Physiological and Performance Correlates of Squash Physical Performance
Bibliographic record
Abstract
The physiological and performance attributes of elite squash players were investigated. Thirty-one players (21 males, world ranking [WR] 42-594; 10 females, WR 7-182) completed a battery of fitness tests which included an aerobic squash-specific physical performance test (SPPT), repeated-sprint ability (RSA), change-of-direction speed (COD), acceleration (5-m sprint), body composition and force development (countermovement jump) assessments. The SPPT provided a finishing lap score, V̇O2max, average movement economy and the lap corresponding to a blood lactate concentration of 4 mM.L-1. Players were ranked and assigned to HIGH or LOW performance tiers. Two-way ANOVA (performance level*sex) revealed higher ranked players performed better (p < 0.05) for SPPT final lap (d = 0.35), 4 mM.L-1 lap (d = 0.52) and COD (d = 0.60). SPPT displayed a ‘very-large’ correlation with 4 mM.L-1 lap (r = 0.86), ‘large’ correlations with COD (r = 0.79), RSA (r = 0.79), sum-of-7 skinfolds (r = 0.71) and V̇O2max (r = 0.69), and a ‘trivial’ correlation with average movement economy (r = 0.02). Assessments of cardiovascular fitness (i.e. 4 mM.L-1 lap), RSA, COD and body composition appear highly pertinent for performance profiling of squash players. Regular, submaximal assessment of the 4 mM.L-1 lap during the SPPT may offer a practical athlete monitoring approach for elite squash players.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".