Comprehensive analysis of ‘knockouts’ in Mixed Martial Arts (MMA)
Bibliographic record
Abstract
Objective To quantify and identify potential risk factors for knockouts (KOs) and technical KOs (TKOs) in Mixed Martial Arts fighters. Setting Ultimate Fighting Championship (UFC). Design Retrospective. Fight card and fighter data was collected from all numbered UFC events over a 4-year period ending in 2009. Publicly available databases and digital video images were used to retrieve all pertinent information. Outcome Measures Event characteristics(eg, location, date, etc),fighter demographics(eg, age, nationality, time since last match),match characteristics(eg, match significance, rounds fought), and injury mechanism. Results A total of 503 matches were reviewed of which 36% ended in either KO (58; 12%; 57.7 per 1000 AE) or TKO (119; 24%; 118.4 per 1000 AE). The prevalence of KOs was highest in fighters between ages 36 and 40 (20.6%). The mechanism of contact resulting in a KO was predominately a direct blow to the head by a fist. 20% of all TKOs occurred in the heavyweight class. 33.9% of title matches result in TKOs and one in five of all KOs occurred during the first minute of a round. Conclusion Most often the mechanism of contact resulting in a KO was direct blow to the head. We have identified factors which were associated with a higher incidence of KO or TKO including age, weight, fight significance, time within the round, and time since last fight. Acknowledgements The Canadian Institutes of Health Research (CIHR) Strategic Team in Applied Injury Research funded this research. Competing interests None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".