Performance Alteration Induced by Weight Cutting in Mixed Martial Arts—A Biomechanical Pilot Investigation
Bibliographic record
Abstract
Currently, there are pros and cons of research results related to weight cutting in combat sports, resulting in inconclusive results regarding the effects of weight-cut on athletes' performance, and biomechanical investigations are hardly seen. Therefore, this pilot study tried to fill the gap by initiating an exploration in real-life competitions. It is our hope to add biomechanical insights (advantages/disadvantages) that would discern the impact of weight cutting on competitive performance and help to structure hypotheses in future research. The method consisted of 3D motion capture, EMG measurement and biomechanical modeling. Through the synchronized data, striking power, striking accuracy and reaction time were quantitatively determined. Pre- and post-test design was used to test common strikes before weight cutting and 24 h after weigh-in. Seven male athletes from local clubs were tested during regional competitions. Results were characterized by using descriptive statistics (means and standard deviations) and T-tests were performed to contrast differences between the pre- and post-tests. This pilot study has revealed that there is actually weight-regain instead of weight-loss. The weight-regain would speed up the perceptional and total reaction, slow down the limbs' movement, worsen the striking accuracy and, possibly, decrease the strike power. The preliminary results are inconclusive regarding the competitive advantages/disadvantages induced by weight cutting. Further biomechanical studies are needed to deal with the controversial subject more objectively and scientifically.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".