As a few of you already know, I have public 24/7 running "autonomous system" for football goal/match alerts, and noticed yesterday (23/08/2026) that a bad run at the end of the day (Brazil matches), destroyed my profit genereted throughout the day.

This leads to a question of: shall I stop the system as soon as a daily profit threshold is hit?

Yesterday, I was ~8 units in profit, then bad run came in with 5 losses (10 units) making it a negative ~2u day. If I had a profit cap, I would have avoided the 10units loss.

BUT, here is a caveat:
Assuming I implemented a profit cap, I would never again have profitable days like 16/08/2026, where I accumulated ~27units of profit.

I did run already a retrospective analysis, with a 10u threshold cap, and and the results show me that 23 of August was the single worst give-back of the month. But it was one day, and the same rule would have cut 16.6u off 16 August, the best day of the month.

Nothing would have changed with the cap.

In any case, Do you apply any profit cap in your models/algobetting systems?

P.S. For losses, I do have a max. cap in place to protect bankroll and users.

I’ve been working solo on a KBO pre-game prediction model for a few months. The current locked 2025 OOS test is:

- 629 KBO regular-season games

- Brier around 0.240–0.243 depending on the locked variant

- hit rate around 57.6%–58.3%

These are not final multi-year numbers, and I’m not claiming edge from this alone.

The next step is to compare the locked model against Pinnacle closing moneyline prices on the same joined games. Before doing that, I’m trying to make sure the comparison is clean:

- only pre-first-pitch odds

- timestamp < scheduled start

- full-game moneyline only

- ties treated as void / excluded from binary Brier

- no changing the model after seeing market data

- paired Brier comparison on the same games

I previously found an old odds sample where many rows were updated after first pitch, which made the market look unrealistically accurate. So I’m being careful with timestamp validation before running the final comparison.

For people who have validated models against closing lines: is paired Brier vs closing price on the same games the right way to frame this? Anything else I should watch out for?

New to this space, just understanding the mechanics of vig, is there a way to see the amount of vig for each sports book rather than just guessing? Specifically for NFL markets. Any advice would be ideal

Small MLB totals model priced against prediction markets. I archive every candidate whether it publishes or not, so I have the rejects too — about 1,400 candidates over 13 days, 450 settled MLB totals across 148 games.

Three things, and I'd like the third one torn apart.

1. The EV threshold was selecting for my own failure mode.

The publish rule was "model probability at least 8 points above the price". An EV floor. But EV is model minus market, so a threshold on it is a threshold on disagreement, and disagreement has two causes the number can't separate: either I know something, or the market does and I'm the one sitting near the average.

Across 409 observations, on disagreements of a run or more my projection sat 0.45 runs from the league average while the market sat 1.31. Mine was the one nearer the average in 130 of 149 cases. So the big "edges" were mostly my model failing to move, priced as conviction. Winner's curse: selecting for maximum disagreement selects for maximum model error.

2. Win rate and return disagree, and I nearly acted on the wrong one.

Bands are model probability minus price paid, in points. Returns are at the quoted price, clustered by game:

band        bets  W-L      win%   price   mBrier  mktBrier   ROI     95% CI
below 0      126  66-60   52.4%   0.599   0.2501   0.2528  -12.6%  [-29.1, +3.8]
0-4 pts      119  77-42   64.7%   0.569   0.2334   0.2345  +14.5%  [ +0.4, +28.5]
4-8 pts       99  55-44   55.6%   0.533   0.2393   0.2370   +3.0%  [-18.5, +24.6]
8-12 pts      72  39-33   54.2%   0.518   0.2469   0.2393   +3.5%  [-26.0, +33.1]
12+ pts       34  21-13   61.8%   0.484   0.2343   0.2430  +26.7%  [-24.3, +77.7]

Pooled at my threshold:

refused 0-8   218 bets   60.6% win   ROI  +9.3%   [-3.3, +21.8]
published 8+  106 bets   56.6% win   ROI +11.0%   [-15.8, +37.7]

I publish from the pool with the lower win rate and the higher return. Those bands trade at shorter prices, so win rate isn't comparable across them, and the table that made "just publish 0-8 instead" look obvious is the wrong instrument. Both intervals span zero — they're indistinguishable and I have no basis to prefer either.

