Comparison of Endurance, Agility, and Strength in Elite Hockey and Soccer 9 Year-Old Players
Bibliographic record
Abstract
Background.Regardless of the age, elite athletes exhibit various motor capabilities (e.g., speed; endurance; strength) that are specific to that particular sport.Objectives.The purpose of this study was to compare different aspects of physical fitness (agility, strength, endurance) in thirty, 9 year old elite soccer and hockey players.Methods.Participants completed a 90 minute familiarization session, and returned at the later date to perform tests examining their agility (T-test), endurance (20mSRT), and strength (long jump; v-ups; push-ups; sit-ups).The tests were administered by an expert trainer at the same location.At the onset no inter-group differences were found for age, height, weight, foot size, number of years playing at the competitive level, and hours training per week, as well as scores from MABC assessment tool.Results.A series of independent sample t-tests revealed statistical differences in endurance (p < .001,d = 3.57), and in strength tasks (p < .001,d = 1.66) (sit-ups, push-ups and v-ups) in favour of soccer group.However, hockey players were more agile (p < .001,d = 1.26) and generated more power as inferred from the long jump (p < .05,d = 1.1).Conclusion.Overall, the results showed that some domains of movement proficiency are specific to either soccer (endurance/ strength) or hockey (agility/power).These results provided coaches with information in regards to their respective teams as well as individual players' performance, and may aid in adaptations of the respective training programmes.
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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) | 0.000 | 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) | 0.002 | 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".