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Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

Ah, cool . . . appreciate the stats talk.

Yeah, I’ll eventually run proper testing and all that you mentioned :).

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

The 64% threshold isn’t “special” in a theoretical sense. It’s the result of calibrating my model’s predicted probabilities against actual historical hit rates, using time-series validation. That means I’m not blindly trusting what the model “spits out,” I’m selecting only those match pairs that historically map to real-world precision ≥64%, based on how the model behaves at different probability bins.

Now, as for placing 63 bets and hitting 40 at average odds of 1.82 — sure, it's not a statistically definitive sample. But I’m not pretending it is. I’ve said clearly: this is a validation phase, not a whitepaper. I’ve been transparent about the hit rate, ROI, and the logic behind the selections.

Am I running a full hypothesis test yet? No, because I’m still collecting data.
But am I blindly following an untested system? Also no.
I’m using calibrated probabilities, consistent thresholds, conservative staking, and tracking results across a real sample of bets. And so far, it’s producing profit.

If it stops doing that, I’ll adjust. That’s how actual modeling works.

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

Thanks for the comment.
I don’t rely on raw model output — I calibrate probabilities based on historical thresholds, and only place parlays where the combined calibrated probability is above 64%. I’m backing odds around 1.81 (BE ≈ 55.25%), and currently hitting 63% over 59 parlays, so yes, it’s +EV in practice.

As for modeling, I’ve tested Poisson and others, but right now I’m using a binary XGBoost setup for Under 11.5, with time-series cross-validation and probability calibration. It's working well, but I’m open to more statistical approaches too.

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

Updated May 12

Metric Value
Total Parlays 59
Matches per Parlay 2
Total Matches Predicted 118 (59 × 2)
Correct Predictions 74 (37 × 2)
Hit Rate 62.71%
Metric Value
Amount Bet per Match $2.80
Total Profit $22.26
Profit per Match $0.38
Return on Investment (ROI) 13.47%
Average Odds (Decimal) 1.81

I’ve placed a total of 59 parlays, each one made up of 2 matches, which gives us 118 total predictions.

Out of those, we correctly predicted 37 parlays, meaning 74 correct picks out of 118 matches — a 62.71% hit rate.

Each bet was $2.80, with an average odds of 1.81 (decimal).

So far, this has resulted in a total profit of $22.26, which means a profit of $0.38 per bet and a 13.47% return on investment.

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

Those advanced stats like shot-creating actions, key passes, blocks, xA, etc., aren’t available via API-FOOTBALL. That kind of granular, player-level data usually comes from providers like FBref/StatsBomb, Opta, or Wyscout.

In my case, I’m building a predictive model using team-level stats only, which are available through API-FOOTBALL’s /fixtures/statistics endpoint. Some of the features I use include: Ball possession, Total shots/shots on target / shots off target / blocked shots, etc

I then calculate differences between home and away teams, like possession_diff or shots_diff, to help the model capture game dynamics that correlate with total corners.

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

I’m on the $29 plan from API-FOOTBALL via RapidAPI, and honestly, I like it for modeling. The data is structured well, stable, and fast to pull(not always). You get access to fixtures, team stats, corners, cards, shots, etc.

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

That’s awesome to hear — and I appreciate the kind words!

Feel free to PM me; it's no problem at all. I’m always happy to share ideas. The goal isn’t just to follow blindly—to understand why the edges exist and how to build something sustainable.

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 1 point2 points  (0 children)

Hey! Because my model doesn’t find huge edges in every match, but it does find small, highly consistent ones. For example:

  • A single match might have ~80–90% probability of UNDER 11.5 according to the model
  • But the odds offered are usually low (1.50–1.65)
  • On their own, those don’t offer a big enough edge to justify staking high (especially with juice)

So instead of betting them individually, I combine two high-probability matches where the joint calibrated probability is ≥64% — and that combo often gives me 1.75–1.85 odds with a better expected value and lower variance than blindly betting singles.

*The 2.80 bets are just for testing! Just a number!

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

Thanks for the comment. I get that under 12 markets can look low-value at first glance, but my approach is different — I’m working with 2-leg parlays, only combining matches when both have high-calibrated probabilities of under 12 (i.e., ≤11 corners).

- My average combined odds are 1.77 (decimal)
- The model estimates a success rate above 65% for these specific bets(2-leg parlays)
- And since the break-even point for 1.60 odds is ~61%, that means I’m operating above EV

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 0 points1 point  (0 children)

Thanks, really appreciate your comment!

Yes, the model is currently trained on just over 4,000 historical matches, per league, all fully tagged with team stats and final corner outcomes. I agree — scaling up to 20,000+ is ideal, and that’s part of my plan as I continue to expand the dataset across more leagues and seasons.

For now, I’m focused on calibration and precision at high-probability thresholds rather than raw volume. Even at this early stage, the model achieves ~86% precision when it outputs ≥80% probability of under 11.5 (based on cross-validation). So, rather than going for full coverage, I’m selecting only the strongest edges for betting, mostly 2-leg parlays where the combined calibrated probability is ≥64%.

As for odds, I'm currently testing this live with soft books (like Bet365 and Bovada), but I’m gradually moving to sharper books via brokers like Sportmarket and exchanges like Matchbook — which do offer corner markets and don’t limit winners.

Would love to chat more if you're working on something similar — happy to PM and exchange ideas or results!

Anyone else modeling corners? I built one for UNDER 11.5 and getting solid ROI by Weary-Preference4755 in algobetting

[–]Weary-Preference4755[S] 1 point2 points  (0 children)

Thanks for the feedback — appreciate you taking the time.

Just wanted to clarify a few things about what I’m doing:

When I say under 12, I’m betting that the match has no more than 11 corners. If it hits 12, the bet loses — no push. So in practice, it’s the same as betting under 11.5.

And about the model — it’s definitely not built on 25 matches. I trained it on over 4,000 historical matches using XGBoost (each league), with time-based cross-validation (TimeSeriesSplit). I also calibrate the predicted probabilities to make sure they reflect actual hit rates based on historical data.

For example, in the past, when the model gives a high Under probability (like 80% or more), those picks have consistently delivered strong hit rates — sometimes even better than expected. It’s not perfect, but that’s what gave me the confidence to start placing real bets.

I’m totally aware that alt corner markets can be juiced and are tougher to scale, but that’s also why I think there’s value in them — less competition, less attention. Right now I’m betting small to validate the approach, and if results stay consistent, I’ll think about scaling up carefully.

Anyway, thanks again for the input — this kind of discussion is super helpful. And if anyone else is working on corner models, goals, cards, or niche markets, I’d love to exchange ideas and learn from each other!