Injuries and risk factors in men’s beach soccer: Japanese national championships 2013–2023
Bibliographic record
Abstract
Understanding the most common injuries in beach soccer and their risk factors is essential for ensuring player safety. We aimed to describe the injury patterns and identify factors associated with the risk of injury in men’s beach soccer. We prospectively recorded injuries reported by players at an on-site aid station during the Japanese National Beach Soccer Championships between 2013 and 2023 (9 tournaments). Match exposure was recorded through video review. We described the injury types and sites, and the factors associated with the injury risk using generalized estimating equations in negative binomial models adjusted for confounders. In total, 796 participants played for 1360.5 player-hours. We observed 144 injuries, with an incidence rate of 106.0/1000 player-hours (95% CI: 89.9–125.0), representing 153 diagnoses. The most frequent injury sites were as follows: foot (n = 40), thigh (n = 24), and lower leg (n = 21). The most frequent injury type was contusion (n = 75). Of the 83 injuries caused by a contact with another player, 24 resulted in a foul. There were 35 injuries associated with time loss (incidence rate 25.7/1000 player-hours, 95% CI: 18.6–35.7), mainly in the foot (n=10) and the thigh (n=8). The risk of injury was lower for goalkeepers and higher in players with time-loss injuries in the past year, with trends towards higher risk for players with lower BMI and history of severe injuries. Lower limb contusions are the predominant injuries in men’s beach soccer. Our findings raise the question of protective foot gear to reduce foot contusions and fractures and can inform playing schedules of players at risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".