Performance tracking in female youth soccer through wearables and subjective assessments
Bibliographic record
Abstract
This study investigates performance development and the relationship between subjective and objective training assessments in female youth soccer using wearable sensor technology. The aim of this study was to assess how subjective post-training ratings (intensity and happiness) relate to high-percentile performance outputs, and to identify longitudinal trends in female youth soccer players using IMU-based wearable data. Data were collected over a 14-month period from 46 players (U17 and U20 teams) equipped with foot-mounted inertial measurement units (IMUs) during regular training sessions. Objective performance metrics, including 95th percentile of ball speed, peak speed, and absolute distance, were derived using a multi-stage machine learning pipeline, while subjective metrics (intensity and happiness) were collected via post-session Likert-scale questionnaires using an app. Using the modified Mann-Kendall test, we found 30 significant longitudinal trends, with 14 positive and 16 negative trends across key performance metrics. Peak speed showed the highest number of trends (13), followed by absolute distance (10) and ball speed (7). Correlation analyses based on the Spearman coefficient (with False Discovery Rate correction) revealed meaningful associations between subjective self-assessments and high-percentile performance metrics, with notable differences across player positions and age groups. A robustness check confirmed these patterns also hold when analyzing the 99th percentile of performance outputs. Our findings underscore the value of combining wearable sensor data with subjective evaluations for individualized, role-specific performance monitoring and training optimization in youth soccer. However, as an exploratory study with a single cohort, findings require further validation in broader populations.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".