Quantification of the perceived training load and its relationship with changes in physical fitness performance in junior soccer players
Bibliographic record
Abstract
The aim of this study was to determine the relationship between perceived respiratory and muscular training load (TL) and changes in physical fitness in elite and non-elite junior soccer players. Twenty-eight elite (n = 14, 17.6 ± 0.6 years, 70.3 ± 4.4 kg, 179.7 ± 5.6 cm) and non-elite (n = 14, 17.5 ± 0.5 years, 71.1 ± 6.5 kg, 178.1 ± 5.6 cm) soccer players belonging to a Spanish first and third division football academies and competing in junior Spanish first division (2012-2013) participated in the study. Countermovement jump (CMJ), CMJ arm swing, 5 and 15 m sprints and the Université de Montreal endurance test were performed in January and 9 weeks later in March. In order to quantify TLs, after each training session and match, players reported their session rating of perceived exertion (sRPE) separately for respiratory (sRPEres) and leg musculature (sRPEmus). Elite players accumulated greater weekly training volume (361 ± 14 vs. 280 ± 48 min; effect sizes (ES) = 5.23 ± 1.74; most likely), and perceived respiratory (1460 ± 184 vs. 1223 ± 260 AU; ES = 1.12 ± 0.79; very likely) and muscular (1548 ± 216 vs. 1318 ± 308 AU; ES = 0.99 ± 0.84; likely) TL than did non-elite players. Training volume, sRPEres-TL and sRPEmus-TL were positively and largely correlated (r = 0.67-0.71) with the changes in aerobic fitness. The present results suggest that a low training volume and TL can impair improvement in aerobic fitness in junior soccer players during the in-season period.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".