affect expected time bleed inflation the most, and you might even be able to get real value from that data one day. Particularly if you could then create and test your own model with custom weighting on the same factors.

I really don’t know where you could access such a thing easily apart from maybe on a weird boxing(for now) + modeling website type thingy.

No links again this week.

And also you don’t have to believe you can 100% trust something to find it interesting.

P.S. for the love of God, could someone please DM me and sanity-check that I’m not crazy / not very stupid? That would be genuinely really helpful. 

Another frustrating weekend from a data point of view. Once again for the second week in a row an entire fight card collapsed so I only ended up getting two more bouts. 

Things looking up for the next week or two though in terms of upcoming bout numbers.

Heres the latest model prediction result data screenshot

https://preview.redd.it/lbwumtrv8lxg1.png?width=1515&format=png&auto=webp&s=e106a8210066290371106e708989384a86f20cc9

The most interesting result this weekend was the model picking an underdog in a relatively “big fight” this has happened a few times, but usually it seems to find value in smaller mid size bouts that still pass data quality checks (as youd expect)

Still i was pleased with this pick, it wasnt crazy from a boxing perspective and although the model overall is selective i dont want it to be too conservative either, boxing fights take a while to happen.

Slightly sad that the accuracy streak came to an end, but it was expected (this is reality), and i’d much rather it did actually make picks like this, i can totally see the sense when i check the matchup data.

Ill refrain from posting any links, as i think i might have been a bit overeager with that so far. Sorry im new here and learning.

If anyone would like to reach out and discuss anything id be happy to.