Deciphering the offensive process in women's elite football: A multivariate study
Bibliographic record
Abstract
Over the last few years, there has been considerable increase in scientific knowledge about women's football. However, the tactical and tactical-strategic aspects have not yet been sufficiently covered in scientific literature. Therefore, this work proposed the following aims: To describe how the offensive phase is produced in women's football, to identify the significant statistical criteria that may be modulating success in attack, and finally to propose different predictive success models, with the ultimate aim of passing this knowledge on to the applied field. The observational methodology was used, one of the most appropriate methodologies for the analysis of motor behaviors in sport. The units of analysis collected and analyzed were 6063 attacks carried out during the FIFA Women's World Cup Canada 2015 and France 2019. The available results demonstrate that, on the one hand, offensive team actions are ineffective (almost 70% finish unsuccessfully), but criteria such as the start form of the attack, zone of ball possession, partial match result, or ball possession time are statistically significant criteria that modulate attack success (goal, shot or pass into the area). Lastly, the multivariate results allow us to propose a theoretical model, passing the probability of success from 31% in the absence of a model, to a theoretical auction probability of 52.6%, based on fast attacks with the intervention of few players, and with possession zone in the opposite field. These results could be directly transferred to the practical field where trainers and technical bodies can put this information into practice in training sessions or matches.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".