The Ecological Validity of Countermovement Jump to On-Court Asymmetry in Basketball
Bibliographic record
Abstract
Abstract Jump-based asymmetry is often used as an indicator of sport performance and may be used to discern injury susceptibility. Due to task specificity, however, countermovement jump asymmetry may not be representative of on-court asymmetry. As such, we assessed the association between countermovement jump asymmetry and on-court impact asymmetry metrics (n=3, and n=4, respectively) using linear regressions (α=0.05). Fifteen female basketball athletes completed countermovement jump and on-court sessions across a competitive season. A significant negative association was found between peak landing force asymmetry and both overall and medium acceleration on-court asymmetry (b=–0.1, R2=0.08, p<0.001; b=–0.1, R2 =0.11, p<0.001, respectively), as well as between peak propulsive force asymmetry and on-court medium acceleration asymmetry (b=–0.24, R2=0.04, p=0.01). Alternatively, both peak landing and peak propulsive force asymmetry were significantly positively associated with on-court high acceleration asymmetry (b=0.17, R2 =0.08, p<0.001; b=0.35, R2=0.02, p=0.04, respectively). While some overlap may exist, countermovement jump and on-court impact asymmetry appear to be independent. Thus, sport-specific monitoring may be necessary to adequately monitor injury susceptibility using asymmetry.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".