u/Character_Pie_277

[model log boxing] 100 confirmed results now logged — 11.25% ROI 81.00% accuracy +11.25u flat-stake P/L

[model log boxing] 100 confirmed results now logged — 11.25% ROI 81.00% accuracy +11.25u flat-stake P/L

Here are the first 100 all model leans results for the fitequant default model:

In this strategy the model makes a prediction on basically all boxing winners and makes a 1u flat stake bet* each time, no matter the odds on offer. So even if a price is terrible… bet anyway.

*Please remember fitequant internally just uses one consistent book as a reference for market odds to take market variance out of the process as much as possible, with predictions made at opening odds and resolved on those odds.

100 confirmed all-leans bets
81 wins / 19 losses
+11.25u flat-stake profit
11.25% ROI

Average odds 1.6886

Below are the latest 3 results added this weekend.

https://preview.redd.it/1jy4z9vqcxjh1.png?width=1510&format=png&auto=webp&s=9fe64293c60783a4b651b615afe84dc411332a91

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

And the value picks only betting strategy results

In this strategy the model only bets if it sees value in the odds on offer by the market. Where the models win probability exceeds the implied volatility of the market odds of the fighter it thinks will win.

So exact same predictions, but you can think of this as “likes the fighter and likes the price”

100 confirmed value picks only results 

30 bets
17 wins / 13 losses
+6.93 u flat stake profit
23.11% ROI

Average odds 2.8666

https://preview.redd.it/ska2vxgscxjh1.png?width=1486&format=png&auto=webp&s=7cbca607f95acc9174ae25d9c6ce986dca93afc3

https://fitequant.com/results

So this week we hit 100 results exactly, and in the most modeling way possible, 2 bouts out of 4 were cancelled over the weekend including the value pick, and 3 totally forgettable no value massive favourite wins take us across the line.

There was one no value bout that took place during the week which i didn’t bother logging publicly, but in the interests of clarity, here it is.

https://fitequant.com/compare/946-michael-zerafa/1197-alejandro-ortiz?canonical_fight_id=26376

Forecast review

After 100 results i’m pleased by my early forecasts both turning out reasonably accurate with both model betting strategies ending up well within variance range of forecasts.

I always think it’s a bit of fun to try and forecast ROI on new models, but now we finally have a decent sample I’ll just talk about the data we actually have.

I'm delighted with the all leans strategy.

100 bets. Flat stake. No cherry-picking.  +11.25 unit profit. In 4 months.

The double digit ROI was highly stable across the whole run, and 0 -> 11.25 profit in 4 months is exactly what double digit ROI looks like with enough opportunities to get it down each week.

I’m actually pretty staggered that it has stayed at double digit ROI for so long, as with boxing there are soo many massive obvious favourites with terrible odds, where even a win hammers the ROI, actually the 100th result itself was a great example of this with Shields winning at 1.0286 (a measly 2 cent profit on $1 flat stake bet)

In terms of the value picks strategy, well it's been very frustrating recently as the boxing hasnt been great for a few weeks and just not much value on offer seemingly with very few value picks recently.

So with only 30 bets placed it might take a little while to get more clarity here, but its been wobbling around 20-35% for a while now and I guess somewhere around there now seems pretty reasonable, but lets see I suppose, although boxing is a bit slow, it is reliable in consistent weekly N.  

Key to my confidence here is the fact that average vs implied edge has been effectively static at an extraordinary 20% across the whole run of bets so far, with accuracy around 55-60% for that period.

Quick look forward to next week

https://preview.redd.it/fv4og3oucxjh1.png?width=1493&format=png&auto=webp&s=e8bacc6dfee9b48617392f101d934864f1a6e013

https://fitequant.com/upcoming

Thankfully after a period of pretty rubbish boxing the current upcoming slate is very active, with 6 bouts already upcoming for next weekend, including two value picks, i’d actually expect to get a fair few more results than this as most undercard bouts dont appear until the days leading up to the weekend itself.

As always if anyone has any questions or would like anything cleared up, please feel free to ask me.

Thanks, Dan

EDIT* Forgot to mention i've written up a more in deoth article on SSI (structured subjective inference), its aimed at a slightly more general audience, but for anyone whos open minded interested to learn more, you should be able to find that relatively easily with a google search, although do DM if anyone interested cant find it.

reddit.com
u/Character_Pie_277 — 3 days ago

[model log boxing] timestamped predictions, one value pick for this weekends fights + womens boxing data quality discussion

Quite a quiet week this week boxing wise, with a lot of women's boxing so not too many predictions and only one value pick actually clearing data quality standards…

Here’s this weekends predictions. 

https://preview.redd.it/4oigr3994djh1.png?width=1489&format=png&auto=webp&s=84f78da598d21a757b7644ed95c7a5303d65438a

https://fitequant.com/upcoming

The value pick this weekend is..

Danielle Perkins vs Olivia Curry

https://preview.redd.it/xwq4vhla4djh1.png?width=1505&format=png&auto=webp&s=785c5485cb572b304266126e80052c5620769165

https://preview.redd.it/8xoyx5ob4djh1.png?width=1482&format=png&auto=webp&s=b1a35a0bda49b42a5571a568ab64317d0637c330

https://fitequant.com/compare/1536-danielle-perkins/2358-olivia-curry?bout_id=322

This is really quite an unusual pick for the model, usually it selects underdogs or more close to even odds matchups as picks, and avoids betting on obvious favourites, I guess as you might expect from a model truly finding value.

But what ends up making this a value pick at 1.13 is the exceptional confidence the model has in its prediction here at 90.95% vs 88.24% implied for an indicated 2.71% edge at an expected 3.07% ROI (1u)

You should be able to observe the reasoning behind this from the screenshots and links above. 

Perkins is overall rated a far stronger fighter (which i guess is what the market also sees) , but crucially enjoys a large height reach advantage on top of a southpaw advantage and superior fighter rating. 

Yep, thats my old favourite height reach delta popping up again as a powerful matchup factor seemingly driving disagreement with the market odds.

In this case the implied edge is really quite small in comparison to the highly consistent 20% average diff vs implied edge advantage this model has enjoyed so far in the 97 results so far.

So far with womens boxing i’ve been 0/3 on value picks, but I really think that's just variance doing its thing. If you check the confidence value the LLM is assigning to SSI ratings in the subjective stats for women you should be able to observe they are pretty much in line with what you’d expect from male boxer ratings elsewhere in the DB.

Certainly to me, for a heavy favourite, these do seem like pretty good odds for boxing, I guess time will tell.

100 Results?

I actually thought twice about even doing a log post this week as only 3 results upcoming, but i do hope sub members can understand why i’m so keen on every prediction, especially every value pick, going down in public before the actual matchup takes place.  And N is a particularly hard thing to get cleanly each week with combat sport modelling

I really do hope to prove my work here over time amongst competent peers. So I really don’t want there to be any mysteriously *convenient* predictions that go unlogged.

But supposing all three confirm with winners that should take us on to 100 results so i’ll try and treat this weeks results log more as a general review on what i think i’ve learned over model 100 bets and 4 months of weekly posting, so far.

As always if anyone has any questions or would like anything cleared up, please just ask.

Thanks,
Dan

reddit.com
u/Character_Pie_277 — 6 days ago

[model log boxing] 97 confirmed results now logged — 80.41% accuracy +11.09u flat-stake P/L

Here are the current all model leans results for the fitequant default model:

In this strategy the model makes a prediction on basically all boxing winners and makes a 1u flat stake bet* each time, no matter the odds on offer. So even if a price is terrible… bet anyway.

*Please remember fitequant internally just uses one consistent bookmaker as a reference for market odds to take market variance out of the process as much as possible, with predictions made at opening odds and resolved on those odds.

