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UFC DATA LAB

Live database

Trend Explorer

Build a historical UFC trend, inspect the sample behind it, then send the exact same filter set into Strategy Lab.

Breakdown

Matching Fighter Instances

Date	Fighter	Opponent	Result	Odds	Age Δ	Reach Δ	Layoff Δ	Streak	Finish

Historical backtests are descriptive, not predictions. Subgroup results can be unstable and vulnerable to selection bias, missing odds, changing competition level and overfitting.

Trend Scanner

Automatically test a curated library of age, reach, price, form, layoff, experience and division patterns. Discovery data finds the pattern; later validation data checks whether it held up.

Important: “Research Score” is a ranking heuristic, not a probability or guarantee. It rewards out-of-sample edge, sample size and discovery/validation consistency. For underdogs, focus on win rate together with ROI and edge versus implied probability—a 44% hit rate can be excellent at plus money.

Ranked Historical Patterns

Trend	Score	Discovery	Validation	Val ROI	Val Edge	Wilson Floor	Full Sample

Best Historical Strategies

A plain-English dashboard of the strongest true plus-money underdog scenarios we found. “Underdog” here means +100 or higher; fights where both sides were negative are kept out of this page.

Money examples

What would this have looked like with your bankroll?

Enter a starting amount. The 2%, 3% and 5% examples below scale the actual historical fixed-percentage simulations. A 2% strategy means the next stake automatically grows or shrinks as the bankroll changes.

Starting bankroll

First bet at 2%

$200

Balanced / lower volatility

First bet at 3%

$300

Growth-focused middle ground

First bet at 5%

$500

Aggressive / larger swings

True underdog rule

+100+

Both-negative fights excluded

Side-by-Side Comparison

2022+ validation replay

Scenario	Record	Win %	Avg Odds	Flat ROI	2% Ending	3% Ending	5% Ending	Risk

What These Numbers Mean

Win %How often fighters matching that exact scenario won during the 2022+ test period.

Flat ROIProfit per dollar wagered if the same flat amount had been risked on every qualifying fight.

SampleHow many qualifying historical bets we actually have. Bigger samples deserve more trust.

Average OddsThe approximate average American moneyline price. +150 means a $100 winner profits $150.

Example: With a $10,000 bankroll and a 2% strategy, the first qualifying bet is $200. If your bankroll later grows to $12,000, the next 2% bet is $240. If it falls to $8,000, the next bet automatically falls to $160. That is why fixed-percentage bankroll sizing is easier to manage than Martingale-style chasing.

Historical simulations are descriptive research, not a promise of future profit. We scanned a very large number of scenarios, so small samples can look unusually strong by chance. The dashboard intentionally labels smaller samples more cautiously.

Strategy Lab

Replay a historical trend chronologically with different bankroll and progression systems. No live betting or bet placement—this is a backtest and risk simulator.

ACTIVE TREND

Current Trend Explorer filters

Bankroll

Starting bankroll ($)

Stop if bankroll below ($)

Max single bet (% bankroll)

Max total event exposure (% bankroll)

Staking System

Strategy

Base unit type

Base unit value

Martingale multiplier

Fibonacci win rule

Kelly fraction

Estimated win probability (%)

Timing & Stress Test

Bet timing

Bootstrap Monte Carlo runs

For Kelly, use an estimate that was available before the test period. Scanner trends automatically load their discovery-period win rate here.

Progression systems do not create an edge. Martingale/Fibonacci can concentrate risk dramatically; the caps above are enforced so the backtest cannot wager money that did not exist.

Bankroll Curve

Bet Ledger

Date	Fighter	Odds	Stake	Result	P/L	Bankroll Before	Bankroll After