Anthropometric and physical performance characteristics of professional handball players: influence of playing position
Bibliographic record
Abstract
The aims of the study were to examine the anthropometric and physical performance characteristics of professional handball players classified by playing position.Twenty-one competitors (age: 25.2±5.1 years) were categorized as backs, pivots, wings or goalkeepers. Measures included anthropometrics (body height and mass), scores on the Yo-Yo Intermittent Recovery Test (total distance covered, TD), repeated-sprint ability (6 repetitions of 2x15-m shuttle sprints with recording of best time for a single trial, RSAbest) and performance on a complex handball test (HBKT) of throw slap (TS) and throw jump (TJ) with and without precision.The anthropometric data revealed a significantly lower body height for wings and pivots than for goalkeepers. Wings, pivots and goalkeepers were significantly shorter than backs, but had a similar BMI. The TD was greater for the wings (2.400 m) than for backs (1.832 m) and pivots (2.067m). Wings also achieved a better RSAbest (5.41 s) than backs (5.68 s) or pivots (5.82 s). Body height was significantly related to throw slap (TS) and jump (JT) (r=0.53, P<0.01; r=0.51, P<0.01 respectively). No significant difference (P=0.675; η2=0.009) was seen between JT with precision and JT without precision.Substantial differences of body build and physical performance between playing positions underline the importance of a careful assignment of such positions and the development of position-specific training for professional handball players by modifying both intermittent aerobic and anaerobic endurance components of training sessions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".