97 confirmed all-leans bets
78 wins / 19 losses
+11.09u flat-stake profit
11.44% ROI

Average odds 1.7083

Below are the latest 5 results added this weekend.

https://preview.redd.it/rbxjtyrhljih1.png?width=1496&format=png&auto=webp&s=2adb12099de0554f98f1cf707ecb4408abace56d

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

And the value picks only betting strategy results

In this strategy the model only bets if it sees value in the odds on offer by the market. So think of this as “likes the fighter and likes the price”

97 confirmed value picks only results 

30 bets
17 wins / 13 losses
+6.93 u flat stake profit
23.11% ROI

Average odds 2.8666

https://preview.redd.it/xcca0t1jljih1.png?width=1483&format=png&auto=webp&s=163e8d354bcdd23b34e739b37f64e41adf5f7488

https://fitequant.com/results

So a pretty dull and forgettable about week modeling wise, one bout cancelled so stays pending for historical prediction data, unaffecting headline metrics, with both strategies taking one loss.

Bloody women's boxing! Metcalf (our one value pick this week) with an area code sized height reach advantage still manages to lose a UD in 8 rounds. I think i’ve had 3 value picks from women's boxing so far in 30 bets placed and all have lost. So annoying!

https://fitequant.com/compare/1206-shurretta-metcalf/12998-amanda-galle?canonical_fight_id=26142

I don’t think that there's any real reason to think this is anything other than variance doing its thing. The confidence values the llm attaches to the female SSI outputs don't look any different to mens boxing in general, and tbh in boxing i need all the time safe N i can get for my users in backesting anyway.

Oh well if there is actually a *problem* with womens boxing its such a small part of the data pipeline overall (with so few bouts clearing data quality checks) that I just dont think it will be a massive issue, and i’m totally happy with the way its currently implemented at the discretion of the data pipeline quality checks

Diagnostic/CLV metrics

https://preview.redd.it/q5bc4c8kljih1.png?width=1493&format=png&auto=webp&s=3600687490d7953f02e123ce6496882a08d63dea

As I was expecting to hit 100 results this week, i thought now we finally have a decent sample it might make sense to introduce some more diagnostic and market metrics for more advanced users.

Some of you here, particularly those with more ML background, will have much deeper experience with these metrics than I do, so I'd genuinely be interested in how you'd interpret them.

My read is…

Brier and log loss I've been checking every now and again for a while, basically just to see what they were, and I'm obviously pleased so far.

Calibration-wise, this is the first time I've actually calculated the expected error precisely, but it's basically where I thought it would be: a fairly conservative model that seems to see most boxing matchups as a lot closer than the market does.

CLV I've always thought was pretty irrelevant as fitequant aims to take market variance out of the process by resolving predictions on opening odds. So I was basically expecting noise here, although I'm not shocked to see it at around 2% given that the model has been so profitable overall. 

Hopefully you can see in the above screenshots that i’ve made bout by bout CLV data inspectable in the desktop ux on mouseover. I felt that was the right balance on ux legibility for users, as i do want this data available as a handy reference for those who might be interested.

That implied EV figure is actually technically accurate btw as the model does sometimes occasionally pick massive underdogs.

I've approached modelling as a computer science systems problem, so I look at these figures more as diagnostics of how this particular user model happens to behave. But as I say, I'd be fascinated to hear how others here interpret them.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 10 days ago

[model log boxing] 6 timestamped predictions, one value picks for this weekends fights + backtesting discussion

A rather lengthy post im afraid this week chaps... 

6 predictions so far this week for fitequant default model Including one value pick.

Here’s this weekends predictions. 

https://preview.redd.it/f0unpu19cyhh1.png?width=1482&format=png&auto=webp&s=c7e5e24161e57217b73973e5bab7d0ee5cbf3d8a

https://fitequant.com/upcoming

The value pick this weekend is..

Shurretta Metcalf vs Amanda Galle

https://fitequant.com/compare/1206-shurretta-metcalf/12998-amanda-galle?bout_id=259

https://preview.redd.it/avzxgtgacyhh1.png?width=1491&format=png&auto=webp&s=3ee6d1fa0ee759de888f4bb68b3753a71a863209

Sadly just the one value pick this weekend, but it is quite an interesting one. Metcalf is 41 so you’d think red flag, but her opponent is 37.

I know absolutely nothing about womens boxing… 

But one thing i do know about though is height reach delta as a factor in the fitequant matchup engine. And 31 cm is an absolutely huge advantage in boxing. Ive not seen anything remotely close to that in all the timesafe data so far. 

But remember public data like this is only ever any use at all because the underlying SSI actor abstractions are seemingly so strong, so although height reach delta does often seem to produce profitable disagreements with odds, it still needs to be very carefully weighted in the user model.

Which brings me on to an interesting subject…

Backtesting discussion

In the default user model height reach delta is weighted low overall, I really havent done that much backtesting since i started we started collecting time safe data around 4 months ago now, but now that we finally have a decent timesafe sample of almost 100 results, i thought it might be time to see if a weighting change on this apparently important factor could improve roi.

So i span off a few model clones as iterations of the default model with different weighting options on height reach delta. What i suspected was, moving this to a lower weighting may produce more consistent roi (as i thought it would drag the model into less massive underdog picks overall)

So here’s the backtest data for the time safe results we have so far with the default model vs the very low height reach delta iteration 

Default model

https://preview.redd.it/60pi0qaqcyhh1.png?width=1475&format=png&auto=webp&s=f30f3d75dcd6ed3f335bde7b5138572e26126dce

The ROI is slightly different to the results page for the default model as backtesting uses closing odds (instead of prediction odds) for consistency across time safe and historical data.

Very Low Height-Reach Delta model

https://preview.redd.it/y1rmccptcyhh1.png?width=1481&format=png&auto=webp&s=d208c5ea09b56afcbc6b2ff4774c7e8942e966ad

For those concerned about the N here, I also checked this on the historical data across hundreds of bouts in various time windows, and it did confirm my findings that this iteration is indeed overall stronger and more consistent at producing +roi and profit than the default model

You should hopefully pretty easily be able to see the advantage the vl hr model has over the default on accuracy, roi and profit on slightly fewer signals overall. 

This supports my theory that reducing weighting slightly on height reach delta can produce +ROI  over whatever the default ends up having. 

That said, im obviously delighted by the default model results so far, itd be nuts to change any weightings now, so ill be tracking this model internally over the next 100 timesafe results before i consider any change at all.

However this new model does seem very promising to me, so if anyone would like to repeat my experiment you can do so by creating a clone of the default, setting height reach to very low and saving the model. 

That should produce a new set of predictions for you for the coming 28 days, and you should be able to repeat my backtesting experiments.  The differences in the predictions between this and the default are really quite impactful. 

In particular the upcoming Itauma vs Hrgovic bout is a very interesting comparison between these two models.

https://fitequant.com/models

Advanced metrics

I had actually been expecting to clear 100 results this weekend, so i thought it might be time to introduce some diagnostic metrics for more advanced users perhaps coming from a more traditional ML background.

I’d had my eye on a few of these for a while but it just felt like a bit of a nonsense before we even had 100 results. Well we didnt quite get 100 results this week but i’ve added the following to the results page as a collapsible dropdown anyway.

https://preview.redd.it/hjj3tg1jdyhh1.png?width=1505&format=png&auto=webp&s=70dd0b6149711f7f7d1710d117b8e9108df52704

https://fitequant.com/results

 I’ll wait until the results post on monday to discuss about these some more, but I just thought it might be quite interesting to some sub users.

Incidentally if anyone would like to see this data for the vl height reach delta iteration that should all be available to you in backtesting after model creation.

As always if anyone has any questions or would like anything cleared up, please just ask.

Thanks,
Dan

reddit.com
u/Character_Pie_277 — 13 days ago

[model log boxing] 92 confirmed results now logged — 80.43% accuracy +11.44u flat-stake P/L

Here are the current all model leans results for the fitequant default model:

In this strategy the model makes a prediction on basically all boxing winners for months and makes a 1u flat stake bet each time, no matter the odds on offer. So even if a price is terrible… bet anyway.

