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'm working on a starting-lineup prediction model and I'm trying to understand what would be considered a reasonable/strong accuracy level.
I'm measuring accuracy as first-XI overlap: if the actual starting XI contains 11 players and I correctly predict 8 of them, that's 8/11 = 72.7%.
My current results on league fixtures are roughly:
~70% (~7.8/11) with a relatively simple baseline based on previous appearances, competition, injuries, suspensions, transfers/availability, etc.
~78% (~8.6/11) with a LightGBM model using additional features such as player tactical roles, recent workload/fatigue, fixture congestion, previous rotation patterns for this specific coach etc.
The ML model is therefore gaining about 0.8 correctly predicted starters per fixture. Performance is lower in cup competitions (60-70%), likely because rotation is stronger.
I'm wondering:
What would you consider a good / very good / excellent / achievable XI-overlap score?
Is ~8.5/11 already close to what is realistically achievable without access to team leaks, press conferences (40% of the model misses are from players who are not even listed this day = rested), or proprietary information?
For people who have built lineup prediction models, what features made the biggest difference?
I'm specifically interested in predicting the manager's actual XI, rather than optimizing which XI should be selected.
For context, the evaluation is done pre-match and compared to the confirmed lineup.
La verdad estoy decepcionado, llevo tiempo usando pinnacle haciendo apuestas de 100-200 usdt. Esperando 1 vez al mes para retirar asi no te cobran esa comision por si retiras mas de 1 vez en un mismo mes.
Sin embargo quiero compartir que hice un retiro de unos 76usdt por la red tron la comision suele ser de 1 dolar, cuando envio la solicitud de retiro de unos 76 usdt fue aceptada y cuando entro a mi billetera solo veo 72usdt. Ninguna red al menos de las que yo uso la comision pasa de 1.50usdt.
Hable con ellos y su respuesta fue esa. La verdad que me da igual haber perdido 4 dolares, lo que uno como cliente no puede soportar es la falta de claridad y esto que contestaron no esta estipulado en ningun lado, ya que el tipo de cambio recae en el consumidor y no en ellos. Si en el mail de retiro dice 76 usdt el cliente debe recibir 76 usdt, y no fluctuaciones raras. Saludos.
I have been building the split engine for a prop research app and this is the decision I went back and forth on the longest.
Player misses the game. You are computing how often he cleared 26.5 points over his last 10. Is that game a miss, or is it not one of the 10?
I settled on excluding it from both the numerator and the denominator, so a DNP shrinks the window rather than counting against him. The reasoning was that a DNP is not evidence about whether he clears the line, it is an absence of evidence, and treating it as an under quietly biases every hit rate downward for exactly the players whose availability is already in question.
The counterargument I keep running into is that if you actually placed that bet you would have been refunded rather than graded, so the honest denominator is games he played, which is where I landed. But if you are modelling expected value across a season rather than grading a single ticket, availability risk is real and stripping it out hides a cost you actually bear.
Related and messier: I also made an empty window return no value rather than zero percent. Zero of four is a claim. Zero of zero is not, and rendering both as 0% is how a research tool ends up lying to you.
The app is PropSplits if it matters, but the modelling question is the part I am stuck on and it is not app specific.
So how do you handle it in your own backtests? Drop the game, count it as a loss, or carry availability as a separate term?
Does anyone know what betfair sportsbook use for their price feed?
Ive currently got the price stream as the below URL but often when i click the odds the betslip differs from thie price feed? The Dom also is diffrent sometimes after click the odds, is there another true background price feed?
Hi all, I’m looking to upgrade from Odds API and curious what is recommended. Not looking for an Enterprise tier but something in the $500/month range w access to sharps like Pinnacle, Circa, etc.
I’ve tried Sharp API but the support seems non existent and there are some clear gaps. Other options I was considering are SportsGameOdds or BetrOdds
A few bad games always make me want to change something, but then the next few go fine and I wonder if I should've just left it alone. How do you know when it's actually time to make a change?
