Striking dynamics and kinetic properties of boxing and MMA gloves
Bibliographic record
Abstract
With the growing popularity of Mixed Martial Arts (MMA) as a competitive sport, questions regarding the dynamic response and properties of MMA gloves arise. High-energy impacts from punches are very similar to boxing yet MMA competition requires the use of 4 oz fingerless glove, compared to the larger full enclosure boxing glove. This work assessed the kinetic properties and strike dynamics of MMA gloves and compared findings with traditional boxing gloves. Gloves mounted on a molded fist were impacted repetitively on an instrumental anvil designed for impact, over a 5 hour period resulting in 10,000 continuous and consistent strikes. Kinetic data from impacts were sampled at the beginning of the data collection and subsequently every 30 minutes (every 1,000 strikes). MMA gloves produced 4-5 times greater peak force and 5 times faster load rate compared to the boxing glove. However, MMA gloves also showed signs of material fatigue, with peak force increasing by 35% and rate of loading increasing by 60% over the duration of the test. Boxing glove characteristics did deteriorate but to a lesser extent. In summary, the kinetic properties of MMA glove differed substantially from the boxing glove resulting in impacts characterized by higher peak forces and more rapid development of force. Material properties including stiffness and thickness play a role in the kinetic characteristics upon impact, and can be inferred to alter injury mechanisms of blunt force trauma.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".