  In the domain of Sport Analytics, Global Positioning Systems devices are
intensively used as they permit to retrieve players' movements. Team sports'
managers and coaches are interested on the relation between players' patterns
of movements and team performance, in order to better manage their team. In
this paper we propose a Cluster Analysis and Multidimensional Scaling approach
to find and describe separate patterns of players movements. Using real data of
multiple professional basketball teams, we find, consistently over different
case studies, that in the defensive clusters players are close one to another
while the transition cluster are characterized by a large space among them.
Moreover, we find the pattern of players' positioning that produce the best
shooting performance.