92 confirmed all-leans bets
84 wins / 18 losses
+11.44u flat-stake profit
12.44% ROI

Average odds 1.7242

Below are the latest 8 results added this weekend.

https://preview.redd.it/ikq28v63q5hh1.png?width=1401&format=png&auto=webp&s=a559e9d9027b9d75c0ac1b3814fbdabe01070202

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

And the value picks only betting strategy results

In this strategy the model only bets if it sees value in the odds on offer by the market. So think of this as “likes the fighter and likes the price”

92 confirmed value picks only results 

29 bets
17 wins / 12 losses
+7.93 u flat stake profit
27.36% ROI

Average odds 2.88817

https://preview.redd.it/056zt264q5hh1.png?width=1397&format=png&auto=webp&s=07800225fa806ece3e4c1bedf29a95f6521e36f0

https://fitequant.com/results

Another good weekend for the model with only one bet placed loosing for the second week in a row, for 7 out 8 bets placed winning this weekend.

The loosing bet was a 6.8 massive underdog value pick, and *annoyingly* Randall lost on a split decision after a great fight, so close to a massive 5.8 profit but no cigar.

Forecast update

I’m pretty happy with my forecasts so far on this log, with both model strategies still very much within expected variance range of my previous forecasts after 92 results now.

I think my approx 13.5% ROI forecast for all leans looks coherent after 92 bets placed now.

In terms of value only well its still only at 29 bets so difficult to say, although given how well the all leans strategy performs despite the betting on obvious massive favourite on terrible odds problem, where even a win hammers the roi … you’d think that if its truly selecting value as a subset of those picks then ROI may well be considerably higher than 13.5%

Its doesnt exactly make sense updating forecasts after every 2-3 bets each week but as we are nearly at 100 results i think perhaps next week (fingers crossed) it might be a good time to review expectations on value only ROI.

How messy is boxing data exactly?

Just to give you guys some idea of the sh*t i deal with running a boxing data pipeline. I ended up having to manually confirm results this week as Golden Boy promotions have still not released the official results for their undercard.

The pipeline is conservative on the scheduler and wont confirm a result with winner until there is publicly available definitive answer from an official source. 

I guess Oscar forgot that when you’re a boxing promoter that necessitates doing things such as releasing basic information relevant to your fighters. Seriously if you guys ever thought boxing promoters dont really earn their crust… try running a boxing data pipeline. You have no idea!

As always if anyone has any questions please just ask.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 17 days ago

[model log boxing] 8 timestamped predictions, 3 value picks for this weekends fights.

8 predictions so far this week for fitequant default model Including three value picks.

Here’s this weekends predictions. 

https://preview.redd.it/ttek60w68kgh1.png?width=1386&format=png&auto=webp&s=5268867f109fdab0f92794462bb2b68194a5da48

https://fitequant.com/upcoming?sort=date&table_mode=full&prediction_strategy=value_only&date_from=&date_to=2026-08-03

And here’s a more detailed look at the active prediction data for the current 28 day upcoming bouts slate.

https://preview.redd.it/btjgis588kgh1.png?width=1398&format=png&auto=webp&s=e25e091602327dd0e6fa624f589d818eac5dc8cf

https://fitequant.com/upcoming

The value picks this weekend are..

Gary Cully vs Lee Reeves

https://fitequant.com/compare/1575-gary-cully/13209-lee-reeves?bout_id=263

In this bout Cully and Reeves are rated very closely in SSI fighter rating but Cully enjoys a decisive +22cm  Height Reach delta advantage (which is HUGE in boxing)  in matchup factors, to support a high confidence 73% confidence vs 58% implied expected edge advantage.

Raúl Curiel vs Quinton Randall

https://fitequant.com/compare/419-raul-curiel/1282-quinton-randall?bout_id=289

Here Curiel actually has a higher fighter ranking, mainly due to a much higher punching power SSI stat, where Randall is noticeably weak. But Randall again enjoys a large height reach advantage, 

The default user model rewards defensive fighters with very high weighting on fight IQ and Defence, so Randalls larger frame advantage just pushes the model into a low confidence 51% massive underdog 6.2 value pick.

William Zepeda vs Lamont Roach

https://fitequant.com/compare/452-william-zepeda/479-lamont-roach?bout_id=245

https://preview.redd.it/pqj0sdsg8kgh1.png?width=1404&format=png&auto=webp&s=136faf18252df0f243a3cc39bbb4d4eb69e6213c

Probably the biggest fight of the weekend, its a relatively close one for boxing according to the odds, but the model favours Zepeda at 2.64.

SSI wise this is an interesting one with Zepeda having quite an extreme profile focused on punching power and pace/pressure.

By contrast Roach has better defensive stats, normally this model prefers defensive fighters but with two very close overall fighter ratings (69.92 vs 70.64) Zepeda wins out at 58% confidence vs 38% implied edge, according to the model due to Roach dropping back down to lightweight and also once again a larger frame (height reach) matchup factors.

Respect the reach

This weekend i think really demonstrated how important carefully weighted height reach delta is in identifying disagreements with the markets.

But its also only as strong as the underlying structured subjective inference ratings representing the fighter as actor in modeling. 

I think this usefully demonstrates one reason why i was so keen to keep fighters that dont have an easily confirmed height or reach out of the data pipeline. 

It looks like its true in data quality just as much as boxing… respect the reach.

As always if anyone has any questions or would like anything cleared up, please just ask.

Thanks,
Dan

reddit.com
u/Character_Pie_277 — 20 days ago

[model log boxing] 84 confirmed all leans bets results now logged — 79.76% accuracy +9.67u flat-stake P/L

Hi guys, good weekend for the model with 8/9 bets placed winning this weekend.

Here are the current all model leans results for the fitequant default model:

In this strategy we force the model to make a prediction on basically all boxing for months and make a 1u flat stake bet each time, no matter the odds on offer. So even if a price is terrible… bet anyway.

84 confirmed all-leans bets
67 wins / 17 losses
+9.67u flat-stake profit
11.52% ROI

Average odds 1.6983

Below are the latest 9 results added this weekend.

https://preview.redd.it/nvwz92yegrfh1.png?width=1388&format=png&auto=webp&s=e92e039129548c129723d6527c7612640c429c6b

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

And the value picks only betting strategy results

In this strategy we maintain exactly the same predictions for each bout, but the model only bets if it sees value in the odds on offer by the market. So think of this as “likes the fighter and likes the price”

84 confirmed value picks only results 

26 bets
15 wins / 11 losses
+6.57 u flat stake profit
25.31% ROI

Average odds 2.8083

https://preview.redd.it/ppw66znggrfh1.png?width=1416&format=png&auto=webp&s=691be5c05f57ba6f1316687829166ada45dbc004

https://fitequant.com/results

9 out of 11 results confirmed successfully this week with two bouts cancelled and so left as pending predictions unaffecting headline metrics in the prediction result data.

So the Spence value pick lost, oh well, no use in a model that never makes bets. 

But i do think Spence was a tough one for a model that relies on structured subjective inference (SSI) for fighter as modeling actor abstractions, primarily because he was a great fighter that has been inactive for years, so its tough for anyone to know, including an LLM, exactly how to rate him now for any one subjective stat. 

I’m really not displeased with the pick at all. Given his height reach and southpaw advantages on top of subjective stats that i think did make sense, and given his last loss to Crawford wasnt anything to be ashamed of… i can totally see where the 74% confidence and hence value pick comes from. 

He just looked shot. Perhaps something to explore around increasing recent activity weighting in objective stats on an iteration/clone of the default model? 

I know.. Its only one result. And I haven’t done any actual backtesting on this as i’m busy working on MMA modeling right now, but it might be interesting to take a look at? 

Forecast update

So nothing changes again this week. 

