Efectos de un programa basado en juegos reducidos sobre la condición física de jóvenes jugadores de fútbol
Bibliographic record
Abstract
Football training methodology has evolved greatly in recent years, including more increasingly specialized\ndrills, with small-side games (SSG) taking on a prominent role. The objective of the current study was to\nanalyze the effects that SSGs have on the physical condition of young football players during a six-week\nprogram. The study included 22 football players of two different age groups: U16 (n= 11, age: 15.8±0.3\nyears) and U18 (n= 11, age: 18.5±0.5 years). The participants were evaluated at the beginning and at the\nend of the intervention with the following tests: counter-movement jump (CMJ), 40 m sprint speed test\n(V40m) and endurance test “University of Montreal Track Test (UMTT)”. The results showed significant\nimprovements of the pre- to post- test on the CMJ (ES= 1.05± 0.13, 100%, 0%, 0%), V40m (ES= 0.29±\n0.15, 0%, 16%, 84%) and UMTT (ES= 0.30± 0.20, 79%, 21%, 0%) p<0.05). With these results we can\nconclude that a program based on SSG, as well as progressive sequence and variety in the game formats\nin terms of the size and number of players on a team, can maintain and/or improve the physical condition\nof the players. One of the practical applications derived from the study is that the coaches can implement\nSSG to develop game tasks without neglecting the players’ physical condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".