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\item motivation for the development of a MI based metric  \end{enumerate}  The current works on soccer match analysis managed to give semantic data and most of the time they used extra data brought in several matches or championship. In this project we focus on the detection of clusters an unsupervised classification  of the  players based on their trajectories during a short period of time. time using clustering method.  This information is deeply woven into the fabric of team strategy analysis since it is related to the placement global placing  of the players and the centers of the cluster may correspond to the leader of the game during the analysed time interval. Furthermore we limited our input to the video of a match filmed with a multi-camera system. We managed to find patterns in the paths of the players clustering players trajectories. The problem of tracking players with multiple camera have been achieved consistently in \cite{Ben_Shitrit_2011} even if their paths may intersect over long period of time. Our contribution is to provided a well fitted similarity measure between trajectories which allows the clustering of the players' trajectories according to the interdependency of their path using clustering. Yet only discrete trajectories are available in the form of array which can be of different sizes with different time discretization or with different speed.