Collision with opponents—but not foul play—dominates injury mechanism in professional men’s basketball
Bibliographic record
Abstract
BACKGROUND: To identify injury patterns and mechanisms in professional men's basketball by means of video match analysis. METHODS: In Germany, injuries are registered with the statutory accident insurance for professional athletes (VBG) by clubs or club physicians as part of occupational accident reporting. Moderate and severe injuries (absence of > 7 days) sustained during basketball competition in one of four seasons (2014-2017 and 2018-2019) in the first or second national men's league in Germany were prospectively analyzed using a newly developed standardized observation form. Season 2017-2018 was excluded because of missing video material. RESULTS: Video analysis included 175 (53%) of 329 moderate and severe match injuries. Contact patterns categorized according to the different body sites yielded eight groups of typical injury patterns: one each for the head, shoulders, and ankles, two for the thighs, and three for the knees. Injuries to the head (92%), ankles (76%), shoulders (70%), knees (47%), and thighs (32%) were mainly caused by direct contact. The injury proportion of foul play was 19%. Most injuries (61%) occurred in the central zone below the basket. More injuries occurred during the second (OR 1.8, p = 0.018) and fourth quarter (OR 1.8, p = 0.022) than during the first and third quarter of the match. CONCLUSION: The eight identified injury patterns differed substantially in their mechanisms. Moderate and severe match injuries to the head, shoulders, knees, and ankles were mainly caused by collision with opponents and teammates. Thus, stricter rule enforcement is unlikely to facilitate safer 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".