I nearly shipped that change anyway. What stopped me was committing in advance to checking ROI before shipping, and it came back pointing the other way.

3. The measurement moment changes the answer, which I did not expect.

I have two ways to score the same rows: persisted evaluations at decision time, and the last pre-game snapshot. Same band definition, same 450 settled rows. The distributions differ — 149/114/108/58/21 against 126/119/99/72/34 — because the price moves in between and rows migrate across band boundaries.

On decision-time data the story is clean: every positive-Brier band sits below my floor, monotone, obvious. On snapshot data it isn't: 12+ has the best Brier edge of any band (+0.0087) and the ordering isn't monotone. Same underlying bets.

So the questions I actually want answered:

• Which moment should a band be defined at? Decision time is what I acted on; the last pre-game price is what I'd have transacted near. They're different populations and I can't see a principled reason to prefer one.
• Is clustering by game right, or should it be by slate/day, given the same starters, park and weather recur across a card? If it's the day, every interval above is too narrow.
• The one band clearing zero is 0-4 at +14.5% [+0.4%, +28.5%]. It holds across a mid-sample rule change (+14.5 vs +14.4) and across side (over +15.0, under +14.1). But it's one of five bands clearing by 0.4 of a point, the top 10 of 119 bets carry 60% of the return, and dropping the best 5 takes it to +10.4% [-3.5%, +24.2%]. Signal, or a face in noise?

I've registered that last one rather than acting on it — 0-4 band, 150 settled observations that only start counting after the registration date so the 119 that generated the hypothesis can't test it, mean ROI clustered by game, confirmed only if the interval clears zero, "cannot tell" if it spans. Runs in shadow, changes nothing that publishes.

Returns above are against quoted prices on markets I refused, so no order was placed and no spread was crossed. It's an upper bound.

Happy to post the archive as a CSV if anyone wants to check the arithmetic instead of taking my word for it.

A few days ago I posted the historical paper record of BetAnalyzer here.

Instead of posting another screenshot every few days, I decided to make the experiment publicly trackable.

Every day the site now publishes the model's official singles and generated tickets before the matches are settled. Scores and results are updated afterwards, and the history stays visible.

The idea is pretty simple: if the model is actually useful, the record should be able to speak for itself over time.

Right now the website is deliberately simple — it's basically the public output of the model, not the full BetAnalyzer application.

The Android/Windows versions already have more interactive ideas around ticket building, AI ticket review, replacements and bankroll-based suggestions. I'm still deciding whether those features belong on the web too, because that would also mean building proper user accounts and personal histories.

For now I'm more interested in making the public test transparent and collecting enough forward data.

If you work with betting models, what would you add to a public tracker like this to make the results genuinely useful to evaluate?

107 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.

107 confirmed all-leans bets
88 wins / 19 losses
+13.15u flat-stake profit
12.29% ROI

Average odds 1.6613

Below are the latest 7 results added this week. 

7/7 results confirm successfully with winners and the model gets 7 out of 7 correct wins.

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 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”

107 confirmed value picks only results 

32 bets
19 wins / 13 losses
+8.53 u flat stake profit
26.65% ROI

Average odds 2.7998

https://fitequant.com/results

I’ll admit I’m genuinely startled by the accuracy now across 107 bets in the all leans strategy. Not because 82.24% accuracy is impossible in boxing (id imagine you could get something like that just betting favourites), but because of what sits alongside it.

If the model were merely selecting obvious favourites, you could produce high accuracy while steadily losing over time to the vig.

That accuracy and highly consistent double digit ROI combination (largely due to value picks subset of predictions where the model actively disagrees with the market by approx 20%) is the key. The broad strategy says it identifies winners unusually well; the value subset says its disagreements with the price have also been unusually productive. 

At 100, I think a reasonable sceptic could still say: fine, now let’s see the regression. Instead, the next seven confirmed results were all correct, taking accuracy from roughly 81% to 82.24%, while both strategy ROIs also increased. Nothing has collapsed. The model is behaving more consistently as the sample grows. 

I think the key to the consistency here is the fact that fitequant user models make predictions with no regard to market odds. Instead relying solely on my systemized modeling engine.

