Gera Tools

MMA Fighter Statistical Comparison

Compare two MMA fighters across UFC-style performance stats.

Input striking accuracy, takedown defence, submission attempts, and significant strikes per minute for two fighters to generate a head-to-head statistical advantage matrix with a weighted overall edge.

What stats does this comparison use?

It uses four common UFC-style metrics: striking accuracy as a percentage, takedown defence as a percentage, submission attempts per fifteen minutes, and significant strikes landed per minute. These mirror the categories tracked on official fight statistics pages.

A like-for-like fighter comparison

Fight previews live and die on the tale of the tape, but the numbers that actually predict the action are the performance metrics — how accurately a fighter strikes, how well they defend takedowns, how often they hunt submissions, and how much volume they land. This tool takes those four figures for two fighters and lays out a clear, category-by-category advantage matrix.

How it works

Each of the four metrics is compared directly between the fighters, and an arrow points to the leader. To produce one overall edge, the values are normalised so they sit on a comparable 0-to-1 scale and then averaged with equal weight:

striking accuracy   → value / 100        (percent)
takedown defence    → value / 100        (percent)
submission attempts → value / 5          (per 15 min, capped at 1)
sig. strikes/min    → value / 8          (SLpM, capped at 1)

overall score = average of the four normalised values

The fighter with the higher overall score is reported as holding the statistical edge, along with the size of the gap. Capping the rate metrics prevents an extreme outlier in one category from dominating the whole comparison.

Tips and example

Fighter A: 52% striking accuracy, 80% takedown defence, 1.2 submission attempts, 4.5 SLpM. Fighter B: 47% accuracy, 65% defence, 2.5 submissions, 3.8 SLpM. A leads in accuracy, defence, and volume; B leads in submission hunting. The weighted average favours A by a modest margin.

Notes: enter realistic values pulled from a stats provider. Because the model weights categories equally, it will not capture a stylistic mismatch — a high-volume striker who can be taken down may still lose to a grappler the numbers slightly favour the striker. Treat the edge as a discussion starter.