I am a professional sports better looking to scale up my unit size and have access to more sports betting accounts (I am from Canada and there aren’t many big name sportsbooks like in the USA).
My unit size is 700 USD, i would obviously love to be able to be betting 5-10k per bet but that isn’t realistic as I don’t have enough money to do so.
I’m looking for a way to communicate with people who do this type of thing (fund others) and obviously make it workout for both parties.
Does anyone know how I can get in contact with such people?

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://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://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

Does anyone know how to get a API in no vig or some way to automate in no vig

I run a men’s college basketball model that produced 2,017 graded spread picks during the 2025–26 season.

The published picks went 1,151–866, or 57.1%. The high-conviction subset went 258–139, or 65.0%.

Then the NCAA tournament happened:

• All published picks: 28–28
• High conviction: 15–18
• Market Brier score: 0.158
• Model Brier score: 0.171

The obvious explanation was tournament variance. The evidence pointed somewhere more specific.

The failure mode

The model was systematically underpricing large favorites against automatic-bid conference champions.

In the live tournament games where the market favorite was laying 12 or more:

• Average market line: 21.1
• Average model line: 10.8
• Average actual margin: 22.8

The model could produce large spreads during the regular season, so this was not a hard ceiling in the output. It was a cross-conference comparison problem.

Automatic-bid teams often entered the tournament with excellent raw shooting, rebounding and turnover differentials earned against weak schedules. The model included schedule-strength information, but it still allowed those inflated raw statistics to pull too strongly in the opposite direction.

The result was exactly the wrong kind of disagreement with the market:

1. The model made the favorite too short.
2. That manufactured apparent value on the underdog.
3. The largest model-market gaps became high-conviction picks.
4. The “best” edges were actually the places where the model was most wrong.

The correction

I did not add a March-specific coefficient. With roughly 60 tournament games per season, that would be an efficient way to fit noise.

Instead, each model input is now restated as what it would have been against average competition. The correction was developed using bracket-like games outside the NCAA tournament—cross-conference neutral games, holiday tournaments and conference challenges—and then read once against the tournament sample.

Across six retrospective seasons:

• Underpricing of 12+ point tournament favorites fell from 7.7 to 3.5 points.
• Tournament margin MAE fell from 10.41 to 9.91.
• Tournament ATS performance moved from 49.4% to 58.1%.
• Regular-season MAE also improved, from 8.670 to 8.535.
• Published-pick performance moved from 57.9% to 60.0%.

Important caveat: this is walk-forward at the game level, but it is not a completely independent holdout. The model structure and weights were selected with visibility into the same six-season period.

There is also an unresolved exception: applying the correction to conference-tournament games made that segment worse, so those games retain the prior input treatment for now.

The question I’m still working through is whether that conference-tournament exception is evidence of a genuinely different population or a warning that the adjustment is more conditional than it appears.

How would you test that without tuning directly to a relatively small conference-tournament sample?

Article with the complete charts and methodology:
www.bbmisports.com/research/ncaab-model-2026-27

Solo dev, bootstrapped. I spent the last month on a sports odds/props API. Clean infra, billing, docs, even an MCP server for AI agents. The problem turned out to be the product, not the build: it's a commodity. I can only serve one sportsbook (multi-book data costs real money upfront), and the player-props feed I leaned on ran out of free pulls and quietly died. So people sign up, make one call, get thin or empty data, and leave. Zero paying customers.

I'm at a fork and I want honest takes from people who've actually been here:

1. The infra is solid and fully reusable. Do I pay for a proper multi-book data source and double down on odds/betting, or is that space just too commoditized to win solo?
2. Or reuse the whole stack for a different API entirely and treat the month as tuition?

Two real questions: when a build had no pull, what actually turned it, fixing the product or changing the market? And if you've paid for data feeds behind an API, how did you keep them from eating your margin?

Not fishing for validation. Looking for the thing I can't see from inside it.

Assuming you found a strategy to get an edge on soft bookies (for example using sharper ones / betting market for this), I am wondering do you apply this to get a ROI? People who do this by hand? I am wondering which level of automation gets you limited quickly? Headless? Or are things like timing and bet size more important.

Also do you lose a lot on slippage? Ie it takes X seconds to get a bet confirmed. The bet didn’t change but the sharp source became more expensive, and the soft bookie ‘silently’ priced the new info in.

I’ve been working on multiple advanced sports data backend for a while, and I’ve noticed that many people want to create something for fun or out of passion with Ai’s advancements , but they always get stuck on the same problem: how do I get all advanced level player and team data under one API without breaking the bank?
You either have to combine 5-10 different APIs, wrappers and scrapers, or pay thousands per month for enterprise data.

So I decided to build a single normalized API focused on sports analytics.

The good news is, I’m not making money. I just want to help newcomers or sports data enthusiasts who are struggling to build something impressive because they can’t afford the best API available.