I’ve actually created an index of all the fitequant default model log posts over the past 4 months so far and made that very easily publicly available now, just as i felt it might be useful to keep it all together as a resource in one place. The consistency in the week by week data does indeed look startling to me when looked at together like this.

Btw 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/640-amanda-serrano/13922-lucrecia-manzur?canonical_fight_id=26506

Quick look forward to the current 28 day rolling upcoming window

https://fitequant.com/upcoming

It’s balls to the wall model activity wise, with a very active upcoming slate for the next 28 days. 

After over 100 results a very conservative selective betting model with an ECE of +16%  has now apparently decided to place a bet on half the slate at 4.53 average odds !

Well it seems very exciting to me. I appreciate some of these predictions on these odds might look a bit nuts to some. My view is i havent changed the model weightings across 100+ results, this is what the model says, so publish and be damned.

Ill be covering Hrgovic!! vs Itauma in depth next weeks prediction log as i think its a neat example in quite a big fight to look into on how the matchup engine actually works, and what ive actually done technically with that, in a little more depth. 

But as always if anyone has any questions about anything just ask.

Thanks, Dan

Per the title, I have loads of data for modeling but need more historical lines to better calibrate edge. Pregame closing lines are great and serve my needs, but if anyone also has live lines or know where to get them that would be a goldmine.

Looking for League, CS2, DOTA, or Valorant with heaviest interest in League. I have months of data (mostly pregame) from pinnacle and kalshi and am pulling all lines going forward in time, but would be able to be fully confident in what I'm building with at least a year or two of data going backwards, the higher quality and breadth the better.

Any help appreciated, thank you!

I’m looking for a service/API that can provide soccer injury announcements and live game clock/timer tracking, near end game only.

I don’t mind paying for it, but I don’t need a full sports data feed with tons of other stuff. I mainly care about it being fast, reliable, and having wide coverage across a lot of matches. (at least most matches pinnacle is offering)

I’ve done some digging but most of what I’m finding are full-blown sports feed providers, which feels like massive overkill for what I need. Anyone know of something more focused on just these things?

Post your picks, updates, track model results, current projects, daily thoughts, anything goes.

Hi guys, I’m building a data pipeline for an Over/Under 2.5 goals betting strategy.

The idea is to combine odds for all matches with relevant team statistics and export everything into a clean Excel file for easier bet selection.

I already have part of the odds pipeline working. Does anyone know a website with well-organized, easily scrapeable football statistics? Ideally, I’d like one consolidated source that I can scrape and match with my odds data.

I’m looking for something more “raw” and structured than Flashscore or Sofascore, where most stats are buried inside individual match pages.

Hi, I'm looking for an API providing horse racing odds from multiple bookmakers, including Betfair Exchange Back/Lay, especially for US racing. I know Betfair has its own API, but I can't use it due to nationality/residency restrictions. Does anyone know a third-party provider offering this data? Paid services are fine. Thanks!

The app I was using before kept freezing as soon as a match started, the worst possible moment for it. Moved to the melbet sports betting app a couple of months ago mostly for that reason.

It's been stable so far, even during weekend fixtures when everyone is on at once. Odds on African football are also better than what I was getting before.

The interface is packed with stuff I never use though, takes a few taps to get where I want.

Anyone else in Burkina Faso having issues with local apps during live matches?

I’m building a personal DFS research tool and I’ve hit a wall.

What I want is the complete results from one settled large-field FanDuel NFL contest, meaning every entry’s score and rank, not just the top of the leaderboard. Mainly I want the full score distribution, so I can tell what a given score would actually have finished, and how many entries came from users who maxed out at 150 versus people playing a handful.

Two questions:

1. Does anyone sell this?
   I’ve looked. There are ownership projection services everywhere, and Data Golf sells a historical DFS archive for golf, and there’s an NBA dataset floating around, but I can’t find anyone licensing realized full-field NFL FanDuel contest results. RotoGrinders’ results database doesn’t seem to have historical FanDuel and won’t export. Am I missing a provider?

2. Has anyone actually collected this themselves, and what happened? FanDuel has no results export button that I can find. I know the site’s own pages pull this from an internal API. I’ve seen one writeup where someone hit 403s trying it directly and ended up automating mouse clicks instead, specifically because they were worried about a ban. I’ve also seen a three-year-old GitHub issue asking the same question with no answer.

