The unpredictable talent selection in youth beach handball players
Bibliographic record
Abstract
This study identified key variables to differentiate selected and non-selected Brazilian young beach handball players using non-linear approaches. A total of 64 players (16.5 ± 0.8 years-old; 33 male) participated in the Brazilian National Team preparatory stage for the Beach Handball World Championships (2017), and were grouped in selected and non-selected players. Participants were assessed for anthropometrics and body composition, countermovement jump, and ball speed throwing. Comparisons between groups were tested, and the outcomes were analyzed using logistic regression (SPSS 25.0; p < 0.05). Network analysis was used to establish systemic non-linear interrelationships between variables, and the Expected Influence was calculated (Rstudio). For male players, increments of 1 kg in fat-free mass and 1 m/s in ball speed, increased the odds of being selected by 44% and 17 times, respectively. For females, 1 cm increase in jump height and in palm diameter, increased the odds of being selected by 29.5%, and 6.7 times, respectively. The non-linear approach showed body mass as the expected influence variable for both selected and non-selected male and female players, differently of those variables observed using the logistic regression. The results reflect the multidimensional predictors of talent in beach handball, and represent important finding for coaches and stakeholders.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".