For the all leans im basically staying unchanged forecasting approx 13.5% ROI

As we begin to approach 100 bets placed i’m not really sure why anyone would now expect this to change much anytime soon? Importantly here avg diff vs edge, avg odds, accuracy and even ROI itself have now been very stable on this strategy for literally months now.

As a sub member rightly pointed out on a results post of mine recently, what’s interesting isnt necessarily the accuracy. You can get 80% accuracy picking favourites in boxing (and see the relative underperformance of the Model confidence >= 60% strategy, with even higher accuracy, above)

What is unusual here is approx 80% accuracy persisting alongside double-digit flat-stake ROI over virtually the whole eligible boxing stream, over months. 

For value picks, obviously far fewer bets places so we wont know exactly for a while,  but i’m continuing to be bold forecasting approx 40% ROI

As always if anyone has any questions or would like anything cleared up, then please just ask.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 24 days ago

[model log boxing] 10 timestamped predictions, two value pick for this weekends fights.

Hello all, 10 predictions so far this week for fitequant default model Including two value picks for this weekends boxing.

Here’s this weekends predictions. 

https://preview.redd.it/8wnd2bap86fh1.png?width=1405&format=png&auto=webp&s=4cdbf1a6c228a222bb3bd3fdc11d2e704c559dec

https://fitequant.com/upcoming

And the value picks this weekend are...

Errol Spence vs Timofei Tszyu

https://fitequant.com/compare/612-errol-spence/904-timofei-tszyu?bout_id=253

Arnold Gonzalez vs Emiliano Moreno

https://fitequant.com/compare/13719-arnold-gonzalez/13725-emiliano-moreno?bout_id=268

So a relatively exciting weekend in prospect modeling wise, and thankfully lots of model activity indicated in the current 28 day upcoming week window.

Two non-predictions

Rather than the picks themselves this week, I wanted to briefly highlight two non-predictions. As i think its a pretty neat demonstration of what i’ve tried to do with the data pipeline.

Anthony Joshua vs Kristian Prenga

https://fitequant.com/compare/283-anthony-joshua/12465-kristian-prenga?fighter_a_profile_source=default&fighter_b_profile_source=default

In the Joshua fight this weekend, his opponent just fails on data quality thresholds to produce an official prediction, but the bout is still being processed and assuming it confirms as a result with winner will be another N for me to use on non strict backtesting.

Here the custom matchup is still pretty useful as the fighters are freshly rated with SSI (even if bout rejected by data quality) so you can get a pretty good indication of what the model would actually predict.

Tyson Fury vs Mariusz Wach

https://fitequant.com/compare/908-tyson-fury/1811-mariusz-wach?fighter_a_profile_source=default&fighter_b_profile_source=default&matchup_division_choice=auto

The Fury bout is pretty different, here there just isnt enough publicly available info for SSI to rate the opponent at all for subjective stats etc, so this is a firm rejection, no fighter rating, and no N in non strict backtesting.

Basically this is the data pipelines way of saying sh*t opponents.

As always if anyone has any questions or would like anything cleared up, please just ask.

Thanks,
Dan

reddit.com
u/Character_Pie_277 — 27 days ago

[model log boxing] 75 confirmed results now logged — 78.67% accuracy +8.70u flat-stake P/L

Here are the current all model leans results for the fitequant default model:

Think of this model strategy as basically forcing the model to bet on all boxing over months, forcing the model to pick a fighter to win each time and making a flat stake bet each time as a result. 

75 confirmed all-leans bets
59 wins / 16 losses
+8.7044u flat-stake profit
11.61% ROI

Average odds 1.746

Below are the latest 7 results added this weekend.

https://preview.redd.it/dls57zbvtdeh1.png?width=1414&format=png&auto=webp&s=a1beed040be3cfdd2d1864c6c160f3df2ab64239

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

And the value picks only betting strategy results, think of this where the model likes the fighter and likes the price.

75 confirmed results 

24 bets
14 wins / 6 losses
+7.06u flat stake profit
29.46% ROI

Average odds 2.9071

https://preview.redd.it/obc80bnwtdeh1.png?width=1386&format=png&auto=webp&s=4e4579d4cf687a97fec8294f8c2c0956ac871910

https://fitequant.com/results

So a pretty rough weekend for the model across admittedly only a 7 total sample and a grand total of 2 value picks in the end.

(there was an extra value pick make it though the data pipeline after the last predictions post)

Two awkward looking losses for the value picks only strategy, but as both were indicated as 50% confidence coin flips by the model anyway, its not exactly shocking.

Still at 75 results i’m obviously more than happy so far with his models performance, remember i really havent changed any weightings here during this entire process.

Forecast update

I don’t know about anyone else, but I just don’t see anything in this latest data to make me revise my estimates of 13-14% roi for all model leans, and approx 40% for value picks only

Key to this is my observations on the overall stability of accuracy, av diff vs implied edge and average odds.

Hopefully by now almost anyone can easily see the incredible seeming stability in these figures over many months now of me carefully posting this log via the regular results data screenshots.

Ill leave the reader to infer for themselves what this may mean regarding longer term roi expectations.

Quick look ahead

I think what i’m saying around the stability of key metrics is usefully demonstrated by the relatively active upcoming slate for the next 28 days.

Id actually expect a lot more results that this during this period, as undercards opening odds appear, but it certainly looks exciting with lots of model activity already indicated.

https://preview.redd.it/4xhi3p7ytdeh1.png?width=1392&format=png&auto=webp&s=2f5998703cdd5d961cc305c28d236d851331353a

https://fitequant.com/upcoming 

As always if anyone has any questions or would like anything cleared up, then please just ask.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 1 month ago

[model log boxing] 7 timestamped predictions, one value pick for this weekends fights.

Phew! That damn soccer world cup is nearly over, and at last the world can get back to what we all know is actually truly important… fitequant default model predictions.

Thankfully boxing has seemingly sprung into life again with a very active upcoming slate overall, even if this weekend specifically may look a little dull with only one value pick.

Here’s this weekends predictions. 

https://preview.redd.it/7i62qaw9fsdh1.png?width=1392&format=png&auto=webp&s=92f8dbd84770b8c60cd3b8b7b4b41c97a3a41d2f

https://fitequant.com/upcoming

And the one value pick for this weekend is..

https://preview.redd.it/ehpd027bfsdh1.png?width=1385&format=png&auto=webp&s=14ef776631868428be9ae25c0df89a0b943179d2

Aarón Alameda vs Albert Gonzalez

https://fitequant.com/compare/3476-aaron-alameda/13080-albert-gonzalez?bout_id=247

At 8.0 odds this is a very unusual value pick for the model. Usually in heavily one sided massive favourite/underdog bouts the model almost always leans towards the favourite, but identifies no value in the price.

Of course at those odds i’d just expect to lose this pick and pickup up a likely -1.0 loss

But given the track record of the model with an approx 80% accuracy overall, and approx 60% accuracy on value picks…  i’m not so sure its total nonsense

Overall the model has appeared well calibrated on prediction confidence historically, and although i obviously don’t think this bout actually is a 50/50 coin flip as indicated by the 50.77% win probability, i’m pretty sure there might well be some value here.

Oh well i guess it will be exciting watching Aarón Alameda vs Albert Gonzalez (whoever they are) and seeing what happens. Of course now i’ve given him the kiss of death by highlighting this bout in this log, I should probably apologise to Alameda for no doubt ruining his career.

As always if anyone has any questions or would like anything cleared up, please just ask.

Thanks,
Dan

reddit.com
u/Character_Pie_277 — 1 month ago

Testing out a new model - 50 confirmed timesafe results. Accuracy 68.75% - P/L 9.29 - avg odds 2.93

Hello everyone,

Most likely looking like there won’t be a model log at all this week as due to the world cup entering the later rounds, this weekend in particular is looking very sparse boxing wise.

But I do have some interesting early data on another model that i’ve been playing around with that I thought might be interesting for the sub.

