Efficient team actions: Outline of a theory of teams in sport

So far, social psychology in sport has preliminary focused on team cohesion, and many studies and meta analyses tried to demonstrate a relation between cohesiveness of a team and it's performance. How a team really co-operates and how the individual actions are integrated towards a team action is a question that has received relatively little attention in research. This may, at least in part, be due to a lack of a theoretical framework for collective actions, a dearth that has only recently begun to challenge sport psychologists. In this presentation a framework for a comprehensive theory of teams in sport is outlined and its potential to integrate the following presentations is put up for discussion. Based on a model developed by von Cranach, Ochsenbein and Valach (1986), teams are information processing organisms, and team actions need to be investigated on two levels: the individual team member and the group as an entity. Elements to be considered are the task, the social structure, the information processing structure and the execution structure. Obviously, different task require different social structures, communication and co-ordination. From a cognitivist point of view, internal representations (or mental models) guide the behaviour mainly in situations requiring quick reactions and adaptations, were deliberate or contingency planning are difficult. In sport teams, the collective representation contains the elements of the team situation, that is team task and team members, and of the team processes, that is communication and co-operation. Different meta-perspectives may be distinguished and bear a potential to explain the actions of efficient teams. Cranach, M. von, Ochsenbein, G., & Valach, L. (1986).The group as a self-active system: Outline of a theory of group action. European Journal of Social Psychology, 16, 193-229.

Foundations of stochastic geometry and theory of random sets

The first section of this chapter starts with the Buffon problem, which is one of the oldest in stochastic geometry, and then continues with the definition of measures on the space of lines. The second section defines random closed sets and related measurability issues, explains how to characterize distributions of random closed sets by means of capacity functionals and introduces the concept of a selection. Based on this concept, the third section starts with the definition of the expectation and proves its convexifying effect that is related to the Lyapunov theorem for ranges of vector-valued measures. Finally, the strong law of large numbers for Minkowski sums of random sets is proved and the corresponding limit theorem is formulated. The chapter is concluded by a discussion of the union-scheme for random closed sets and a characterization of the corresponding stable laws.