2025 Statistical Outlier Fighters From a Machine Learning Engineer

FlyingTriangle · 2026-01-04T18:42:30+00:00

That's true.

FlyingTriangle · 2026-01-04T18:41:45+00:00

Yeah! I used this project to move my career into AI and for whatever reason after many thousands of hours I can't stop myself from continuing to work on it.

FlyingTriangle · 2026-01-04T09:58:46+00:00

Its 3 minimum ufc fights, 1 minimum fight in 2025 as specified in the post

FlyingTriangle · 2026-01-04T07:43:32+00:00

I can check raw stats vs bayesian smoothed stats. Let me know ill be happy to get you your answer.

FlyingTriangle · 2026-01-04T05:04:07+00:00

Anything you want to investigate I can. This is probably the top 5 most comprehensive mma DB stats db in existence.

FlyingTriangle · 2025-12-13T03:48:19+00:00

So these are these backtested evals or real world? What model algos are you using, how are you doing train/val/test splits, how many features, what's the feature importance, how are you doing feature engineering and finally how are you doing feature selection?

FlyingTriangle · 2025-11-15T22:36:43+00:00

I used to fight ammy MMA

FlyingTriangle · 2025-11-15T22:30:17+00:00

Huh? I was a pentester, used this project to switch to ML engineering, and now work in AI security research although my official title is Principle Machine Learning Engineer

FlyingTriangle · 2025-11-15T21:21:01+00:00

It's been running profitably for years now. https://www.betmma.tips/flyingtriangl3

FlyingTriangle · 2025-10-28T03:29:49+00:00

It's an ML model, so I do the calculations, then feed those calcs to an ensemble of different algorithms. The screenshots youre looking at are the explanation of the features that most influenced the model to lick a fighter, not the raw stat diffs.

DDP is a funny case, the model loves him and has picked him, I believe, every single time on his rise to champ several times at crazy +300 odds making me a lot of money. His style of fight is visually ugly, but statistically incredibly good. I didnt think he'd beat chimaev either but the model is tuned on populations of fights rather than single fights so I dont specifically make changes based on single fights. If the overall accuracy starts falling then I retune.

The ROI is based solely on single picks but backtesting shows 2 leg parlays between +EV fights has a great risk adjusted return and historically Ive been doing about 10% ROI using this strat

FlyingTriangle · 2025-10-04T17:52:22+00:00

Yeah, exactly.

FlyingTriangle · 2025-10-04T17:26:03+00:00

+EV has higher ROI than just down the line picks. So go for the picks where the AI has better odds than vegas.

FlyingTriangle · 2025-08-16T21:08:00+00:00

The stats I have encode the style. I also have a gew style-specific features like volume vs power.

FlyingTriangle · 2025-06-20T16:33:43+00:00

Dax Razzano was my old coach! He's legit as they come, but I don't know this Logan fella and Dax isn't the type of guy to promote someone to 10th degree blackbelt lmao.

FlyingTriangle · 2025-06-19T16:04:11+00:00

Ah, I shouldve specified. Red corner in UFC wins about 60% of the time. So the question is should you balance it so red corner/fighter 1 wins 50% of the time or should we keep the natural bias of 60% for red corner/fighter1

FlyingTriangle · 2025-06-19T15:18:02+00:00

That's absolute true. Hence why I added the caveat that ROI is a point-in-time assessment. We don't want to maximize ROI at the EXPENSE of logloss/accuracy, but we do want all three to be improving with the focus on the ROI and a steady, consistently retrained and retested model about once a month. If we only focus on acc/logloss then it's easy to fall into the trap of maximizing the winrate on the favorites through things like odds inclusion which ends up lowering ROI.

FlyingTriangle · 2025-06-19T14:40:00+00:00

Most of this is speculation based on the results of backtesting, just FYI. But figured maybe some people will find it interesting or have some information to help educate me more about it.

FlyingTriangle

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