High ai confidence model*

*please not ai does not make any picks in fitequant, ai is purely used to rate fighters for ssi and persist an associated confidence score

https://fitequant.com/results?model_id=17

https://preview.redd.it/ydyx7kkuzsbh1.png?width=1388&format=png&auto=webp&s=f1885b90b3ad9c46d7bb9d3a25ac433cd6e45fdf

https://preview.redd.it/wkglm31c0tbh1.png?width=1395&format=png&auto=webp&s=af7602b3b3959454ce92bde90d34a76b4032b3d4

https://fitequant.com/models?model_id=17

This is a derivation clone of the default model that i’ve been logging on here carefully for months, with a few important weighting changes.

In particular what i was testing here when I created it a while back, was whether increasing the AI confidence multiplier factor would produce any extra +ev. 

Initial backtesting was pretty inconclusive but as all structured subjective inference (ssi) fighter ratings receive an associated confidence score via the lllm, the ai confidence multiplier is a very important compound factor

(for example see “subjective stats” “confidence” value in fighter profiles https://fitequant.com/fighters/277-canelo-alvarez?model_id=17)

Obviously “only 16 bets” as opposed to the default models 22… so its important to bear that in mind here. But crucially i’m seeing this model outperform the default one in all betting strategies on strict +roi grounds, including the critical 15% roi on the all model leans.

This model may be doing something valuable by using AI confidence as a noise suppressor. It seems to reduce the number of positive-diff bets to cases where the subjective layer is more coherent, so the model is less often generating cheap fake value from low-confidence SSI uncertainty. 

High AI confidence multiplier + value-only selection may produce +ev in strict roi terms better than current default.

I had a prior suspicion that AI-confidence weighting might be a major ROI lever. Now I have a live forward sample that makes that suspicion worth testing properly, plus enough non-strict time-safe coverage to see whether the effect shape appears outside the strict model log.

I haven’t really done much backtesting around this yet as this data is basically a new discovery. Raising AI-confidence weighting appears to move the model toward a more selective, higher-conviction value profile. The interesting question is not whether this is universally better, but where it sits on the ROI/opportunity frontier 

I think this looks like a really promising model to perhaps form the basis of some more in depth research. I’m obviously not going to change any weightings on the default model <- that would be nuts, as this one is even more selective and may make for a very boring log even if it is strictly higher expected roi than the default. 

So i’m not actually going to log this model going forwards at all. But I think it might prove an excellent clone option for users doing their own modeling research

A quick note the upcoming bouts page for this model is very interesting with two active value picks, including one massive underdog that the default model passes on.

https://fitequant.com/upcoming?model_id=17

Raymond Muratalla vs Robson Conceição

https://fitequant.com/compare/455-raymond-muratalla/476-robson-conceicao?bout_id=244&model_id=17

This bout is very close indicated approx 50% pick (across the two models) with the high ai confidence model leaning to the underdog due to Conceicao having higher confidence scores associated with his ratings.

Normally i’d just expect this to be an error by the model at 6.5 odds and expect to lose easily. Obviously i’m still expecting this pick to lose overall, but in strict value terms i dunno, it would be easier to dismiss this pick as nonsense if the model hadn’t proved so highly selective and profitable previously.

As always if anyone would like anything cleared up or has any questions please just ask.

Thanks,
Dan

reddit.com
u/Character_Pie_277 — 1 month ago

Let’s try this again, r/sportsanalytics: I built a time-safe sports modelling environment, not another picks post

Given the recent thread about this sub being flooded with “my model found a 5% edge” posts, I thought I’d try posting here again with hopefully something of a bit more value for the sub.

A few months ago I posted about a sports modelling project I’d built and it was basically dismissed as AI slop, so I stopped posting here and moved the technical/log side elsewhere.

Since then I’ve kept building it and logging it publicly.

The thing I’m actually interested in discussing here is not the picks or the ROI. Those are public and easy enough to inspect if anyone cares. What I think may be more interesting for this sub is the environment itself.

I built a time-safe boxing modelling/backtesting environment... Currently on boxing.

Please note *I built/developed it* . Yes of course i‘ve used AI (mostly to create a novel dataset) but I built the modeling and backtesting environment myself, AI doesnt make any picks at all, this isnt how this works.

fitequant.com

For more info on this there is an publicly easily available fitequant FAQ that may answer some questions. 

I’m also explicitly deciding not to even talk about results here. So if anyone is wondering “does it actually work” i’d imagine if you searched something like “fitequant model log boxing” you should be able to find a timestamped predictions vs results public log on basically all boxing over months, with all picks and losses carefully logged.

Fighter ratings

The main reason i decided to choose boxing was that i’m approaching sports modeling from a computer science systems background. And i wanted my sports analytics system to actually work. So i figured boxing with terrible public tooling and stats might actually represent an edge opportunity. That’s the last time i’ll mention edge or roi in this post, but if anyone is wondering... thats why boxing.

But this does present the problem? How to rate a fighter. Of course an ELO like approach might work, but then everyone else can do that to. 

So I employed something i call “structured subjective inference”, using LLM’s to rate fighters to provide a rich abstraction of a fighter as modeling "actor". In fitequant terms this is basically what i call “subjective stats” on a fighter profile...

https://preview.redd.it/byzp5up3nlbh1.png?width=1386&format=png&auto=webp&s=9c3041143357afcb7098f9a733176378dbea7e7e

https://fitequant.com/fighters/908-tyson-fury

Matchup interaction layer

Even the best fighter as modeling actor abstraction is only ever likely to produce agreement with the odds over time. 

But if you layer other relevant factors on top of the fighter you can hopefully achieve profitable active disagreements with odds.

In fitequant terms these are called “matchup factors” but I guess any ML guys could think of them as features.

What i’ve found is that the interactions between these various matchup factors (height reach delta, weightclass, relative stance advantage, style) is what actually decides the outcome of the prediction in closer fights (where subjective stats between the fighters are similar )

Any user can use the matchup function to run a matchup for any two fighters. For example

Ryan Garcia vs Conor Benn

https://fitequant.com/compare/427-ryan-garcia/853-conor-benn?fighter_a_profile_source=default&fighter_b_profile_source=default

However all upcoming bouts with odds within a 28 day rolling window are marked upcoming by the system with a locked prediction. In practice this means with boxing most fights often appear in the days leading up to the bout itself, especially non-title bouts.

https://fitequant.com/upcoming

In fitequant odds are essentially irrelevant, apart from as a calibration point. They are effecitvely ingested at odds opening using just one consistent bookmaker 

Fitequant isnt really about CLV arbitrage or anything. It doesnt even try to shop around for odds. The goal is literally to basically price sports outcomes better than the markets. I realise that sounds like a mad ambition but thats literally what I tried to do.

User models + backtesting

https://preview.redd.it/dfrst3r4nlbh1.png?width=1399&format=png&auto=webp&s=2649cc44a0bd5d7625ac2f2e82001c536e1c39db

https://fitequant.com/models

There is a default fitequant model (the one i have been logging results for publicly) available to all users, its what forms the basis of the UX unless you create your own user models.

All user models start from the default fitequant model config above^. So everything is transparent on model weightings. Any user can clone/ create a model with fully custom weightings. 

Overall the model config UX may look simple, but thats by design, I think you may find it more powerful than you might expect as even small factor weighting changes can have very large consequences around model picks.

All bouts marked upcoming by the system receive a prediction automatically on odds opening and when a confirmed result is available they appear in the results page.

All bouts that confirm with winner + closing odds available are persisted as a strict time safe result in backtesting. 

There are now 68 strict time safe results available in backtesting, with more being added by the data pipeline automatically each week, plus much deeper 500+ non strict time safe historical bout coverage for anyone doing their own modeling research.

I’d actually found that the historical non strict coverage was a lot more useful than first anticipated in terms of inferring weighting decisions when modeling.

