The Evaluation of the Modified Wave Periodization Model Efficiency on the Example of Young Soccer Players' Sprint Tests
Bibliographic record
Abstract
The research aimed to evaluate the modified model of wave periodization efficiency in running speed tests conducted among soccer players aged 12 to 16. Participants included prospective players of a leading Polish top league soccer club. The research was carried out from 2018 to 2022 in June (Testing A) and December (Testing B) of each year. The test involved 30-m straight line running with 5-, 10-, and 30-m split time measurements. For this purpose, electronic photocells were used (FITLIGHT, Canada). The six-month training intervention increased the athletes' speed as there was a considerable decrease in the running time over the distance of 5 m (F = 7.86; p < 0.001), 10 m (F = 73.99; p < 0.001) and 30 m (F = 127.55; p < 0.001). Analysis of running performance of young soccer players aged 12-16 showed a significant improvement in speed at distances of 5, 10 and 30 m, confirming training effectiveness based on the wave periodization model. The negative correlation between testing year and performance suggests the influence of biological development on players' speed. The COVID-19 pandemic has impacted training, which was reflected in reduced differences between test scores. Improving initial running technique can contribute to better match results, which emphasizes the need for an individual approach to the physical preparation of players.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".