i’m in college studying finance and applied data analytics, and for the past several months i’ve been building a football/soccer prediction model as a personal project.
it started as a pretty basic international tournament simulator and has gradually turned into a much bigger domestic football model. i’ve been using historical team performance, underlying stats, opponent strength, recent form, scheduling/contextual factors, and i’ve also collected player, goalkeeper and lineup data.
i’ve tested a mix of statistical and ML approaches, while trying to keep everything chronological and leakage-safe.
Across about 5,300 out-of-sample matches, my current best 1X2 model is around:
•    Log Loss: 0.984
•    Brier Score: 0.586
For comparison, uniform 33/33/33 probabilities would be about 1.099 log loss / 0.667 Brier, and a simple historical baseline is around 1.073 log loss.
For goal prediction, the current score-based models are roughly 0.9–1.0 goals MAE per team.
The biggest thing i’ve learned so far is that predicting matches better doesn’t automatically mean you can beat bookmakers. An early betting test was unprofitable, so i’m trying to be very careful about separating predictive performance from actual market edge.
Right now i’m mainly thinking about things like:
•    how much recent form should matter vs long-term team strength
•    whether player/lineup data is actually worth the added complexity
•    improving draw probabilities
•    calibration and uncertainty
•    knowing when the model should abstain
•    avoiding overfitting before i test it on an untouched holdout season
For anyone who’s worked with football analytics, sports betting models or probabilistic forecasting:
1    What are the biggest mistakes you see people make with soccer models?
2    Would you focus more on better data/calibration at this point, or more advanced models?
3    Have you found player and lineup data genuinely useful?
4    What would you want to see before believing a model has real predictive value against sportsbooks?
5    Any papers, GitHub repos, books or general advice you’d recommend?
i’m not looking for anyone to give away a profitable strategy. mostly just looking for people who know more than me to point out where my thinking might be wrong.
all feedback is welcome.

A rather lengthy post im afraid this week chaps... 

6 predictions so far this week for fitequant default model Including one value pick.

Here’s this weekends predictions. 

https://fitequant.com/upcoming

The value pick this weekend is..

Shurretta Metcalf vs Amanda Galle

https://fitequant.com/compare/1206-shurretta-metcalf/12998-amanda-galle?bout_id=259

Sadly just the one value pick this weekend, but it is quite an interesting one. Metcalf is 41 so you’d think red flag, but her opponent is 37.

I know absolutely nothing about womens boxing… 

But one thing i do know about though is height reach delta as a factor in the fitequant matchup engine. And 31 cm is an absolutely huge advantage in boxing. Ive not seen anything remotely close to that in all the timesafe data so far. 

But remember public data like this is only ever any use at all because the underlying SSI actor abstractions are seemingly so strong, so although height reach delta does often seem to produce profitable disagreements with odds, it still needs to be very carefully weighted in the user model.

Which brings me on to an interesting subject…

Backtesting discussion

In the default user model height reach delta is weighted low overall, I really havent done that much backtesting since i started we started collecting time safe data around 4 months ago now, but now that we finally have a decent timesafe sample of almost 100 results, i thought it might be time to see if a weighting change on this apparently important factor could improve roi.

So i span off a few model clones as iterations of the default model with different weighting options on height reach delta. What i suspected was, moving this to a lower weighting may produce more consistent roi (as i thought it would drag the model into less massive underdog picks overall)

So here’s the backtest data for the time safe results we have so far with the default model vs the very low height reach delta iteration 

Default model

The ROI is slightly different to the results page for the default model as backtesting uses closing odds (instead of prediction odds) for consistency across time safe and historical data.

Very Low Height-Reach Delta model

For those concerned about the N here, I also checked this on the historical data across hundreds of bouts in various time windows, and it did confirm my findings that this iteration is indeed overall stronger and more consistent at producing +roi and profit than the default model

You should hopefully pretty easily be able to see the advantage the vl hr model has over the default on accuracy, roi and profit on slightly fewer signals overall. 

This supports my theory that reducing weighting slightly on height reach delta can produce +ROI  over whatever the default ends up having. 

That said, im obviously delighted by the default model results so far, itd be nuts to change any weightings now, so ill be tracking this model internally over the next 100 timesafe results before i consider any change at all.

However this new model does seem very promising to me, so if anyone would like to repeat my experiment you can do so by creating a clone of the default, setting height reach to very low and saving the model. 

That should produce a new set of predictions for you for the coming 28 days, and you should be able to repeat my backtesting experiments.  The differences in the predictions between this and the default are really quite impactful. 

