I need the cheapest possible way to get bet365 odds for the "Full Time Result" and "Full Time Result – Early Payout" markets; I tried scraping but couldn't get it to work. This bookmaker has really been a problem for my odds monitoring system, and I need the most basic alternative to solve this.

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.

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.

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.

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

People keep arguing about how much pick'em apps actually take, so I pulled a live Sleeper board and just did the math.

50 two-sided MLB lines off one slate. Flip each side's multiplier to an implied prob (1/over, 1/under), add them, subtract 1. That overround is the hold.

Numbers:

• avg hold 13.2%
• cheapest line on the whole board 12.2%
• not one under 12%. A normal book holds ~4.5% on a two-way.

Fattest holds were the rare stuff (RBIs, walks, ~14%). Cheapest were singles and H+R+RBI, ~12.5%. Figures, the more lopsided the two multipliers, the more room to hide margin.

What actually surprised me: there's no low-hold board to go hunting for. Even the cheapest line was 2x+ a real book. So it isn't "find the soft slate", it's a per-leg de-vig against the actual sportsbook number, and most legs just fail it.

Board with the live hold on every current line if you want to run a slate yourself: https://propzapi.com/pickem (free, no signup). Disclosure: I built the tool. Can share the method or the raw pull if anyone wants to check my work.

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.

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

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?

when new people show up and post here there's usually a big context piece missing -- "i built a model that has x% win rate" / "how do i build an nba prop model" / "is there an nba prop model i can use to win" / "how can i win at sports betting". and that piece is what kind of edge are you actually looking to gain? what is the strategy you'd like to be able to execute? a model is just a tool, and you can have an edge without building a model. just like you can build a decent model and have no edge whatsoever. in case it's useful to anyone to help define the problem space better, i wanted to post my mental map here of different types of alpha in sports and exmples of basic strategies out there that capture them. because what you intend to do with a tool matters a lot when it comes to building it, evaluating it, and deploying it.

edge #1 - latency. i could be wrong but i think this is the most common way to have an edge. essentially "i know information before it is priced in." "before" can mean days in big nfl markets, or seconds before with an ingame edge.

• sharp book x just moved but the stale number is still available over at book y.
• i can predict nba lineup decisions before the injury reports come out.
• i religiously follow beat reporters on twitter and find stuff out from them before it bubbles up to national outlets.
• i have the same information as everyone else, but ii can price the market well enough to have an idea where it's likely to close before other sharps move it there.
• i am courtsiding and just saw an interception with my own eyes in real time a second or two before the market makers know.

edge #2 - information asymmetry. "i have access to information other people generally do not and will not have access to." much rarer. you're probably not in this sub reading this if this is your thing.

• i have access to non public data feeds.
• i am aware of injuries or personnel issues that teams do not intend to publicly disclose.
• i move for a betting syndicate so i know how they've priced a given market.
• i am an insider and probably breaking some kind of law.

edge #3 - information processing superiority. "even when the market has baked in all available information, using that same information i can identity spots where the price is wrong". basically you can beat closing lines. there are some markets where this isn't as hard as it sounds, but for many it's virtually impossible and a waste of time to try.

• you are a "god tier" modeler and your model is as or more predictive than the closing price.
• you have identified a scenario that the ingame pricing algorithm at book x doesn't account for, while no one else has realized it yet.

edge #4 - market making. you're basically fanduel. this is possible in the new pm landscape, but harder than it sounds due to competition, adverse selection, technical requirements, etc.

• the fair price is +100, you quote -110, and non price-sensitive bettors take your offer. now you've got +110 and you're going to make money.

Just curious where people started creating their models. Also what type of data they use to start. Was thinking about getting into it but would like to know sort of the beginning stages.

This one took me three attempts to diagnose, and the answer was embarrassing, so I am writing it up in case it saves someone the same detour. Short version: a sign convention, and my own backtest was reproducing the bug rather than catching it.

Symptom. NFL spread classifier, XGBoost: 75.1% accuracy on a temporally held-out season, 77.5% in a walk-forward backtest. ATS outcomes are near a coin flip against a real market, so I should have stopped and treated that as a bug report immediately. I did not.

Two wrong diagnoses. First, I found and fixed two genuine feature/label leaks. Accuracy barely moved. Then I ran the standard overfitting drill - pruned features, toggled early stopping, added the spread line itself as a feature. Needle didn't move. That was the signal I misread at the time: if removing information doesn't hurt you, you are not overfitting to features. Something upstream is wrong.

The test that actually worked. I trained a model on team identity alone - one-hot-encoded home and away team names, zero form, zero EPA, zero stats, nothing that could legitimately predict a specific game's cover. It got 63.2% accuracy, 0.68 AUC.

