Exploring the effects of physical fatigue on cognitive performance of youth soccer players
Bibliographic record
Abstract
This study aimed to verify whether the peripheral perception and decision making of young soccer players are influenced by physical fatigue. The sample was composed of 48 soccer players from two youth academies of Brazilian clubs (17.03 ± 2.33 years old). In laboratory conditions, the Vienna Test System and the TacticUP® video test was used to assess peripheral perception and decision making (response time and decision-making quality), respectively. Physical fatigue was induced through T-SAFT90 that simulated the metabolic and physical demands of a soccer game (e.g., acceleration, deacceleration, change direction, jump, and technical action). Peripheral perception and decision-making abilities were compared between the control and physical fatigue “conditions”. Results displayed that physical fatigue did not influence peripheral perception and decision-making quality, although it improved decision-making response time for the tactical principles of penetration, width and length with the ball, delay, defensive coverage, and recovery balance. In summary, physical fatigue did not affect players’ ability to detect information from the peripheral visual field and did not influence the quality of decision-making of soccer players. In addition, physical fatigue induced players to make quicker decisions regarding tactical actions near the ball and inside the centre of play. Thus, we conclude that only the response time of decision-making of youth soccer players is influenced by physical fatigue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".