Analysis of mechanical properties of different volleyballs
Bibliographic record
Abstract
Concussions are becoming an increasingly important issue in sports, especially in an area like volleyball that, until recently, seemed less prone to such injuries. The purpose of this study was to determine the coefficient of restitution and basic mechanical properties of standardized volleyballs, including stiffness and hysteresis, dependent on different influencing factors. A drop test was performed to calculate the coefficient of restitution based on the time of flight of the ball between the first and second bounce. This was detected using a microphone with a sampling frequency of 44.1 kHz. To determine the mechanical properties, a materials testing machine applied a compression force until a deformation of 50 mm was achieved. These data were sampled with a frequency of 12.5 kHz. The results showed that the three analysed factors in this study, including the ball model, gauge pressure, and incident velocity, had a significant influence on the coefficient of restitution. Therefore, the amount of energy dissipation during an impact situation at low velocities was significantly different within the tested sample of official standardized volleyballs. The stiffness and hysteresis of the volleyballs also varied significantly. These basic mechanical findings raise an interesting question as to whether the investigated factors are appropriate to predict forces acting on the head during a high velocity collision (25–30 m/s), ultimately resulting in a risk of concussion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".