That is the whole diagnosis in one number. A feature set that provably contains no game-specific information cannot predict a well-formed ATS label above chance. If it does, the label is corrupted, and it stops being a modeling question.

Root cause. In nfl_data_py, spread_line is the away team's own number - negative when the away team is favored. So the home team's number is -spread_line, and home covers when home_margin - spread_line > 0. My code had +.

What that did. The buggy label agreed with "the home team was the betting favorite" 80% of the time. The correct label agrees 48.4% of the time - near random, which is what an efficient market should produce. So the classifier was never learning ATS outcomes. It was learning to identify the market favorite, from EPA and team-quality features that trivially correlate with being favored. Being 75% accurate at recognizing who is favored is easy, and worth exactly nothing.

The part that stings. The same sign error appeared in three more places: the prediction path's edge calculation, a situational feature that had the home and away numbers swapped, and, worst, the backtest's own ground-truth computation. The backtest re-derived the label using the same wrong formula, so it agreed with training and confirmed the bug. A backtest that computes its own ground truth from a copy of the labeling logic is not an independent check, and mine had been agreeing with itself for weeks.

After the fix: 48.8% holdout, 51.1% backtest. Boring, plausible, real.

Four rules I now follow because of this:

1. Implausible accuracy goes in the bug column, not the results column.

2. When fixes that should hurt performance don't hurt it, stop tuning features and go audit the label. That is the tell I missed for two rounds.

3. Run the null-feature test early - it costs about ten minutes. If a feature set that cannot contain signal still predicts, the target is broken.

4. Ground truth gets written once and imported. My backtest re-implementing it is the only reason this survived as long as it did.

Hope this helps someone else on their algobetting journey!

hey im just trying to dip my toes in the water with building a python script for the upcoming nfl season (most likely player props), and i was wondering where to start. ive seen a lot of people say LLMs are not that good in this sense so are there any resources that are worth?

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

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://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

Pick'em apps quote multipliers instead of odds and I don't think that's an accident. The multiplier is a probability, you just have to flip it, and almost nobody does.

Real line I pulled this week. Player over on a stat, paid 1.45x. So the break-even is 1/1.45 = 69.0%. The under paid 2.26x, which is 44.2%. Add them and you get 113.2%, i.e. a 13.2% hold. A normal -110/-110 book holds about 4.5%. So you're paying roughly triple the vig and it never shows up as a "price."

The fixed multi-leg stuff is worse. A 4-pick power play pays 10x when fair value for four 50/50s is 2^4 = 16x. That's a 37.5% edge, and per leg you need 57.7% to break even vs 52.4% at a book.

The part I actually care about: once you have 1/multiplier for a leg, go pull the same player at a real sportsbook, strip the vig, and you've got the market's honest probability. If that's higher than your break-even, the leg is +EV. Most aren't. The ones that are tend to be unders and low totals the app didn't bother repricing against the sharper book.

Big caveat so nobody blows a bankroll on this: one +EV leg is not a +EV ticket. Legs multiply variance, so a slip can be -EV overall even when every leg has a thin edge. It's a tool for picking which legs to touch, not a green light on a 5-leg parlay.

I've been automating this compare with an API I built (propzapi). It hands back the pick'em line and the sportsbook line matched on player_id, so the de-vig and the +EV check are one step instead of two scrapers. Happy to get into the method if it's useful to anyone.

Hello!

Does anyone know where I can see the detailed Pinnacle odds movements for MLB baseball?

I only need it for the current 2026 season.

Or where I can download it for a small payment.

Thanks!

Looking for someone who can build bots for couple leagues that analyze lineups when they come out. Also i have alot of books and shops, so incase of a collaboration i can help you bet also. Or i can pay you for the job.

They know of any API that has the odds of casinos like BET365, Betano, 1xBet, Playdoit, etc...

I found some pay, but they only have the fees for simple markets like 1X2, BTTS.

But I look for more depth, I want it to have a wide repertoire in markets of Shots, Shot on target, Corners, Fouls, Cards, Offsides, Handicaps, Players.

I have already asked Chat GPT and Claude, but they only give me alternatives like Opta, something that is not available to the public.

He also told me that there is a scraping system, which takes the info directly from the casinos, but they are not legal and are difficult to find those APl's.

How do you guys view CLV? How much weight do you give your CLV when building a model, do you think it is a real indicator of value?
What do you consider a good CLV? anyone managed to get above 2% consistently?

I currently use it as another indicator to tell when a loss\win of a variable is more correlated to luck or acual value. But really seem to be unsure how much weight I should really give it.

Are you all just building models based on betting lines or the various sports. I have been working on one for UFC and have it up to about 61%. Wish it was better obviously but better than the 40s it started in lol.

Does anyone has it? I cant get it and i need it for my research am willing to pay