Identification of Performance Profiles in Handball Throws: A Multifactorial Analysis of Physical and Anthropometric Capabilities

• Grant number: 24/17794-4
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: April 01, 2025
• End date: July 31, 2025
• Field of knowledge: Health Sciences - Physical Education
• Principal Investigator: Enrico Fuini Puggina
• Grantee: Maraysa Spagnollo da Silva
• Supervisor: Juliana Exel Santana
• Host Institution: Escola de Educação Física e Esporte de Ribeirão Preto (EEFERP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Institution abroad: University of Vienna, Austria
• Associated to the scholarship: 24/05326-6 - The relationship between muscle strength and power in throw velocity., BP.IC

Abstract

The ability to perform a fast throw is essential for handball athletes as it can increase the chances of scoring by reducing the goalkeeper's reaction time. Thus, it is necessary to investigate the throwing movement in this sport in a complex manner, observing not only the physical abilities but also how they relate to performance. Therefore, this study aims to verify the possible existence of a correlation between anthropometric variables, performance evaluations of strength, power, and coordination of upper and lower limbs. For this purpose, 12 handball athletes will undergo the following tests: one repetition maximum (RM) in the bench press, a power test in the bench press using a linear potentiometer at three intensities (30%-1RM, 60%-1RM, and 90%-1RM), seated medicine ball throw, and ballistic push-up. The participants will also perform specific handball tests, such as the standing throw from the 7-meter line and a running three-step throw from the 9-meter line. To identify the impact of each variable assessed in the physical tests, the Pearson or Spearman Correlation Coefficient will be used. In order to evaluate the ability to predict throwing performance from each variable and analyze the contribution and interaction between them as multiple factors, a multiple regression test will be applied. Finally, to identify potential performance profiles among athletes, principal component analysis and clustering will be employed.