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\section{Introduction}  \begin{enumerate}  \item exposition of the problem of trajectory analysis for soccer  \item motivation for the development of a MI based metric  \end{enumerate}  The current works on soccer match analysis used extra high level data and annotation (such as whoscored, transfertmark) which require human annotation and so preprocessing. In this project we limited our input to the video of a match filmed with a multi-camera system. We focus on an unsupervised classification of the players based on clustering of their trajectories which are extracted automatically from the video of a match during a short period of time. This information is deeply woven into the fabric of team strategy analysis since it is related to the global placing of the players and the centers of the cluster may correspond to the leader of the game at that period. Thus we managed to find patterns in the paths of the players clustering players trajectories.