So basically the idea is a user creates a model, maybe does a bit of backtesting and changes a few weightings. Then weeks/months later you should have literally dozens of fully time safe results for your model, without you even having to do anything but spend a few mins configing it just once.

https://fitequant.com/results

https://fitequant.com/testing

Happy to respond if anyone has any questions or would like something cleared up. 

I’m aware theres a hell of a lot of new seeming stuff here, so if I can clarify anything feel free to reach out.

Thanks,
Dan

reddit.com
u/Character_Pie_277 — 2 months ago

[model log boxing] 68 confirmed all leans bets results now logged — 79.41% accuracy +9.47u flat-stake P/L

Here are the current all model leans results for the fitequant default model:

Think of this model strategy as basically forcing the model to bet on all boxing over months, forcing the model to pick a fighter to win each time and making a flat stake bet each time as a result. 

68 confirmed all-leans bets
54 wins / 14 losses
+9.477u flat-stake profit
13.94% ROI

Average odds 1.67

Below are the latest 4 results added this weekend (all wins for the model).

https://preview.redd.it/iu29gqv5cgbh1.png?width=1396&format=png&auto=webp&s=4474b396681b504141aeca3ab2683a2d6a0f1c01

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

And the value picks only betting strategy result

In this strategy the model only bets if it likes the fighter and also likes the price, in boxing this usually comes down to closer fights, where a lower but still very consistant overall accuracy of approx 60% results in much higher ROI as you'd expect

68 confirmed results 

22 bets
14 wins / 8 losses
+9.06u flat stake profit
41.22% ROI

Average odds 2.6881

https://preview.redd.it/tcfesxf7cgbh1.png?width=1389&format=png&auto=webp&s=b9931e103a58faedc37f4fb8e0b56a78c2994fb3

https://fitequant.com/results

I was about to apologise for their only being four results this week, due to boxing understandably being very slow around the world cup.

But i’ve also just seen the genius’s on this sub apparently think some soccer scores on a website without even any actual modeling results + an extremely basic UI, without even an english language option, is somehow impressive after a whole months dev work. 

So it’s increasingly apparent that i’m largely wasting my breath here.

Anyway I guess I shall go on madly talking to the void, who knows one day someone might even think this stuff is somehow interesting in some way.

What i thought i’d focus on this week is the >60% confidence row in the above screenshots.

This betting strategy forces a bet when the model is over 60% confident in a prediction. So again this is a strategy where odds value is essentially irrelevant.

Model confidence ≥ 60%
44 bets
81.82% accuracy
+1.1805u profit
+2.68% ROI
Avg diff vs implied: -8.40%

This data is a useful reminder that accuracy and +ev are not the same thing. 

The model’s ≥60% confidence picks are now 36–8, an 81.82% hit rate over 44 confirmed bets, but only +2.68% ROI.  

That is not because the model is weak — it is because many high-confidence boxing picks are already priced like high-confidence outcomes.

The value-only strategy remains much smaller, but much sharper: 22 bets, 63.64% accuracy, +41.22% ROI.

In boxing, apparently staggering accuracy can still produce pretty marginal profit if the market already agrees. These results are quite amusing to me given the amount of betting tools that seem to be sold on the basis of incredible seeming accuracy. Who cares if it’s correct if the market already see that?

As always if anyone has any questions or would like anything cleared up, then please just ask.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 2 months ago

[model log boxing] timestamped predictions, this weekends fights + backtesting update

Unfortunately with a busy world cup weekend it looks like boxing promoters are understandably reluctant to put on any big profile fights, so only a meagre four predictions make it through the pipeline so far this week, i guess we will just have to be patient.

I’ll try and do my best to make this weeks log a bit more “in depth” than usual so that this just doesn't descend into a boring picks post.

Here’s this weekends predictions. 

https://preview.redd.it/1bh5pbeat1bh1.png?width=1388&format=png&auto=webp&s=602ce0292d0eae794c49cea78d5d0ac829a0c8e8

https://fitequant.com/upcoming

Out of this week’s predictions this seems like an interesting bout.

https://fitequant.com/compare/12498-tsubasa-narai/12504-yamato-hata?bout_id=242

Tsubasa Narai vs Yamato Hata

FiteQuant likes Hata, but the market likes him a bit more, so the interesting thing is not the pick — it’s why the model refuses value.

The first interesting thing is that the raw fighter scores are basically level: Narai 64.90 / rank #229 and Hata 65.16 / rank #214, both with roughly Medium fighter confidence around 0.50. Yet the locked model still gets to Hata 61.58%. That means this is not being driven by a crude structured subjective inference(ssi) fighter rating effect; the separation is coming from the interaction layer.

The market is also making this interesting. Hata at 1.50 implies 66.67%, while FiteQuant has him at 61.58%, hence the visible -7.64% ROI / -5.09% edge / No Value result. But after removing the overround from the 2.63 / 1.50 prices, the market is roughly 63.7% Hata / 36.3% Narai, so FiteQuant is not wildly disagreeing with the market; it is saying “Hata, but not quite at that price.”

There is also a subtle guardrail spotlight here: Narai’s implied probability at 2.63 is 38.02%, while FiteQuant’s inverse Hata probability gives Narai about 38.42%. Purely mechanically, that is a tiny theoretical underdog twitch, but the “strict pick” rule avoids calling that value because the model still expects Narai to lose outright. This is a very good example of why the strict “value only when the model expects the fighter to win” rule exists

Without it, the system could start surfacing thin, ugly underdog pseudo-value in exactly the kind of fight where boxing variance tempts over-interpretation.

The matchup itself is interesting, because they are very closely ranked fighters on pure ssi “subjective stats” with very similar fighter scores for the model.

But the compound “matchup factors” make the real difference here with multiple interesting factors at play. 

Hata gets a medium height/reach advantage and a medium stance-interaction advantage as southpaw versus orthodox, while Narai gets the medium style-interaction advantage as boxer-puncher versus power slugger. 

Increasingly im thinking that compound factors (sorry ML guys i refuse to call them features) are whats important in systemized modeling/fitequant. SSI can drive a rich abstraction as a fighter as actor, but then i guess an ELO approach could get something not a million miles away.

But in systemized modeling its actually the matchup factors that drive the disagreement with odds that seemingly produces consistent reliable +ev

Backtesting coverage update

It occurred to me that because my data pipeline rates fighters with ssi even if their canonical bout doesnt pass strict data quality checks, it might be possible to get more time safe result data even if a few fighters are missing a “height” or “reach” or other critical datapoints.

Currently my timesafe data pipeline strictly rejects bouts where even one fighter doesnt have a key datapoint. In boxing this happens more than you’d think.

For example there is a fight this weekend between boxers called Erik Hanley and Ibrahim Mason, fitequant has actually freshly rated these fighters and you can view a custom matchup prediction for that bout as follows (the model leans Mason)

https://fitequant.com/compare/12429-erik-hanley/12433-ibrahim-mason?fighter_a_profile_source=default&fighter_b_profile_source=default

So if you use a resource like proboxingodds (to check for upcoming fights with odds) or equivalent you can often get pseudo-predictions for bouts even if they arent strictly marked upcoming by the system 

Anyway the result of all this was me realizing I probably had a bunch of effectively “timesafe results” just sitting there, even if it was lower quality data.

So i’ve literally just implemented this in backtesting in “non strict time safe mode” where pleasingly the bout coverage is now up to 86 bouts, and I expect it to keep growing from here. 
I did do some quick backtesting and admittedly on only a 50 sample with 86 results with a batch test of 10 i got the following data with the default model.

https://preview.redd.it/gjkairjbt1bh1.png?width=1398&format=png&auto=webp&s=70a53f173bacc5c433c60bd3b399b20c97f35177

https://fitequant.com/testing

Obviously this data should be treated with caution, because its explicitly non strict. But seeing as the relevant factor is just nulled when a reach or whatever is missing, i think it does point to the fact that fitequant may be usefully used for bouts that arent strictly marked upcoming by the system.

