Physical performance characteristics of high‐level female soccer players 12–21 years of age
Bibliographic record
Abstract
Performance assessment has become an invaluable component of monitoring player development and within talent identification programs in soccer, yet limited performance data are available for female soccer players across a wide age range. The aim of this study was to describe the physical performance characteristics of female soccer players ranging in age from 12 to 21 years. High-level female soccer players (n=414) were evaluated on linear sprinting (36.6 m with 9.1 m splits), countermovement jump (CMJ), and two agility tests. Separate one-way ANOVAs were used to compare performance characteristics between (1) each year of chronological age and (2) three age groups: 12-13 years, n=78, 14-17 years, n=223, and 18-21 years, n=113. Mean linear sprint speed over 9.1 m was similar across all chronological ages, however sprint speed over the final 9.1 m, CMJ height and agility scores improved until approximately 15-16 years. Outcomes from the group data indicated better performance on all tests for the 14-17-year-old group compared with the 12-13-year-old group. Additionally, sprint speed on the second and fourth 9.1 m splits and 36.6 m sprint speed as well as performance on the Illinois agility test was better in the 18-21-year-old group compared with the 14-17-year-old group. The findings from this study indicate that marked improvements of high intensity short duration work occur up until 15-16 years. Smaller gains in performance were observed beyond 16 years of age as evidenced by better performance on 36.6 m sprint speed, several sprint splits and the Illinois agility test in the college aged players (i.e., 18-21-year-old group).
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 | 0.001 |
| 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) | 0.003 | 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".