How we calculate everything you see

01  Data source and coverage

All historical fight data on CFL is sourced from the official UFC statistics website, accessed via public HTTP requests at a rate-limited cadence designed to avoid burdening their servers. We collect publicly available facts only — fighter biographical data, fight outcomes, and round-level box-score statistics. We do not collect personal information, do not bypass any authentication or rate limit, and do not republish source HTML.

The database powering this site currently contains:

• 4,492 fighter profiles covering substantially the full historical UFC roster
• 8,766 completed fights with outcome, method, round, and weight class
• 782 events spanning UFC history through the most recent card

Fight metadata (who fought whom, who won, by what method, in what round) is complete across the dataset. Per-fight round-by-round statistics (significant strikes, takedowns, control time) are populated for the subset of fights where UFC published them — primarily Zuffa-era events.

02  Age-gap win rate

For every completed UFC fight in the dataset where both fighters' dates of birth are known, we compute the age difference in whole years between the two fighters on the date of the fight. We then bucket fights by the absolute age gap and measure the win rate of the younger fighter within each bucket.

Formula

Why we trust this signal

• The sample size (3,309 fights) is large enough to be statistically meaningful
• The pattern is monotonic — larger age gaps produce larger win-rate gaps for the younger fighter, which is what you would expect if the effect is real and not noise
• Age is exogenous — fighters cannot manipulate it, and matchmaking does not systematically pair younger versus older fighters in a way that would confound the result

Known caveats

• Effect size diminishes for age gaps under 2 years (within normal cohort variation)
• Heavyweight division shows a slightly weaker age effect than lighter weight classes, likely because power-punching ages better than speed
• This is a population-level finding and does not predict any individual fight outcome

03  Reach-gap win rate

For every completed UFC fight where both fighters' reach measurements are recorded, we compute the reach difference in whole inches and measure the win rate of the longer-reach fighter at each gap.

Formula

Known caveats

• Reach is correlated with height and weight class — within a single weight class the effect is smaller than it appears at the cross-divisional level
• Reach matters more for primary strikers than for grappling-first fighters
• Effect decays smoothly with smaller gaps; a 1-inch reach advantage is statistically indistinguishable from no advantage

04  Stance matchup win rate

For every completed UFC fight where both fighters' stances are recorded, we group fights by the specific stance pairing (Orthodox vs Orthodox, Orthodox vs Southpaw, Switch vs Southpaw, etc.) and measure win rates by stance.

Why we report only the matchups with real signal

Most stance pairings cluster near 50% and are not predictive. We publish only the specific pairings where the deviation from 50% is large enough and the sample is wide enough to be worth a bettor's attention. Orthodox-versus-Orthodox is treated as a 50% baseline.

Known caveats

• The Switch-vs-Southpaw sample (191 fights) is far smaller than the age or reach samples — the confidence interval around 58.1% is wider
• "Switch" classification depends on how UFC categorizes a fighter, which is occasionally inconsistent
• This effect may partially reflect that Switch-stance fighters tend to be more experienced, not that the stance itself confers an advantage

05  Implied win percentage model

For each upcoming fight, we compute an implied win percentage for each fighter by starting at 50% and additively applying the deviation from base rate contributed by each measured signal.

Formula

What this model deliberately is not

• It is not a multivariate regression. Each signal is applied independently, which slightly overcounts when signals are correlated (e.g., age and reach can interact)
• It does not include career stats like finish rate, strikes-per-minute, or takedown defense. Those metrics are confounded with fighter quality and we have not yet isolated their independent predictive value
• It does not adjust for opponent quality, recent form, weight cut, layoff, or camp changes

06  Fighter style classification

For every fighter with at least 10 minutes of Round 1 fight time in the dataset, we classify their style based on Round 1 output rates. We use Round 1 specifically because later rounds are confounded by fatigue, damage taken, and tactical adjustments — Round 1 reveals fighter intent more cleanly.

Scoring

Classification logic

We use a dominance-ratio approach rather than fixed thresholds. Fixed thresholds incorrectly classified extreme grapplers as "hybrid" because their striking output happened to clear an absolute floor. Dominance ratio asks: relative to their own activity, what does this fighter actually do?

We use the term "grappler" intentionally — the bucket includes both shoot-takedown wrestlers (e.g., Khabib Nurmagomedov) and submission grapplers (e.g., Charles Oliveira). Distinguishing those two sub-styles is a future enhancement.

07  Cardio and consistency scores

For fighters with sufficient three-plus round fight data, we measure the ratio of output in Round 3+ versus Round 1. A 1.0 ratio means perfectly maintained pace; below 0.7 indicates measurable cardio fall-off.

Grappling cardio metric

The takedown-attempt-based version of this metric (used in earlier drafts) was discarded — it penalized one-shot finishers like Khamzat Chimaev and rewarded fighters who shoot inefficiently. The control-time-aware version measures grappling success, not effort.

Striking cardio metric

The striking effectiveness rewrite is in progress. Current striking cardio is reported only where the underlying sample supports it, and is flagged as preliminary on the fighter page.

08  What we deliberately do not publish

We measured several other variables and chose not to publish them as base rates because they failed our internal tests for being clean, single-variable signals.

• Finish rate. Strongly correlated with fighter quality — high-finish-rate fighters tend to face weaker opposition early in their careers. We cannot yet isolate the independent contribution
• Strikes landed per minute (SLPM). Same confound as finish rate. A high SLPM may reflect dominant matchmaking rather than predictive striking volume
• Takedown defense. Sample-size issues for fighters who are rarely shot on. Reported on the fighter card but not used in the implied win percentage model
• Recent form / last five. Reported as a streak but not modeled — small samples and high variance make it noisy
• Short-notice replacements. Sample size too small for any specific weight class to be informative

If and when we develop methods that isolate these signals cleanly, we will publish them with full methodology and sample sizes documented here.

09  Known limitations

• None of these statistics predict any individual fight outcome. They describe historical population-level patterns
• The implied win percentage is additive across signals and does not account for interactions between them
• UFC matchmaking is non-random. Title contenders fight title contenders. This biases certain base rates in ways we have not fully corrected for
• Per-fight round-by-round statistics are missing for older fights — analytics that require these stats use the subset of fights for which they exist, which skews more recent
• Stance, reach, and DOB are self-reported through UFC and occasionally inconsistent or missing
• Sample sizes for sub-buckets (e.g., specific weight class within a specific stance matchup) are often too small to be informative and we generally do not publish them

10  How and when this data updates

• Fight outcomes and event data update nightly via a scheduled scraper
• Fighter career averages refresh after each new fight is recorded
• Base-rate analytics (age gap, reach gap, stance) are recomputed weekly against the latest dataset
• If a methodology changes — formula, bucket boundaries, sample inclusion rules — this page is updated and the change is dated

Disagreement with a specific number? Spot a methodology error? Get in touch — we treat substantiation questions as the most important questions a user can ask.