Performance Factors in Women's Team Handball: Physical and Physiological Aspects—A Review

Team handball is an Olympic sport played professionally in many European countries. Nevertheless, a scientific knowledge regarding women's elite team handball demands is limited. Thus, the purpose of this article was to review a series of studies (n = 33) on physical characteristics, physiological attributes, physical attributes, throwing velocity, and on-court performances of women's team handball players. Such empirical and practical information is essential to design and implement successful short-term and long-term training programs for women's team handball players. Our review revealed that (a) players that have a higher skill level are taller and have a higher fat-free mass; (b) players who are more aerobically resistant are at an advantage in international level women team handball; (c) strength and power exercises should be emphasized in conditioning programs, because they are associated with both sprint performance and throwing velocity; (d) speed drills should also be implemented in conditioning programs but after a decrease in physical training volume; (e) a time-motion analysis is an effective method of quantifying the demands of team handball and provides a conceptual framework for the specific physical preparation of players. According to our results, there are only few studies on on-court performance and time-motion analysis for women's team handball players, especially concerning acceleration profiles. More studies are needed to examine the effectiveness of different training programs of women's team handball players' physiological and physical attributes.

Probabilistic world modelling for distributed team planning

This paper describes an application of decoupled probabilistic world modeling to achieve team planning. The research is based on the principle that tbe action selection mechanism of a member in a robot team cm select am effective action if a global world model is available to all team members. In the real world, the sensors are imprecise, and are individual to each robot, hence providing each robot a partial and unique view about the environment. We address this problem by creating a probabilistic global view on each agent by combining the perceptual information from each robot. This probsbilistie view forms the basis for selecting actions to achieve the team goal in a dynamic environment. Experiments have been carried ont to investigate the effectiveness of this principle using custom-built robots for real world performance, in addition, to extensive simulation results. The results show an improvement in team effectiveness when using probabilistic world modeling based on perception sharing for team planning.