Are Elite Soccer Teams’ Preseason Training Sessions Associated With Fewer In-Season Injuries? A 15-Year Analysis From the Union of European Football Associations (UEFA) Elite Club Injury Study
Bibliographic record
Abstract
Background: Preseason training develops players’ physical capacities and prepares them for the demands of the competitive season. In rugby, Australian football, and American football, preseason training may protect elite players against in-season injury. However, no study has evaluated this relationship at the team level in elite soccer. Purpose/Hypothesis: The aim of this study was to investigate whether the number of preseason training sessions completed by elite soccer teams was associated with team injury rates and player availability during the competitive season. It was hypothesized that elite soccer teams who participate in more preseason training will sustain fewer injuries during the competitive season. Study Design: Descriptive epidemiology study. Methods: We used the Union of European Football Associations (UEFA) injury dataset to analyze 44 teams for up to 15 seasons (total, 244 team-seasons). Separate linear regression models examined the association between the number of team preseason training sessions and 5 in-season injury measures. Injury-related problems per team were quantified by totals of the following: (1) injury burden, (2) severe injury incidence, (3) training attendance, (4) match availability, and (5) injury incidence. Results: Teams averaged 30 preseason training sessions (range, 10-51). A greater number of preseason training sessions was associated with less injury load during the competitive season in 4 out of 5 injury-related measures. Our linear regression models revealed that for every 10 additional preseason training sessions that the team performed, the in-season injury burden was 22 layoff days lower per 1000 hours ( P = .002), the severe injury incidence was 0.18 severe injuries lower per 1000 hours ( P = .015), the training attendance was 1.4 percentage points greater ( P = .014), and the match availability was 1.0 percentage points greater ( P = .042). As model fits were relatively low (adjusted R 2 = 1.3%-3.2%), several factors that contribute to in-season injury outcomes were unaccounted for. Conclusion: Teams that performed a greater number of preseason training sessions had “healthier” in-season periods. Many other factors also contribute to in-season injury rates. Understanding the benefit of preseason training on in-season injury patterns may inform sport teams’ planning and preparation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".