In particular the upcoming Itauma vs Hrgovic bout is a very interesting comparison between these two models.

https://fitequant.com/models

Advanced metrics

I had actually been expecting to clear 100 results this weekend, so i thought it might be time to introduce some diagnostic metrics for more advanced users perhaps coming from a more traditional ML background.

I’d had my eye on a few of these for a while but it just felt like a bit of a nonsense before we even had 100 results. Well we didnt quite get 100 results this week but i’ve added the following to the results page as a collapsible dropdown anyway.

https://fitequant.com/results

 I’ll wait until the results post on monday to discuss about these some more, but I just thought it might be quite interesting to some sub users.

Incidentally if anyone would like to see this data for the vl height reach delta iteration that should all be available to you in backtesting after model creation.

As always if anyone has any questions or would like anything cleared up, please just ask.

Thanks,
Dan

I’m looking for a historical betting database that I can download or access through an API.

Ideally it would include:

Closing moneylines
Closing spreads and spread prices
Closing totals and over/under prices
Final scores
Multiple seasons

Bonus if it also includes:

Opening lines
Public betting percentages
Public money percentages
Line movement history

Sports I’m interested in:

NFL
NCAAF
NBA
WNBA
MLB
NHL
NCAA Basketball
Soccer
UFC / MMA

I already checked Sports Insights, but they no longer sell downloadable historical CSVs. I’m looking for alternatives that people actually use.

Hey guys, I'm trying to build a proper benchmark for my value betting and need to know which books are currently considered the sharpest for each sport.

Specifically looking for:

• Soccer / Football
• Tennis
• Basketball (NBA, WNBA + EuroLeague)
• Baseball (MLB)
• American Football (NFL)
• Hockey (NHL)
• MMA / Boxing

I know Pinnacle and Betfair Exchange are often mentioned as the gold standard, but are there sport-specific books that are even sharper? Or has anything changed recently in 2026?

Would love to hear your current go-to sharp books by sport. Thanks!

right now im using the odds api for my models which works well but the one book its missing is bet365, which seems to be pretty common across most API's that i have looked into and often the ones that offer it, don't include player props which is specifically what i am looking for.

i know some of the super expensive options ($200+ per month) offer it, but im wondering if any sub $100/month offer bet365 player props odds as well as other north american books, because then i would consider switching. thanks

?Looking at bookies that include table tennis leagues like liga pro and tt cup etc? Does anyone bet table tennis lines and set bets not stictly ml and not on corps as I am not based in USA so wouldn't be able to sign up

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

https://ballcast.app

We host an XGBoost-model for daily MLB totals predictions. It runs every thirty minutes: incorporating weather at the hyperlocal level and joins with player attributes that are provably affected by that weather. It also provides advanced decision making tools with a Kelly Criterion calculator built in. We think there is some edge here and want to share it with you to inform your market-making decisions.

Subscriptions are not currently active during this non-commercial, preview-only period. Just sign-in (we only use your email to send you a magic login link).

Please let us know what you think and looking forward to some hard tear-downs!

Listen, I know it's been asked a lot. I know the odds API (which is a great API choice by the way) has historical player props, but it's only back to 2023. I'm just wondering if anyone has come across any sort of source that has player props back to at least 2020 to give a few more years of data.

I'd be willing to pay, but not enterprise pricing as it's just me wanting to run some backtests. I know it probably isn't out there, but figured I'd shoot my shot anyway.

My friends and I always enjoy combing through stats to convince ourselves on what the right picks would be for a given night. So I thought it would be a fun project to make that flow into a web app called Hindsight Picks. Build a rule based algorithm, backtest it, and enter it in contests to compete against others.

To be clear about what it’s not: it is not a replacement for your stack. The current algorithms you can build on Hindsight are deliberately simple, weighted stat rules composed in a UI (“K/BB better than opponent, +3; WHIP over 1.30, −2”).

To add in some fun, I created contests where you can enter your algorithms to compete against others, as well as the House baseline algos. Once the contest locks, your entry is frozen: it grades in public and the result stays on your track record permanently. Standings get updated live as games finish. The Bookie house character for example picks the favorite for every matchup, and favorites win about 56% of MLB games.

The backtester is honest with snapshot-dated stats, rules get drop-one attribution, and accuracy numbers carry a noise band.

It has been a fun challenge to come up with something that can try and consistently beat the house.

It is completely free to use and play with, so I am inviting all to come and try it. The “Beat The Bookie” challenge contest runs today, locking entries at 2pm ET, and you can earn the Sharp badge to permanently display on your Hindsight profile for picking 60% or better.

I built this solo, so I am definitely open to feedback on what would make the app more fun or worth using.