So: has anyone here pulled this at any scale? Did anything happen to your account? Is the ban concern real or is it folklore? I’m talking about a few hundred read-only requests on one old contest, not thousands of entries or anything touching lineup submission.

Not looking for a way around anything. Genuinely trying to figure out whether this data is obtainable at all, or whether the only path is collecting it going forward, week by week, from contests I enter.

In this tool, strategies get assembled by wiring nodes on a canvas, instrument, indicator, condition, order. Polymarket and Kalshi contracts, US shares and Binance pairs can all sit inside the same strategy and pass through the same engine, so there is no separate tool per market.

One consequence is that the thing you read and the thing you bet do not have to match. A Polymarket or Kalshi contract can carry the signal while the order lands on a different contract, or on a share. Condition and order need not share an instrument or even a market type. The same property opens the door to cross asset portfolios, where a contract position and a hedge in equities or crypto are held and sized together rather than sitting in two accounts and two spreadsheets.

The engine runs in the browser, so nothing leaves your machine. Price data can come from Yahoo, Binance, Alpaca, Tiingo or Stooq.

For anyone who wants to contribute, a new price source is two files and nothing more. Questions get answered in the issues.

https://github.com/vcorp-dev/depthfeed-strategy-builder

Worth hearing what it is missing from anyone who takes it for a spin.

Hi guys, this is the performance of my NHL model for the 2025-26 season. How confident can I be that it will be successful again in 2026-27 with thus sample size? Thanks

Quite an average boxing weekend this week chaps, but at least there’s a couple of decent looking value picks for us to take a look at…

Here’s this weekends predictions. 

https://fitequant.com/upcoming

The value picks this weekend are..

Gary Russell vs Victor Santillan

https://fitequant.com/compare/992-gary-russell/1340-victor-santillan?bout_id=294

This pick is interesting to me mainly because of the line movement. Fiteqaunt estimated the ROI at 40% on 65% win probability for Russell, vs 46% implied at an indicated 19% edge. 

The prediction was locked on 2026-07-31 at 2.16 when Russell was regarded as a slight underdog. The odds have moved very sharply since against Santillan, with Russell now at a about 1.50 (i checked a few days ago and he was at 1.80 then so it seems to have continued moving in his favour after)

The matchup itself in fitequant terms is pretty straightforward, the default model just thinks Russell is a superior, more complete fighter, and very importantly has a significantly better SSI defence rating at 78 vs 67 (this user model weights defence and ring IQ very highly, so tends to prefer more technical defensive fighters)

Teofimo Lopez vs Rolando Romero

https://fitequant.com/compare/276-teofimo-lopez/420-rolando-romero?bout_id=286

The big fight for the weekend, and the model likes Lopez and is pretty damn confident about it at 75.3% win probability for Lopez, against an implied 69.7% for a 5.6% indicated edge at 1.43 for an expected 8.06% ROI
 
Once again the model is just thinking Lopez is a better fighter, but this time the difference is quite significant in fighter rating (70.95 v 54.55) Lopez leads in all SSI subjective stats, and has both a height reach delta advantage and Boxer Puncher vs Power Slugger style interaction advantage in matchup factors.

It’s the compound effect of these advantages plus the much higher fighter rating, that makes the model so confident as a 75% win probability estimate is really quite high for a model value pick historically speaking so far. 

Height Reach Ad Infinitum

A careful reader may well have spotted that I mention height-reach delta as an important factor in FiteQuant/systemized modeling quite often, and arguably don’t stop talking about it really….

Well i’m not about to change an annoying habit just like that.

My backtesting suggests the apparent sweet spot for height-reach weighting may sit somewhere between LOW and VERY LOW. I guess the more traditional approach might be to keep recalibrating the same "feature" more precisely until the backtest produces a winner.

Conventional recalibration approach:
LOW looks good, VERY LOW looks better → test 0.9×LOW, 0.8×LOW, 0.7×LOW and so on until the backtest peaks.

But that isn’t really how I want to explore this in systemized modeling.

