Injury Burden in Professional European Football (Soccer): Systematic Review, Meta-Analysis, and Economic Considerations
Bibliographic record
Abstract
OBJECTIVE: We performed a systematic review and meta-analysis to evaluate the injury burden and the related economic cost in European professional male football players. DATA SOURCES: Multiple database research was performed up to August 5, 2022 (PubMed, EMbase, Scopus, Cochrane Library), including only studies that reported severity in the number of days of absence for each injury, incidence reported in the number of injuries/1000 hours, or reported number of injuries and exposure time and adult male football players, professionals from European clubs. Two reviewers extracted data and assessed paper quality with the Strengthening the Reporting of Observational Studies in Epidemiology statement and the Newcastle-Ottawa Scale. MAIN RESULTS: Twenty-two studies have reported incidence, severity, and burden of injuries in professional football. The highest injury burden was found for ligament-joint injuries (37.9 days/1000 hours; 222 397 €/1000 hours) and for muscle injuries (34.7 days/1000 hours; 203 620 €/1000 hours). Injury locations with high burden were knee injuries (34.8 days/1000 hours; 20 4206 €/1000 hours)-mainly anterior cruciate ligament injuries (14.4 days/1000 hours; 84 499 €/1000 h)-followed by thigh injuries (25.0 days/1000 hours; 146 700 €/1000 hours), hamstrings injuries (15.4 days/1000 hours; 90 367 €/1000 hours), hip-and-groin injuries (16.1 days/1000 hours; 94 475 €/1000 hours), primarily adductor muscles injuries (9.4 days/1000 hours; 55 159€/1000 hours), and ankle injuries (13.1 days/1000 h; 76 871 €/1000 hours) with ankle sprains (7.4 days/1000 hours; 43 423 €/1000 hours). CONCLUSIONS: Being exposed to injury risk has serious consequences for individual and club performance and economy. This review identified the most relevant targets in injury management, compared their injury data with reference values, and provided economic evidence when trying to gain buy-in from the key decision makers.
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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 | 0.031 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".