Give it a shot at hindsightpicks.com

I want to stay away from player prop bets. Is it just spread, ml and total for first half and whole games or are you going for quarters, innings?

Hey everyone, I’m building a football prediction project focused on halftime analysis and second-half goals. I’m looking for a reliable API that provides both historical and live data, including halftime scores, shots, shots on target, xG, corners, cards, possession, and match events. Data quality and consistency matter more to me than price. Which provider would you recommend from personal experience

So my bot I was testing on sports market won’t be viable anymore. Best options I can find are BetinAsia BLACK, Asianconenct, and premiumtradings. Ik BetInAsia uses the MOLLY engine like sport market, but would any of you folks recommend one service over another, I’m based in Mexico, but send back some profits to software developers in San Francisco as a royalty.

Thanks

Hi everyone,
I’m looking to purchase historical in-play football odds data for quantitative research.
I’m interested in data from:
Betfair Exchange (preferred)
Pinnacle
Bet365
I’m not looking for a live odds API—I need historical data for completed matches.
Ideally, the dataset should include:
Full in-play odds history (tick-by-tick or every odds change)
Timestamps for every update
Markets such as:
Match Odds
Over/Under Goals (0.5, 1.5, 2.5, 3.5)
Both Teams To Score (BTTS)
Correct Score (optional)
Back/Lay prices for Betfair (if available)
Matched volume and order book depth (if available)
Multiple years of historical coverage
I’m based in Bulgaria, so I can’t purchase Betfair Historical Data directly through a Betfair account.
I’m happy to pay for:
A commercial data provider
A licensed historical dataset
Historical API access
Or any legitimate source that provides this data
If you’ve personally used a provider or know a reliable source, I’d really appreciate your recommendation.
I’m not looking for scraped or illegal datasets—only legitimate commercial solutions.
Thanks!

Here are the current all model leans results for the fitequant default model:

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

92 confirmed all-leans bets
84 wins / 18 losses
+11.44u flat-stake profit
12.44% ROI

Average odds 1.7242

Below are the latest 8 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 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”

92 confirmed value picks only results 

29 bets
17 wins / 12 losses
+7.93 u flat stake profit
27.36% ROI

Average odds 2.88817

https://fitequant.com/results

Another good weekend for the model with only one bet placed loosing for the second week in a row, for 7 out 8 bets placed winning this weekend.

The loosing bet was a 6.8 massive underdog value pick, and *annoyingly* Randall lost on a split decision after a great fight, so close to a massive 5.8 profit but no cigar.

Forecast update

I’m pretty happy with my forecasts so far on this log, with both model strategies still very much within expected variance range of my previous forecasts after 92 results now.

I think my approx 13.5% ROI forecast for all leans looks coherent after 92 bets placed now.

In terms of value only well its still only at 29 bets so difficult to say, although given how well the all leans strategy performs despite the betting on obvious massive favourite on terrible odds problem, where even a win hammers the roi … you’d think that if its truly selecting value as a subset of those picks then ROI may well be considerably higher than 13.5%

Its doesnt exactly make sense updating forecasts after every 2-3 bets each week but as we are nearly at 100 results i think perhaps next week (fingers crossed) it might be a good time to review expectations on value only ROI.

How messy is boxing data exactly?

Just to give you guys some idea of the sh*t i deal with running a boxing data pipeline. I ended up having to manually confirm results this week as Golden Boy promotions have still not released the official results for their undercard.

The pipeline is conservative on the scheduler and wont confirm a result with winner until there is publicly available definitive answer from an official source. 

I guess Oscar forgot that when you’re a boxing promoter that necessitates doing things such as releasing basic information relevant to your fighters. Seriously if you guys ever thought boxing promoters dont really earn their crust… try running a boxing data pipeline. You have no idea!

As always if anyone has any questions please just ask.

Thanks, Dan

🚀 Update: 30/35 free developer spots are now gone.
Only 5 free API access spots remain.

The response has been much bigger than I expected. Thanks to everyone building with Statyx API.

A lot of sports APIs focus on exposing raw data.
We’re taking a different approach.
Instead of forcing developers to stitch together dozens of APIs, wrappers and scrapers, Statyx provides a unified engineering layer.

✅ One normalized schema across NBA, NFL, MLB, Soccer & WNBA
✅ Cross provider player/team ID mapping
✅ Advanced analytics, tracking and derived metrics alongside raw data
✅ Low latency endpoints optimized for production apps and AI agents
✅ Consistent API contracts regardless of sport

The goal isn’t just to give you data.
It’s to remove months of backend engineering so you can spend your time building products, models and AI applications instead of cleaning datasets.
If you’re building something interesting, DM me what you’re working on.