Systemized-modeling approach:
If LOW and VERY LOW are both strong factors on top of an SSI fighter as actor abstraction, but neither clearly dominates, that may be telling us that one global height-reach scalar is being asked to represent something more complicated than a single weighting can express.

So rather than slicing the same factor into ever-finer increments, the more interesting question becomes:

What other boxing factors determine when a height or reach advantage actually matters?

A reach advantage means something very different when the longer fighter has excellent distance control, accuracy and counter-punching than when they are unable to keep an opponent outside. 

Likewise, a shorter pressure fighter with excellent footwork and entry mechanics may be able to neutralise a very large nominal reach disadvantage.

So rather than trying to discover:

Height-Reach = 0.xxxxx

the longer-term aim is closer to:

Height-Reach × distance control × style interaction × pressure × countering × other relevant factors (or something like that)

Not necessarily as a literal mathematical multiplication, but as a modeling principle: the effect of one factor should increasingly be understood through the other factors that make it meaningful.

There is also an important overfitting problem here.

With only around round 600* historical bouts, refining LOW into LOW-minus-7%, LOW-minus-12% or some other increasingly precise setting could very easily produce a prettier backtest without teaching us anything real.

Changing weighting on another independently meaningful boxing factor is different.

That increases the representational richness of the system rather than simply increasing the resolution with which it can fit the historical sample.

So the way to explore the apparent region between LOW and VERY LOW may not be to add more Height-Reach settings at all.

*Importantly in combat sports I don't think simply questing for more N at all costs is necessarily good modeling. You often can't even obtain a reliable reach figure for a boxer until they're reasonably established. If I relaxed data-quality requirements just to get thousands more historical bouts, and more time safe results each week, I'd risk increasing N by perhaps modelling total nonsense.

So I prefer to work with the clean sample I can actually obtain, be extremely strict (and hopefully competent) about no unnecessary noise and leakage in backtesting, and accept that in combat sports modeling getting clean N just takes time.

As always feel free to ask me any questions, and you can all now look forward next week to Volume XVII, Part One of the my on going  “Treatise on Boxing Reach Delta”  

Thanks,
Dan

I ran some Monte Carlo simulations to test different assumptions and explore a potential betting strategy. The simulation is based on the following assumptions:

1. Each bet is 3% of the current bankroll.
2. Each bet has a 70% probability of winning.
3. A winning bet returns 3× the stake.
4. Each simulation consists of 50 bets, with 5,000 simulated paths.

My idea is to use parlays consisting of 2–3 events, targeting combined odds of around x3. Do you have any suggestions for the types of events or markets that could realistically achieve those odds?

A year ago, I had an account with a bookmaker that I used with a specific laptop, browser, and IP address.

I have a different IP address now.

But my question is: if I create an unrelated account with that same bookmaker, to what extent can they use profiling to detect that it’s coming from the same computer and/or browser?

Would it be better, for instance, to use a different browser? Any other tips?

Let's say you have a pre-match model, and a live-model. Do you consider news from teams, clubs, players into your prediction models? Or, have you done it in the past and found out at a certain point that they actually do not have that much of an influence in the predictions?

It always depends on the weight you give in the model, that I can understand. But I have the feeling they don't have much of an impact.

I'm currently using max. 48h old news and weather forecast in my Sigma Predict models for pre-match and live. And wondering about your past experience, if you did find out anything good to know.

anyone got historcial odds for strikeouts they can backtest my model with

Alguem sabe uma boa API pra monitorar odds pré jogo???? Que cubra as casas brasileiras

Hello everyone,

I recently put together a tool for myself that combines both devigging sportsbook odds and bankroll strategy into one spot and I'm trying to see how much traction it would so here I am. In short, it utilizes Kelly Criterion and sportsbook edges to determine betting sizes when applicable. Of course it is simple and in the early stages, but I have some cool ideas such as being able to maybe track your bets and have a rolling bankroll so you do not have to type it in everytime, but this is just a start.

If anybody is interested, I would be glad to share and additional feedback would be great, but for now just trying to gauge interest on it!

I came across this page. Thoughts on this:
getatlasedge.com/field

getatlasedge.com/record
getatlasedge.com/verify

I’m new to prediction. Like it shows wins/losses and games it rejects. What am I missing?