Analysis of patterns of ball recovery in youth futsal
Bibliographic record
Abstract
Purpose To investigate the patterns of recovery of ball possession in a young futsal team. Methods Seven games played by a youth futsal team were analysed. Patterns of recovery of ball possession were investigated on the basis of the following variables: way to recover the ball, location of recovery, tactical behaviour after the recovery, and result of the match. One-way ANOVA and post-hoc Tukey honest significant difference test were used to compare the variables. Principal component analysis was also applied to verify the association between variables. Results It was observed that there was a greater number of ball recoveries in the defensive sector (F3,24 = 35.6; p < 0.001; ηp2 = 0.79), that set pieces were the most frequent way to recover the ball (F5,36 = 7.9; p < 0.001; ηp2 = 0.46), that ball possession was maintained more often after the recovery of the ball (F3,24 = 79.6; p < 0.001; ηp2 = 0.90), and that there was no correlation between the result of the match and the number of ball recoveries (F3,24 = 0.20; p = 0.93; ηp2 = 0.10). Four components were identified that represented a variance of 95% for all variables. Factor 1 was related to the patterns of ball possession recovery in the offensive sector, while factor 2 was related to the tackle. Conclusions It was concluded that the way to recover the ball and the location of recovery affected both patterns of recovery and tactical behaviour after the recovery of the ball.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".