As i say i’ve literally just implemented this, and havent really dont much experimenting yet, but i don’t mind saying i’m excited and pleased that this is now available for user research.

As always if anyone has any questions feel free to reach out.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 2 months ago

[model log boxing] 64 confirmed results now logged — 78.13% accuracy +8.57u flat-stake P/L

Here are the current all model leans results for the fitequant default model:

64 confirmed all-leans bets
50 wins / 14 losses
+8.57u flat-stake profit
13.40% ROI

Average odds 1.70

Below are the latest 10 results added this weekend.

https://preview.redd.it/72kt46j2n7ah1.png?width=1393&format=png&auto=webp&s=ab35d01ec287fe1a2972b4776cde207a6f5899de

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

And the value picks only betting strategy data…

64 confirmed results 

22 confirmed strategy bets
14 wins / 8 losses
+9.06u flat stake profit
41.22% ROI

Average odds 2.6881

https://preview.redd.it/04z8bx94n7ah1.png?width=1390&format=png&auto=webp&s=bae5d3c2573c91f49be43f64ddb27140caa2e673

https://fitequant.com/results

Decent weekend for the model this week, with all 10 bouts confirming successfully (no draws/cancellations)

The value picks only strategy is now more profitable than the entire 64-fight all-leans set, while taking only about a third of the bets. 

 Forecast update

My earlier 20% all-leans ROI forecast on all model leans was based largely on ranked-fighter/expected time-bleed accounted for backtesting, where the fight population was less variable and odds were less distorted by obvious massive favourites. 

Across broader real boxing coverage, approx 13-14% may be a more realistic all-leans expectation. The value-only forecast around ~40% still looks coherent, because that strategy is explicitly designed to avoid the “correct but badly priced favourite” problem. 

“If I force this model to flat-stake every single direction it prefers, including horrible 1.01 / 1.02 / etc favourite spots, does the underlying predictive signal still survive?”

All-leans ROI being lower than 20% is not surprising once you properly account for how stupid the forced strategy is.  Ive painfully watched over time it being forced to make many times repeated 0.01, 0.02 bets where even a win absolutely hammers the ROI. 

Positive all-leans ROI across general boxing may itself be the more remarkable fact than whatever the ROI itself ends up being. 

Interestingly the model has, for weeks, produced a remarkably stable ~20 percentage-point advantage over market-implied probability on its tracked predictions. In the current boxing odds environment, that has translated into roughly ~13.5% ROI across all leans and a recurring ~40% expected ROI on the smaller value-only subset. 

The edge is not the ROI. The edge is the persistent probability gap. ROI is how boxing odds pay that gap. 

Hopefully any interested readers should be able to make use of this model log as a resource to assess model performance over time. I adopted the standard of including [model log boxing] in the title so they are easily searchable.

As always if anyone has any questions or would like anything cleared up, then please just ask.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 2 months ago

[model log boxing] 10 timestamped predictions, 3 value picks for this weekends fights + last weeks result summary

Well finally a decent upcoming weekend slate! I have a new rule “i must never brag about my pipeline data coverage in public again”

There was very little indicated upcoming last week (full details below)

So I decided just to skip last weeks log as i have to admit i was pretty embarrassed making two whole threads for what in the end was exactly 2 results last time.

Anyway a much more exciting weekend in prospect with 10 predictions currently locked including three value picks

Here’s this weekends predictions. 

https://preview.redd.it/a8pyh5o6zl9h1.png?width=1390&format=png&auto=webp&s=88ed92852425cdc07b61a935029a8fcdbf261772

https://fitequant.com/upcoming

Euri Martínez vs Jahi Tucker
https://fitequant.com/compare/948-euri-martinez/1443-jahi-tucker?bout_id=230

Edwin Santos vs José Valenzuela
https://fitequant.com/compare/963-edwin-santos/1508-jose-valenzuela?bout_id=235

Floyd Diaz vs José Santibanez
https://fitequant.com/compare/12300-floyd-diaz/12306-jose-santibanez?bout_id=240

Last weeks results

https://preview.redd.it/hadet028zl9h1.png?width=1387&format=png&auto=webp&s=6df07470e84db36fb91c60d82e2123fde8f89cd1

https://fitequant.com/results

There were five results in total since the last log post. The model got them all correct but most were easy unremarkable wins.

The interesting result was this value pick which unusually happened midweek

https://fitequant.com/compare/428-liam-paro/432-lewis-crocker?canonical_fight_id=24783

I really wanted there not to be any “missing” picks in this log, so I made a public timestamped pre-fight prediction screenshot for this bout as a comment on my previous log post.

Also, a user contacted me this week and mentioned that the model’s log loss and Brier score data look interesting so far, i do agree it certainly looks interesting. 

But I’ve thought about it, but I’ve decided not to add log loss or Brier score to the FiteQuant results UX right now, although thanks to the user in question for raising this. 

As always if anyone has any questions feel free to reach out.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 2 months ago

[model log boxing] 49 confirmed all-leans now logged — 77.55% accuracy +6.31u flat-stake P/L

Here are the current "all model leans" results for the fitequant default model:

49 confirmed all-leans bets
77.55% accuracy
+6.31u flat-stake profit
12.89% ROI

Below are the latest 2 results added this weekend.

https://preview.redd.it/0ch69m913g7h1.png?width=1409&format=png&auto=webp&s=262b682a0bad166511f2d3d3f857013e1a9aa150

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

And the" value picks only" betting strategy data…

49 confirmed results 

18 strategy bets
61.11% accuracy
+6.76u flat-stake profit
37.60% ROI

https://preview.redd.it/btxkmuc53g7h1.png?width=1397&format=png&auto=webp&s=814b1d18d68a17224bb9c75dfa0f0526812e6ac3

https://fitequant.com/results

Only 2 results in the end this week. Frustrating, but with my data pipeline performing well as a whole im not changing anything. Lets see what happens next week. 

Not much currently indicated as upcoming for next week, but thats not unusual at this stage on a Monday. If anyone is interested i’d recommend checking regularly the upcoming page. Even i cant really predict when a new bout will make it through data quality gates, but i guess as you’d expect in boxing more bouts gradually appear in the days leading up to the weekend itself.

Quiet week is annoying for the product screenshot itch, but it is better than forcing a bad slate into the system. Patience is the least glamorous data-quality feature, sadly. 

https://fitequant.com/upcoming

Hilariously the womens boxing bout that I said in this weeks prediction post “looked like a good bet” obviously lost. 

https://fitequant.com/compare/11602-jasmine-artiga/11616-nataly-hernandez?canonical_fight_id=24705

Very sensibly seeming now, the model said there was no value in this bout, so the value picks only strategy said no bet, and as result the value only strategy takes a brief lead in overall profit as well as roi now.

Not for the first time fitequant seems much smarter than me here, and overall the model continues to look strong albeit on a 2 sample slate only for this weekend itself.

Obviously only 2 results this week so my roi forecasts remain unchanged at approx 20% for the all model leans, and approx 40% for the value only picks strategy.

Lets hope for a more usual sample size for next weekend as we hopefully, and rather excitingly perhaps, cross 50 time safe results

As always if anyone has any questions or would like anything cleared up, then please just ask.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 2 months ago

[model log boxing] all model leans two predictions for this weekends fights + multi model data so far

Unfortunately the slowest weekend indicated so far in now several weeks of this on-going boxing log now, with only 2 bouts making it past data quality checks so far.

https://preview.redd.it/vdhr40dj9u6h1.png?width=1410&format=png&auto=webp&s=9535e383172551a3925b37fe92865c87dab951dd

https://fitequant.com/upcoming

Jasmine Artiga vs Nataly Hernandez

https://fitequant.com/compare/11602-jasmine-artiga/11616-nataly-hernandez?bout_id=224

Jesse Rodriguez vs Antonio Vargas

https://fitequant.com/compare/268-jesse-rodriguez/1277-antonio-vargas?bout_id=201

Naturally this is frustrating as i’m keen to get more results.

