Hamstring injury rates have increased during recent seasons and now constitute 24% of all injuries in men’s professional football: the UEFA Elite Club Injury Study from 2001/02 to 2021/22
Bibliographic record
Abstract
OBJECTIVES: To: (1) describe hamstring injury incidence and burden in male professional football players over 21 seasons (2001/02 to 2021/22); (2) analyse the time-trends of hamstring muscle injuries over the most recent eight seasons (2014/15 to 2021/22); and (3) describe hamstring injury location, mechanism and recurrence rate. METHODS: 3909 players from 54 teams (in 20 European countries) from 2001/02 to 2021/22 (21 consecutive seasons) were included. Team medical staff recorded individual player exposure and time-loss injuries. Time-trend analyses were performed with Poisson regression using generalised linear models. RESULTS: 2636 hamstring injuries represented 19% of all reported injuries, with the proportion of all injuries increasing from 12% during the first season to 24% in the most recent season. During that same period, the percentage of all injury absence days caused by hamstring injuries increased from 10% to 20%. Between 2014/15 and 2021/22, training hamstring injury incidence increased (6.7% annually, 95% CI 1.7% to 12.5%) as did burden (9.0% annually, 95% CI 1.2% to 18.3%). During those years, the match hamstring injury incidence also increased (3.9% annually, 95% CI 0.1% to 7.9%) and with the same trend (not statistically significant) for match hamstring injury burden (6.2% annually, 95% CI -0.5% to 15.0%). CONCLUSIONS: Hamstring injury proportions-in number of injuries and total absence days-doubled during the 21-year period of study. During the last eight seasons, hamstring injury rates have increased both in training and match play.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".