RESUMO
The present study aimed to identify parameters that best discriminate between high-level and scholar-level players for the Brazilian 13-14-year-old girl's handball and propose a mathematical model to identify sports talent for handball. The sample was made up of all available handball players comprising these two groups: 100 girls who participated in the high-level handball championship in Brazil and 53 girls (age 13-14 years) as finalists of the school-level games in one region of Brazil. We assess the anthropometric profile, maturity offset, physical fitness, and technical skills for handball. To propose the equation, the Discriminant Function Analysis method was used. The discriminant function was significant (p ≤ .05), had a good canonical correlation (0.590), and still had an average Wilk Lambda (0.652). The variables considered in the talent identification model included: 1. flexibility, 2. abdominal strength, 3. lower limbs muscle power, 4. agility, 5. defensive movement and 6. slalom with ball. The values from the equation for identifying school-age athletes with high motor skills and performance for handball can be classified by a cutoff point (Y = 0.192). The results showed that the mathematical-model obtained was able to select school-age athletes with high motor skills for handball, and with the profile for participation in high-level championships.
RESUMO
The objective of this study was to identify parameters that best discriminate between selected and non-selected players for the Brazilian under-19 men's volleyball team and propose mathematical models to identify high-performance players. To this end, 18 selected (16.89±0.96 years) and 138 non-selected (16.91±0.74 years) players for the under-19 team were assessed for the training profile, anthropometric profile, and physical performance level. The discriminant function analysis was used to build the models, with a significance of α<0.05. The spike jump reach showed a greater correlation with the discriminant scores obtained in the two models (r=0.701; r=0.782). The 10 variables included in Model 1 helped identify 88.9% of the players selected in their group of origin; Model 2 - obtained by the spike jump reach and duration of playing experience - identified 83.3% of the players selected. Therefore, coaches should be aware that differences between the selected and non-selected players are multi-factorial, with the spike jump reach being the most relevant assessment factor. Furthermore, good players for the selection can be identified using the two models: Model 1 promises greater success with ten assessments, whereas Model 2 allows the identification of suitable players for the under-19 men's volleyball team with only two simple assessments.
