AI Sprawl Analysis Example: Real mmaGPT Output

Setup

Amateur MMA fighter, two-and-a-half years of training, uploaded three minutes of takedown defense drilling against a wrestling-heavy partner. The athlete had been told by their coach that their sprawl was "slow" and wanted a measurement. They selected wrestling-focused review.

What mmaGPT observed

The system measured reaction time from opponent level-change to defender sprawl initiation across 12 shot attempts. Average reaction was 0.58 seconds — meaningfully slower than the 0.4-second competitive threshold. The defender consistently triggered the sprawl off hand-on-leg contact instead of off the level-change visual cue. Hip trajectory was diagonal-forward instead of straight down. Three of twelve sprawls finished with no upper-body frame.

The full AI output

Exactly as the session returned it.

Corrections applied

1. Switched the visual cue: drill the sprawl off partner hip drop, not off shot contact.
2. Hip-trajectory cue: "belly button to the floor" — practiced static against a wall.
3. Added an underhook finish to every sprawl in drilling.
4. Widened stance by four inches to give the sprawl more runway.