Comparison of Repeated Sprint Ability of Amateur Football Players According to Age and Playing Positions
Bibliographic record
Abstract
The purpose of this study is to compare the repeated sprint ability of amateur footbal players according to age andplaying positions. For this purpose, 174 young amateur soccer players (age: 17.2±1.8 years, height: 175.8±7.5 cm,weight: 67.0±9.8 kg) struggling in different playing positions participated voluntarily to the study. The players dividedinto six categories as goalkeepers (n: 16; age: 17.4±1.4 years, height: 181.9±6.1 cm, weight: 77.4±9.8 kg), centraldefenders (n: 30; age: 16.9±1.9 years, height: 179.3±8.2 weight: 69.5±11.2 kg), full-backs (n: 34; age: 17.3±1.9 years,height: 174.9±5.9 cm, weight: 66.6±7.8 kg), central midfielders (n: 36; age: 17.9±1,6 years; height: 174.3±7.1 cm;weight: 67.1±9.3 kg), wide midfielders (n: 30; age: 16.8±1.6 years, height: 171.5± 5.1 cm, weight: 60.8±7.4 kg) andforwards (n: 28; age: 16.6±1.9 years, height: 175.9±8.3 cm, weight: 65.6±8.0 kg). In the study, a repeated sprint testwas used to determine the repeated sprint ability of the football players, with an in field 34.2 meter long sprint run anda 25 second rest period after each run. In evaluating the data; descriptive statistics, one-way ANOVA andkruskal-wallis tests were used. According to the analysis results; the best test time (BTT), mean test time (MTT), andtotal test time (TTT) values in the repeated sprint test showed a statistically significant difference according to playingpositions and age factor (p<.05); On the other hand, fatigue index (FI) value showed a statistically significantdifference according to playing positions (p<.05), but it didn’t show any significant difference according to age (p>.05).As a result, it can be argued that repeated sprint ability differs according to age and playing positions.
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 | 0.001 |
| 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 | 0.001 | 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".