Pressing, pressure and re-aggressing as tactical movement of anterior cruciate ligament injuries in women's soccer
Bibliographic record
Abstract
INTRODUCTION: A better understanding of anterior cruciate ligament (ACL) injury mechanisms in female soccer can guide better research on both prevention programs and late-stage rehabilitation for the return-to-sport process. This narrative review investigates the technical and tactical game situations in female soccer linked to ACL injury mechanisms. EVIDENCE ACQUISITION: Through a literature search, we reviewed scientific literature to identify soccer-specific technical movements and tactical patterns that increase the risk of ACL injuries in female players. Articles were retrieved through Web of Science, SPORTDiscus, ScienceDirect, PubMed, and PubMed Central. Inclusion criteria were: 1) studies on ACL injury mechanisms in women's soccer; 2) studies examining soccer techniques/tactics to identify non-contact injury mechanisms. Gray literature was included to supplement limited indexed data, aiming to stay within author guidelines. EVIDENCE SYNTHESIS: Female athletes experience a 2-8 times higher risk of ACL injury than males, with 70% of these injuries being non-contact. Common scenarios leading to these injuries involve ball possession/non-possession phases, tactical actions (pressing, pressure, re-aggression), and technical movements (cutting, changing direction). Tactical aspects, particularly pressing and re-aggression, are key contributors to ACL injury risk regardless of ball possession. CONCLUSIONS: In female soccer, ACL injuries are related to non-contact injury mechanisms during technical and tactical situations. The knowledge and study of these situational patterns of play, such as pressing, re-aggression, pressure, and cutting maneuvers, are essential to target prevention strategies and return-to-sport processes objectively.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".