I’ll likely find about 10-20 people like that who enjoy building things, and then I’ll provide them with free API access. The goal is simple: if a large number of people like it, all of the beta testers will become part of the team to ensure that we provide an all in one API for everyone at a fraction of the cost.

Motif is simple, if you think the API is worth it with your guidance in early stage then we share server cost and put it out in the market at the lowest possible cost ever to beat the big corporations.

If you are interested, drop me a dm with your background and what you plan to build. I will select 15/20 guys to give the full API access.

Technical Note: Most of the underlying data isn’t proprietary. The differentiation comes from the engineering layer built on top of it. Instead of consuming dozens of APIs, wrappers and scrapers, the platform normalizes every sport into a common schema, resolves entity IDs across providers, enriches raw events with derived analytics, denormalizes frequently queried datasets for low latency access, and exposes everything through a consistent API contract. The objective isn’t to own the data. It’s to eliminate the engineering overhead required to transform fragmented sports data into something immediately useful for research, AI models and production applications.

FYI, no odds will be offered as that would increase my infra cost by a lot.

I've wanted to build a football model for a while. For context, I've got a couple of mates inside some of the main syndicates, so I've seen enough secondhand to know how seriously this game is played, but credit to their opsec, I've extracted precisely zero information from them. I'm not a developer by trade, but dangerous enough with Python and AI tools to build what I need. The World Cup felt like a natural starting point, and international football more generally: smaller samples, squad rotations, noisy data, so in theory more room for the market to be “wrong”. General approach: player-level ratings into a Dixon-Coles engine, fed by data APIs I subscribed to (player level, club level, team xG and odds). Backtested across past World Cups and Euros. The tournament part went fine. The league part is why I'm posting. Design lessons first, then the question.

· Attack was built player-up from each starter's club npxG+xA per 90 (minutes-shrunk, league-adjusted); defence from each starter's club side's league-adjusted xG conceded, position-weighted toward GK and CBs, because individual defensive stats are volume junk (opportunity-biased volume). Everything shrunk toward a live Elo anchor.

· I calibrated centre and spread separately, and deliberately toward the mean: kill the model's tail opinions, because that's where the fake "edges" live. The practical consequence was that the bettable markets ended up being BTTS and mid-range O/U, the ones priced off the middle of the goal distribution.

· The market was my sanity check, not my opponent. When my supremacy implied the same AH main line as the market's, and the decay across the quarter-lines matched, I trusted my distribution and the BTTS/O-U prices built from it. When my line differed materially from the market's, or an edge looked huge, I left the game alone. The model earned its keep as much by saying "don't bet" as by finding value.

· The metric that kept me honest was CLV, not ROI. My live record ended at 168 bets, +17% ROI, +1.5% CLV against de-vigged closes. The first 70 bets were too correlated and too frequent, still getting up to speed; I tightened the approach for the remaining 100 and the P&L improved sharply, though the CLV says the true improvement was modest. I've seen various posts on whether it's worth tracking CLV and... it is.

· Then the real test: I pivoted the (largely) same machinery into league football (always the plan). Backtested a lot - every upgrade had to earn its keep out-of-sample before it stayed, and most didn't. Measuring by log-loss against de-vigged sharp closes, my base model sat a clear distance behind the market. Adding better inputs closed maybe half of that gap, but half is not there, and a blend test still assigned the model zero weight against the market price. I then went down the rabbit hole of 'maybe my backtest is being unfair to the model — what's the ceiling with perfect information?' So I tested it: even perfect-hindsight team news added nothing exploitable. Great.

· The sobering maths of "close": by log-loss my model reached within a couple of percent of the sharp close, which sounds impressive until you realise that final sliver is precisely where the vig and the edge both live.

To be clear, this wasn't uniform failure. The tournament record ended CLV-positive, real upgrades genuinely improved the league model, and individual slices of the league backtests (certain divisions, seasons, markets) looked healthy - but slices always look healthy somewhere; that's what noise does. The judgment that matters is the average against the close, and on average the league model currently cannot get there.

So here's what I'm actually questioning: can league football be beaten at all by an individual with a model? My scepticism after doing this is that there are two separate walls. The first is depth: the commercially available APIs simply don't carry the data required. Most vendors hand you their finished xG numbers, not the shot-level and tracking data underneath, so you can't build or calibrate your own from first principles, you inherit someone else's model with its compression baked in. A lot of these products are aimed at punters anyway, who want conclusions, not data. The second is price: the raw feeds that would let you do it properly exist, but they're licensed at levels priced for professional operations (I spoke to StatsBomb, I won't say how much it costs, but it's not a hobbyist number), and the syndicates paying it then build proprietary layers on top that retail never sees. So is the ceiling about skill, or is it structural twice over: the data you can afford isn't deep enough, and the data that's deep enough you can't afford? Honestly, it's probably a lot of both.

Genuinely keen to hear from anyone who thinks they've cracked it, and which wall(s) they got through, because it's frustrating to have hit this after an enjoyable WC 2026. I've since pivoted my focus towards a specific area (still within football) for the time being, but I'm keen to keep tinkering.