Anthropometric analysis and performance characteristics to predict selection in young male and female handball players

School-Age Talent Identification in Female Handball by a Mathematical Model: Equation Proposed from Anthropometry, Maturity Offset, Fitness and Technical Field-Tests

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.

Speed, Change of Direction Speed, and Lower Body Power in Young Athletes and Nonathletes According to Maturity Stage

The aim of this study was to establish the role of maturation on the development of physical performance in adolescent athletes and nonathletes. The total studied sample consisted of 231 participants (131 athletes: 72 boys with an average chronological age of 13.53 ± 0.7 and 59 girls with an average chronological age of 11.97 ± 0.8; 100 nonathletes: 47 boys with an average chronological age of 13.73 ± 0.47 and 53 girls with an average chronological age of 11.93 ± 0.33), distributed according to their biological maturity stage (Pre-, Mid-, and Post-Peak Height Velocity [PHV]) and to their gender. The assessment of physical performance was performed using the following tests: Countermovement jump (CMJ), countermovement jump with arm swing (CMJA), squat jump (SJ), five-jump test (5JT), 5 m sprint (5 m), 10 m sprint (10 m), 20 m sprint (20 m), T-test, Zig Zag, and Slalom. The differences in athletes according to biological maturity were identified in all variables except for 5 m (p = 0.33) and Slalom (p = 0.07), while in nonathletes the differences were found in 5JT (p = 0.01), 5 m (p = 0.02), 10 m (p = 0.01), and 20 m (p = 0.01) tests. Additionally, a significant interaction of gender and biological maturity was detected for CMJ (p = 0.03), CMJA (p = 0.01), and Zig Zag (p = 0.05) in athletes. The findings of the current study confirm the importance of maturity status in the assessment of physical performance. As a consequence, a more rational selection of talented athletes could be provided, also enabling the timely development of physical performance in nonathletes as a “window of opportunity”.

Applications of fitness function in Pubalgia affliction

Introduction. The relationships among variables are important in medicine and sport, and the most common approaches use linear correlation for optimization and prediction. Material and method. We propose to o novel usage of a known method (the genetic programming algorithm) to construct a nonlinear model in a practical case for rehabilitation find out the mathematical formula for a relationship among many variables . This study seeks to find a mathematical function to estimate the current state of the patient: healthy, affected by Pubalgia. Results and discussions. In the proposed application, a mathematical relationship is sought for five variables that express a state of health for patients, plantar footprint, plantar footprint – right, plantar pressure, plantar pressure – right and the surface of the body weight center. Conclusions. In this article, genetic programming has been proposed to construct a mathematical function to estimate the patient's condition possibly affected by Pubalgia. Keywords: e-health, rehabilitation, genetic algorithm,fitness function,