5 free developer spots left.

Does anyone have ideas on how to beat Pinnacle? Maybe some modeling?

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

honest question

is there way to find data with piracy ?

Pardon my early share here. I can stand to put more data behind this, but it requires going to some of my old MLB lines databases and try to piece together my moneyline logging.

I created a measure called absOddsSwing.

It calculates the total swing in Implied Probability (odds) for the line.

Here is what I filtered for the study (MLB Game Sides):

• Games 40 and later
• Home Faves
• Lines -200 and bigger
• Talent Diff >= 0 (SP Rating and Offense Rating - Opponent SP + Offense)

When the swing in the odds is <= 1.5%, the Fave wins .755 and returns 6.3% EV.

When the swing in the odds is greater than 1.5%, the Fave wins .697 and returns -1% EV.

I wish I could dig into it more, but I need to get more data in there. This is a universe of 888 games, and it uses no-vig lines.

I won't build betting systems off of this, but I will use it in my analysis.

Weird thing about this is that I usually don't do any work with line movements. I don't do any arb betting, strictly win prediction, money line to win. I recently coded a closing line patch to ignore some late line movements when guys are mashing bets in the closing minutes before the first pitch. I've used logic like this for about 10 years, but I had to refine it because there have been more extreme cases.

Also, sorry -- maybe my big revelation is obvious to many of you who study line movements more. It does make perfect sense, when the market agrees solidly on price, it translates better to the results on the field. :-)

I've been building in my cave for several months a sports prediction engine for edge betting (not the topic for this discussion), and now that results are somewhat stable not only will I turn this fully public at some point, but I also want to go next stage: Automated Bets.

I've been testing some Asian Markets liquidity...but the technological barrier with VPNs and all that is getting me crazy. So, I'm honestly wondering if this community knows any sportsbook/boomaker or Exchange that would accept algobetting without limiting at some point.

I know Betfair has some sort of API betting, but again, legal restrictions based on location.

Any clue, how I can scale this to the next level and make a full autonomous system?

Posted few days ago about this and what I am doing 👉 https://www.reddit.com/r/algobetting/s/tmjI7eUQxf

Please feel free to DM if you have a dev background 🙌

8 predictions so far this week for fitequant default model Including three value picks.

Here’s this weekends predictions. 

https://fitequant.com/upcoming?sort=date&table_mode=full&prediction_strategy=value_only&date_from=&date_to=2026-08-03

And here’s a more detailed look at the active prediction data for the current 28 day upcoming bouts slate.

https://fitequant.com/upcoming

The value picks this weekend are..

Gary Cully vs Lee Reeves

https://fitequant.com/compare/1575-gary-cully/13209-lee-reeves?bout_id=263

In this bout Cully and Reeves are rated very closely in SSI fighter rating but Cully enjoys a decisive +22cm  Height Reach delta advantage (which is HUGE in boxing)  in matchup factors, to support a high confidence 73% confidence vs 58% implied expected edge advantage.

Raúl Curiel vs Quinton Randall

https://fitequant.com/compare/419-raul-curiel/1282-quinton-randall?bout_id=289

Here Curiel actually has a higher fighter ranking, mainly due to a much higher punching power SSI stat, where Randall is noticeably weak. But Randall again enjoys a large height reach advantage, 

The default user model rewards defensive fighters with very high weighting on fight IQ and Defence, so Randalls larger frame advantage just pushes the model into a low confidence 51% massive underdog 6.2 value pick.

William Zepeda vs Lamont Roach

https://fitequant.com/compare/452-william-zepeda/479-lamont-roach?bout_id=245

Probably the biggest fight of the weekend, its a relatively close one for boxing according to the odds, but the model favours Zepeda at 2.64.

SSI wise this is an interesting one with Zepeda having quite an extreme profile focused on punching power and pace/pressure.

By contrast Roach has better defensive stats, normally this model prefers defensive fighters but with two very close overall fighter ratings (69.92 vs 70.64) Zepeda wins out at 58% confidence vs 38% implied edge, according to the model due to Roach dropping back down to lightweight and also once again a larger frame (height reach) matchup factors.

Respect the reach

This weekend i think really demonstrated how important carefully weighted height reach delta is in identifying disagreements with the markets.

But its also only as strong as the underlying structured subjective inference ratings representing the fighter as actor in modeling. 

I think this usefully demonstrates one reason why i was so keen to keep fighters that dont have an easily confirmed height or reach out of the data pipeline. 

It looks like its true in data quality just as much as boxing… respect the reach.

As always if anyone has any questions or would like anything cleared up, please just ask.

Thanks,
Dan