Sport Equipment Evaluation and Optimization - A Review of the Relationship between Sport Science Research and Engineering
Bibliographic record
Abstract
In current sport equipment evaluation and optimization, most studies consider the body and an equipment together as one system.This is partially because equipment optimization is mainly done through modification of mechanical designs, thus equipment evaluation is conducted through statistical comparisons of how different mechanical designs perform under human usage.However, it is known that any change in the performance environment would cause one to adapt certain aspects of his or her movements.Variation in equipment is considered as such a performance-altering environmental change.Yet, this equipment-induced motor control change is hardly studied in sport equipment evaluation/optimization, such as studies on golf clubs, pole-vaulting poles and hockey sticks.Without a thorough understanding of the interactions between equipment alteration and human motor control adaptation, equipment optimization is like a hit-and-miss game.Therefore this paper aims: 1) to look back at the different generations (eras) in the development of sports equipment, 2) to elaborate the roles of engineering and sport science/motion analysis technology in each generation and 3) to discuss the essence of sport science research in sport equipment optimization, which has evolved beyond pure engineering.One focus of this review is on body-equipment interactions and body movement adjustments in response to different equipment designs.Both these aspects should ideally be included in future studies related to sports equipments.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".