But there is a lot of female boxing this weekend, and also the main bout fighter this weekend, Jesse Rodriguez is in a lighter weightclass, with most likely a weak undercard.

So think this is an unusual situation where there arent many bouts available with enough public data to pass data quality checks. Its also sadly expected behaviour after me bragging about my data pipeline coverage last week :)

I’d expect both these predictions to be correct and collect an approx 33% profit on the weekend (for all model leans strategy) as a whole if these prove to be the only predictions made this weekend, but i often get a bout or two extra over the weekend itself through the pipeline.

It would be a shock if Jesse Rodriguez lost at those odds, and i think the Jasmine Artiga vs Nataly Hernandez fight (although i know nothing about womens boxing) looks like a good bet at those odds, with that level of model confidence (even if the model doesnt strictly indicate value its close at -3%).

Something interesting

Because this weekend seems like it might be a bit slow, and im really trying not just to make this a picks post, i thought id share some interesting early data with the sub, please see the below screenshot.

Early timesafe multi model results (all model leans, so result = bet)

https://preview.redd.it/gjqw1tiq9u6h1.png?width=1406&format=png&auto=webp&s=08299f0b70317e218f21a756f14d0d26556b6ee1

What i’m showing here is basically a list of models that ive created as a user in fitequant to test out various different theories, they all have whatever stupid name i decided to call them at the time of creation and initial backtesting, but you can hopefully still see some patterns emerging.

Public data focused models

Objective only

https://fitequant.com/models?model_id=19

Opponent Derived Objective

https://fitequant.com/models?model_id=20

Height Reach Delta

https://fitequant.com/models?model_id=25

Public data focused models what i’m calling “objective” in fitequant, do overall not terrible in accuracy, but it turns out that’s not enough in boxing, as even 60-70% accuracy results in seemingly strictly negative ROI for these models. Even when more naively perhaps, they might make sense.

Structured subjective inference (ssi) focused models

Pure subjective

https://fitequant.com/models?model_id=22

Very high subjective

https://fitequant.com/models?model_id=18

Structured subjective inference, what i’ve called “subjective” in fitequant, is arguably fitequants killer edge and innovation, but it seems that just “setting it to max” in the model config isn’t enough to compete with the best performing models.

Best performing models heavy ssi + public data blend

Algobetting model (i configd this in a model log post a little while ago)

https://fitequant.com/models?model_id=21

Fitequant default model

https://fitequant.com/models

Admittedly the fitequant model and algobetting model are very similar as one is an iteration of the other, but it really supports what ive seen in backtesting consistently for some time now, ssi is very real, but by itself not responsible for the current ROI.

I think public “objective” data does real work in what i call “matchup factors” (height reach delta etc) and also even just as a guard in cases where the ssi rating is perhaps not as accurate as usual.

Reassuringly this all backs up what i’ve been seeing in backtesting for some time now. But it feels great that just because i decided to backtest a theory one day as a user, the result of that is that fitequant quietly logs all this valuable timesafe data over time.

The fitequant model builder may look relatively simple but thats by design, i’ve been unimpressed by UX in this space, and thought i could maybe do a better job. Im glad to see that the early timesafe multi-model results seem to confirm backtesting that user model weighting changes are overall really quite powerful and decisive.

Overall a frustratingly slow week in store results wise, but i’ve tried to demonstrate that I now think real valuable research can be done in this space in a way that just wasn’t easily accessible before.

As always if anyone has any questions feel free to reach out.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 2 months ago

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

Here are the current results for the fitequant default model:

47 confirmed all-leans bets
78.72% accuracy
+7.28u flat-stake profit
15.50% ROI

Below are the latest 9 results added this weekend.

https://preview.redd.it/60xypkh0iu5h1.png?width=1399&format=png&auto=webp&s=f9503359e23e76d706f3384fe959928eef0ebc44

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

And the value picks only betting strategy data…

https://preview.redd.it/dpzswut1iu5h1.png?width=1408&format=png&auto=webp&s=9aa4f58ab8a273ca4569e8cbfa020c063f0ce013

https://fitequant.com/results

A pretty relaxing and useful weekend, flat stake p/l doing nicely still with both strategies in some profit over the weekend again. Ended up getting 9 bouts through the data pipeline and all were confirmed as results (no cancellations)

I don’t mind saying that im genuinely proud of the way my time safe data pipeline is now generating quality data for users, to hopefully push on with roi in future with strict timesafe backtesting etc, id initially targeted boxing as i thought it would be the perfect opportunity for structured subjective inference and when you consider the ridiculous lack of public tooling on top of that, it seemed to me worth a shot.

I knew exactly what i was letting myself in for with messy boxing data, and i’m sure sub users wont be shocked to discover it did not disappoint in the messiness stakes. 

Im thinking based on current data my forecast of 20% ROI on the all leans strategy looks coherent, and no real reason to change it? Its important to remember that boxing payments are lumpy so i’d expect it to wabble around for a while yet.

As for my rather bold early forecast on approx 40% ROI on the value picks strategy, well annoyingly only two value bouts were found this weekend, which tells us almost exactly nothing, so i just don’t see any reason based on current data to change that either.

As always happy to answer any questions so feel free to reach out if i can clear anything up.

Thanks, Dan

reddit.com
u/Character_Pie_277 — 2 months ago

[model log boxing] timestamped predictions. 8 total leans 2 value picks for this weekends fights

Here’s the locked predictions for this weekends fights as of today for the fitequant default model

https://preview.redd.it/k4lk30ezeg5h1.png?width=1403&format=png&auto=webp&s=cdf7579c28ef7a16840b2446df05e4f0c45f2ebf

https://fitequant.com/upcoming

Here are the two value pick bouts so far this weekend

Kenneth Llover vs Michael Angeletti
https://fitequant.com/compare/1470-kenneth-llover/11127-michael-angeletti?bout_id=216

Jack Massey vs Cheavon Clarke
https://fitequant.com/compare/315-jack-massey/319-cheavon-clarke?bout_id=214

Excitingly 8 predictions already, and it’s often the case that an extra bout or two gets through data pipeline quality gates over the weekend itself, so we may get more.

Interestingly i’m starting to see approx 40% ROI not just on the model results (value picks strategy), but also in backtesting and in active prediction data quite consistently now.

So i’m being bold and forecasting that the fitequant default model likely has approx 40% long term ROI. I appreciate with only 16 actual bets places its still quite early and i think its likely to vary a bit for a couple of weeks at least, but i now wouldn’t be surprised to see it settle at that approx over time.

Please remember i’m not changing the model config anyway so the difference vs implied should be now essentially static. 

See the following screenshot to see what i mean, this is the current active prediction data for the total of 10 bouts currently marked as upcoming in fitequant. 

https://preview.redd.it/aps0eq08fg5h1.png?width=1386&format=png&auto=webp&s=7441166422bc76ddedc92d20d26faaa3d4bceccf

The key is although its only a 10 sample i think this is really quite typical of what a user could expect from a typical upcoming slate.

Small mistake

Yesterday while fumbling with a broken hand while trying to use mobile reddit, i managed to delete the last timestamped results post i did <- genius! 

This is pretty annoying considering id been trying to do my best to leave a timestamped  predictions vs results public log, over weeks, as a resource for competent sub users who may wish to know more.

Anyway for completeness sake i just thought i’d repost that last results screenshot here

https://preview.redd.it/2f99wggafg5h1.png?width=1424&format=png&auto=webp&s=4bbab7d48102d632ffeae6c370a04bb8de34882f

You can access the current pre-weekend result data here:

Value picks only: https://fitequant.com/results
All model leans:  https://fitequant.com/results?prediction_strategy=all_leans&period=all&per_page=20

As always i’m more than happy to respond if anyone has any questions, so please feel free to DM or just ask any questions in a comment if you wish.

Thanks
Dan

reddit.com
u/Character_Pie_277 — 3 months ago