r/algobetting

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'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:

1. What would you consider a good / very good / excellent / achievable XI-overlap score?
2. 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?
3. 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.

I’m a college student building a soccer prediction model and finally tested it on a season I had kept completely untouched.

The forecasting model itself held up reasonably well, but on ~1,700 matches de-vigged Bet365 probabilities beat every model I tested on log loss/Brier/RPS, and all of my pre-frozen betting strategies ended negative. My main one finished around -12.5% ROI.

Right now I already use things like goals/xG, shots/SOT, home/away splits, Elo/opponent strength, form, rest/congestion, standings, league scoring environment, and promoted-team uncertainty. I also have a decent amount of player, lineup and goalkeeper data, but the historical player/lineup experiments didn’t add much and exact pre-match availability timing was a limitation.
I’m planning to stop optimizing on historical results and run the system prospectively with timestamped odds, injuries and confirmed lineups.

What I’m mainly wondering:
What additional data would you look for at this point?
Manager changes? Transfers? Better injury/suspension history? Player market values? Weather? Travel? Referees? Tactical/style data? More detailed event/possession data?
Is there any dataset you’ve found that actually adds signal beyond what the market already knows?
Or would you stop adding features and focus on prospective validation / finding where the model disagrees with the market for a defensible reason?
Trying to avoid just throwing more variables at it until something backtests well.

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?

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

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

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

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

Average odds 1.6886

Below are the latest 3 results added this weekend.

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

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

And the value picks only betting strategy results

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

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

100 confirmed value picks only results 

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

Average odds 2.8666

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

https://fitequant.com/results

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

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

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

Forecast review

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

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

I'm delighted with the all leans strategy.

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

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

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

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

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

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

Quick look forward to next week

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

https://fitequant.com/upcoming

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

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

Thanks, Dan

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

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?

Looking to round out my collection, looking for these three NHL seasons 22-23, 23-24, & 24-25. Please help if you know any good sources. SBR was my go to

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?

https://smp.betfair.com/www/sports/fixedodds/readonly/v1/getMarketPrices

For the last few months I've been building a pipeline that monitors local sports media in 100+ countries, ties every "X players out" fact to a specific upcoming fixture, and issues a signal with a concrete market before kickoff. The idea is simple: bookmakers are slow exactly where nobody watches, second and third divisions, women's and youth leagues. When a club in Peru doesn't pay wages and four starters refuse to play, the local press knows hours before the odds move.

Some honest numbers from the public archive (small samples, losses included): 61% winning bets over 36 settled, positive CLV, and 57% of bets beat the closing line. Every signal is archived with its delivery time and the final score, nothing gets edited after the fact.

The engineering bits that turned out hardest: multilingual extraction with anti-hallucination guards (the model can only pick from real quoted markets, it physically can't invent a number), entity resolution across 40 languages (homonym teams, women's and reserve squads are a minefield), and closing-line snapshots for CLV.

Biggest lesson so far: a strong news signal is not the same thing as a good bet. We only learned where the actual edge lives by replaying our own archive.

Happy to answer questions about the approach. I won't go into specific sources or the betting logic itself since that's the product, but everything else is fair game. Mods, if a link to the public archive is okay under rule 1, I'll drop it in a comment.

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.

Hey guys, apologies in advance if it’s against the rules to promote a site. Let me know and I can take this post down.

I’ve been using the odds api to detect +EV/Arb bets for MLB/WNBA and I wanted to share what I have so everyone else can also use it.

You can check it out here: https://oddspool.up.railway.app/
(THE MOBILE VIEW LOOKS LIKE SHIT RN IM SORRY)

The main idea behind it is that whenever a user ingests odds from the odds api, all other users can also see those odds.

You can input your own odds api key and ingest odds, or wait for other users to ingest odds. Then everyone sees the same +EV/Arb bets.

You absolutely do not have to input your own odds api key if you do not feel comfortable. I will personally ingest MLB/WNBA odds throughout the day so you can check the site periodically, and see what +EV bets are available. The hope is that more people than just me will ingest odds throughout the day so hopefully the odds don’t get too stale.

You can also track as many bets as you’d like for and see for yourself if it’s actually profitable before actually spending any of your own money.

WARNINGS:

\\\\- sometimes the odds api returns incorrect odds data which leads to bets incorrectly being flagged as +EV. I’ve done my best to flag such instances or not show them at all. For all bets I try to be very clear in how the +EV is being calculated so you can determine for yourself if it’s real or not.

\\\\- I would also be cautious for any live betting. The odds seem a little out of wack for that, and I have personally only used it for pregame betting

The ui is also a little jank so I’m working on that, but everything should be functionally sound, but let me know if anything is buggy.

Let me know what you guys think, or if you have any questions or suggestions.

Everything is currently free, but I’m eating some costs in terms of hosting the site, so I plan on eventually charging for certain things, but for now everything is completely free.

I also plan on adding more sports as it grows.

Thank you!

Hi everyone,

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

Hi everyone, after the Fbref data lockdown of January I made a post here offering a database with advanced football metrics and the response I received was overwhelmingly positive.

Since then I've been working hard on building an API incorporating an event feed and creating a pipeline that will convert the feed into SPADL format and serve meaningful derived metrics directly via API instead of scraping it and storing it in a database.

This API serves post match data only like lineups, xG xGOT per shot with coordinates, xT and VAEP per player, GCA, SCA, xGChains, passing networks, momentum pressure index and much more.

Feel free to check out my docs page at pitchapi.dev to read more about the service. The VPS i currently use to host this can only handle around 100 requests per second so please use this with some rate limiting in mind.

I would love your feedback on this and any suggestions about how to improve the overall experience.

Is circa sportsbook for live betting sharp?
I know they are supposedly sharp for pre game betting, but what about their live lines?

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.

Any Odds API recommendations for Soccer stats? Specifically ones that can provide Team Shots on Target?

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.

I’ve been building out STAATY.com and the UFC fight prediction model over the last 5 events logged has been at 70.8%.

Site is free to use and would love and feedback. Hopefully this is allowed. I’m just stoked to be hitting good numbers and kinda bummed I’m not a gambler lol.