The development of an observational tool to code knockouts in Mixed Martial Arts (MMA)
Bibliographic record
Abstract
Objective To develop an observational tool to reliably code the situational characteristics and mechanism of contact for fights ending in knockout (KO) or technical KO (TKO). Design Digital video images and fight card information were collected retrospectively for UFC events during 2006–2009. Subjects Two coders independently viewed and completed to the Mixed Martial Arts (MMA) ‘Knockout’ Tool for 60 events (30=KO's 30=TKO's) chosen randomly from the pool of identified events during 2006–2009. Outcome Measures The MMA ‘Knockout’ Tool was developed by consensus and consists of 17 factors, including theSituational Context(eg, Round, octagon location, technical position),Pre-KO/TKO Strike Profile(eg, striking implement, strike location),Assessment of Fighters(eg, visual obstruction, clinical signs of concussion), andKO strike profile(eg, striking implement, strike location, head motion, additional head impacts). Intercoder agreements for each factor of interest were calculated using weighted κ coefficient. Results Coders achievedsubstantial agreementfor the majority of the factors with κ coefficients ranging from 0.46 to 1.00. An overall reliability value was calculated based on all factors of the MMA ‘Knockout’ Tool, with an average κ coefficient of 0.81. Conclusion The MMA ‘Knockout’ Tool is reliable for coding the situational characteristics and mechanism of contact(s) for fights ending in a TKO or KO. 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 | 0.028 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".