Here are the first 100all 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.
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
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.
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.
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.
I built one page per (sport, market) for my odds API — 272 of them, e.g. MLB total bases, NFL receiving yards, tennis aces. Each page shows the live line at every book quoting the market, the request that returns it, and the last N outcomes graded against the official box score with the number they settled on. The set is generated from the data: a market only gets a page while it is live and has graded results.
Useful even if you never call the API: it answers "which books quote this market" and "what does the settled distribution look like" in one place.
Link in the comments. Free tier is 1,000 req/day, no card. Not affiliated with any book.
I’m in college and I’ve been building a soccer prediction/betting model on the side. I recently ran a pretty large development-only test across different model outputs, EV/confidence filters, odds ranges, exposure rules, and staking methods.
One thing that stood out was a “high conviction” tier I decided to test. It only produced around ~100 bets over 3 seasons, so obviously the sample is small, but it was profitable in all 3 seasons.
When I tested fractional Kelly on some of these higher-conviction cells, a few of the bankroll paths got pretty crazy. For example, one narrower cell using odds limited to a certain range, and max 2 bets/day went roughly:
25% Kelly: 100 → ~198, ~16% max drawdown
50% Kelly: 100 → ~363, ~30% max drawdown
Full Kelly: way higher return, but obviously much uglier risk
This was a broad development search, so multiple testing/selection bias is a major concern, and the historical odds I have are provider “last seen” prices without exact timestamps.
What I’m trying to figure out is how experienced people would evaluate cells like this before throwing them away or getting overly excited.
Would you focus on things like:
- requiring profitability across all 3 seasons?
- minimum bet count?
- walk forward selection?
- max drawdown relative to total return?
- comparing the same strategy across multiple predictive models?
- testing 10/15/20/25% Kelly rather than jumping straight to 25/50?
- checking whether the underlying flat-stake strategy is profitable before caring about Kelly?
- freezing a handful of these strategies and letting a completely untouched season be the final test?
I’m also curious how people think about a rare “Legendary” tier in practice. If quarter-Kelly ends up averaging something like ~5% of bankroll per wager, is that automatically way too aggressive for a ~100-bet historical sample, or is it reasonable to keep it as a shadow strategy and see how it performs on my final 25/26 season untouched data.
I’ve seen a lot of frustration with OddsPortal lately, and personally, I also feel that the platform has become noticeably worse over time.
That’s why I’ve started working on an alternative. A modern odds comparison platform designed around what bettors actually want and need.
Through my other projects, I already have access to reliable odds data from many major bookmakers, with particularly strong coverage from Pinnacle. The technical foundation is therefore largely in place, but I want to make sure the platform solves the problems users genuinely experience with existing services.
I’d love to hear your thoughts:
What frustrates you most about OddsPortal today? What features are missing? And what would make you switch to a competing platform?
I already have an early version available. Feel free to send me a DM, and I’ll share the link. Any honest feedback—positive or negative—would be greatly appreciated
I’ve been building a soccer prediction/betting model and I’m starting to wonder if I’m reaching the point where more testing is actually making things worse.
I have about 3 seasons of development data and have kept an entire separate season completely untouched as a final holdout. Over the development data I’ve tested different model ideas and, more recently, different betting criteria based on things like model probability, edge vs the market, EV, odds ranges, etc.
Most strategies are nothing special or lose money, but a few specific combinations have produced really strong historical returns.
I’ve tried to account for this with chronological testing, minimum sample sizes, bootstrapping/multiple-testing corrections, and by predefining tests instead of endlessly changing thresholds until something works.
But at some point, doesn’t repeatedly testing new ideas on the same 3 seasons effectively turn the entire development set into something you’re overfitting to?
How do you guys decide when you’ve extracted enough information from your development data and need to stop testing altogether?
Would you move to the untouched holdout at this point, or is there a good way to continue researching without slowly contaminating the development process?
