The FIFA 11+ Program Significantly Enhances Physical Performance and Dynamic Balance in Male Handball Players
Bibliographic record
Abstract
Injury prevention and performance enhancement are key objectives in sports training. The FIFA 11+ program, originally developed to reduce injury risks, has gained attention for its potential benefits in improving physical performance and dynamic balance. This study aimed to examine the impact of an 8-week FIFA 11+ training program on vertical jump, Illinois Agility, and Y-Balance Test (YBT) performances in adult male handball players. Twenty-five players from two senior national male handball teams were recruited and randomly assigned to an experimental group (n = 13) or a control group (n = 11). Assessments were conducted before and after the intervention, including the countermovement jump (CMJ), the Illinois Agility Test (IAT), and the Y-Balance Test (YBT), which measured anterior (AT), posteromedial (PM), and posterolateral (PL) reach directions as well as a composite score (CS). The FIFA 11+ group showed significant improvements after the eight-week program, with increased CMJ (p = 0.013) and reduced IAT time (p < 0.001). Dynamic balance, as measured by the YBT, improved significantly in both lower limbs (p = 0.022–0.001), with enhanced postural stability across multiple directions (F = 6.92–20.23, p = 0.022–0.001, ηp2 = 0.366–0.628, power = 0.68–0.98). In contrast, the control group exhibited minimal or no significant changes. While the results suggest that the FIFA 11+ program can improve specific performance outcomes in this population, the relatively small sample size and focus on a single sport and age group warrant caution in generalizing these findings. Further studies involving larger and more diverse cohorts are recommended.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".