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

Players Movements and Team Shooting Performance: a Data Mining approach for Basketball