About
How the ratings work · How the predictor works · What's coming next
How ELO Ratings Work
Every fighter in the database starts at 1500 — a neutral baseline meaning "we have no information on you yet." From there, ratings move based on results. Beat a highly-rated opponent and you gain a lot. Grind out a split-decision over a low-rated fighter and you gain much less.
The core idea: your rating reflects the quality of your wins and losses, not just the number of them. A 20-fight veteran feasting on journeymen might be rated lower than a 7-fight prospect who's already beaten two ranked opponents.
[ Finish Type Matters ]
A knockout or submission is worth more than a unanimous decision, and a unanimous decision more than a split. Finishing a fight is a stronger signal of dominance. Fighters who consistently finish get more credit.
[ New Fighters Move Faster ]
Under 5 UFC fights = provisional. Each result moves the rating significantly more until the system has enough data. This lets it calibrate quickly on new talent rather than spending years slowly nudging a number.
[ Title Fights Carry Extra Weight ]
Championship bouts have bigger impact. The stakes are higher, the competition certified elite, and the system treats those results accordingly.
[ Inactivity Decay ]
A rating earned two years ago and undefended isn't worth as much as one earned last month. Fighters inactive for 12+ months see their rating drift back toward 1500. Not a punishment — an acknowledgment of uncertainty about what they currently are.
[ Confidence Scores ]
Every rating has a confidence score. High confidence = recently active with enough fights. Low confidence flags provisional fighters or those deep in a layoff. The number is still informative — treat it with appropriate skepticism.
How the Fight Predictor Works
A machine learning model trained on historical UFC fights. Given two fighters, it outputs a win probability for each — not a gut feeling or a narrative, just what the data says has historically happened when fighters with similar profiles match up.
[ Training Data ]
Trained on UFC fights through end of 2022, tested on 2023 onward — fights it had never seen before. That temporal separation is how you check whether a model learned something real versus memorizing history.
[ 37 Input Signals ]
Striking output (SLpM), striking absorption (SApM), opponent quality, Elo rating, recent form (last 5), finish rate, days inactive, age, reach, stance, and takedown stats. The Vegas opening line gets a seat at the table when available — the market is good, and ignoring it would waste signal.
[ Accuracy ]
On a holdout backtest of 790 fights from 2023 to present, the model picks the right winner 68.4% of the time. Against 370 fights with Vegas odds (Nov 2018 – Mar 2026): model 72.7% vs Vegas 70.3%. It called 49 of 110 upsets — Vegas called zero.
Real numbers, not cherry-picked. The model is solid. It's also not infallible — MMA is chaotic, and no model captures a fighter's chin, their corner, or whether they had a bad camp.
[ What the % Means ]
When Fighter A wins 67%, it means: historically, when fighters with profiles like these matched up, the Fighter A analog won ~⅔ of those fights. A statement about distributions — not a guarantee about Saturday night.
What's Coming
The site is in active development. Post-launch pipeline:
Surface picks from top MMA analysts alongside the model probability and Vegas line. Three signals at once: the data, the market, and the minds who watch this sport for a living.
Upcoming and recent fight cards as prediction panels — fighter vs. fighter, model probability, Vegas line side-by-side.
When two fighters have history, it should be visible. A fight timeline that gives every rematch the context it deserves.
The model knows every fighter's Elo and stats at any point in history. GSP vs. Khabib at their respective peaks. Fedor vs. Ngannou in 2009. It's a natural extension of the data.
Key Terms
- ELO Rating
- Starts at 1500. Reflects performance quality weighted by opponent level and finish type. Top P4P contenders typically sit 1650–1750+.
- ELO Win Probability
- Raw prediction from the Elo gap alone — one of 37 signals in the full model. Layering in striking stats meaningfully improves over Elo alone.
- Z-Score (P4P)
- Standard deviations above divisional mean. Fixes the cross-division comparison problem — raw Elo ratings can't be compared across weight classes.
- Confidence Score
- 0.0–1.0. High = recently active, enough fights. Low = new, inactive, or provisional. Doesn't affect the math, just tells you how much to trust the number.
- Recent Form
- Win rate over last 5 fights. Recency matters — a fighter on a streak gets a bump regardless of career Elo.
- Finish Rate
- % of wins by KO/TKO/sub. High finish rate = more volatility. Factored into matchup dynamics.
- Days Inactive
- Days since last bout. Triggers inactivity decay in Elo and is a direct feature in the predictor. Ring rust is real.