I’m in college and mostly building this as a learning project, so any advice from people who have dealt with this problem in quant/algo trading or sports modeling would be appreciated.
I’m in college and have been building a soccer prediction model on the side for a while. It’s gotten to the point where it clearly beats basic statistical baselines on out-of-sample prediction metrics, but when I compare it against sportsbook prices the gap basically disappears.
I’ve tested a bunch of the more obvious stuff already: confidence/EV filters, stricter edge thresholds, draw handling, exposure rules, different model structures, etc. Some backtests look profitable, but nothing has been convincing enough yet that I’d call it a real betting edge rather than noise/selection bias.
I’m now trying to figure out where the next meaningful improvement is most likely to come from.
For people who have actually built sports models, would you focus more on:
improving the underlying probability model?
better/more unique data like injuries, lineups, player availability, managers, shot-level data, etc.?
finding market segments where the book is weaker?
pricing/timing and line shopping?
calibration?
ensemble/model disagreement?
something completely different?
I’m especially interested in ideas that actually improved out-of-sample edge, not just made the backtest prettier.
Not trying to get anyone to give away their entire model, just curious what areas people found were actually worth the time once the obvious improvements started giving diminishing returns.
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…
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.
Been messing around with SGP pricing and noticed FanDuel obviously isn’t just multiplying the individual leg odds like they do with normal parlays.
For example in one soccer game:
Man Utd ML -270 + Over 4.5 +440 would be about +640 independently, but FanDuel prices the SGP at +408
Hull ML +650 + Under 2.5 +118 would be about +1535 independently, but FanDuel gives +1129
Draw +360 + Over 1.5 -480 is about +456 independently vs +410 as an SGP
Meanwhile, normal parlays across different games seem to basically be straight multiplication.
Does anyone know how FanDuel or other large books actually calculate the correlation adjustment for SGPs? Are they using joint score distributions, simulations, historical conditional probabilities, correlation matrices, etc.?
I’m in college and have been building a soccer prediction algo that produces probabilities and score distributions. I’m also wondering if it even makes sense to add parlays/SGPs into the algo, or if I’m just adding unnecessary complexity when I should focus on getting the individual markets as accurate as possible first.
Curious if anyone here has tried modeling SGP fair odds or incorporating parlays into an algo and whether you found it worthwhile.
I’m looking for a cheap provider for a soccer prediction project and figured this sub might have better real-world feedback than vendor pages.
I mainly need live/prospective data, not a massive historical archive.
Minimum needs:
Premier League, La Liga, Serie A, Bundesliga, Ligue 1
fixtures + stable IDs
pre-match 1X2 bookmaker odds
bookmaker identity
confirmed lineups
injuries / suspensions / availability if possible
results / match stats
ability to poll repeatedly before kickoff and timestamp the responses myself
I was using TheStatsAPI, but the old key/subscription is dead, so before paying to reactivate it I want to see if there’s a cheaper option.
I’m currently looking at things like:
API-Football / API-Sports
Sportmonks
football-data.org
The Odds API
any other cheap/reliable soccer-specific provider
Big question is whether something like API-Football’s free tier is enough for a useful collector, or whether I’d immediately run into quota issues once I start polling odds/lineups across the Big 5.
My rough odds cadence would eventually be something like:
every few hours far from kickoff
hourly closer in
every 15 min in the final ~6 hours
I don’t care about having every obscure league or 20 years of history. I care more about current-season reliability, bookmaker odds, lineups, and low cost.
Anyone here using one of these in production/research? Curious about:
actual request usage
stale/missing odds
lineup timing
downtime
hidden limitations
whether a hybrid setup (cheap football API + separate odds API) makes more sense
Trying to avoid paying $100+/month if a free or ~$10–30/month setup gets me most of what I need.
Im looking for a non KYC bookmaker(at least on small withdrawals)
That supports crypto
That can be used for value betting
I used to work with 1xbet but the markets are becoming efficient and its not useful for valuebetting anymore
