Football is one of the biggest sports in the world. Professional teams track their player's positions using GPS (Global Positioning System). This report is divided into two parts, both focusing on applying collective motion to football. % The goal of the first part was to both see if a set of cheaper GPS units could be used to analyze the collective motion of a youth football team. 15 football players did two experiments and played three versus three football matches against each other while wearing a GPS. The first experiment measured the player's ability to control the ball while the second experiment measured how well they were able to move together as a team. Different measurements were measured from the match and Spearman correlations were calculated between measurements from the experiments and matches. Players which had good ball control also scored more goals in the match and received more passes. However, they also took the middle position in the field which naturally is a position which receives more passes. Players which were correlated during the team experiment were also correlated with team-members in the match. But, this correlation was weak and the experiment should be done again with more players. The GPS did not work well in the team experiment but have potential to work well in experiments done on a normal-sized football field. % The goal of the second part of the report was to visualize collective motion, more specifically leader-follower relations, in football which can be used as a basis for further research. This is done by plotting the player's positions at each time step to a user interface. Between each player, a double pointed arrow is drawn, where each side of the arrow has a separate color and arrow width. The maximum time lag between the between the two players is shown as the "pointiness" of the arrow while the color of the arrow show the maximum time lag correlation. The user can change the metrics the correlations are based of. As a compliment to the lagged correlation, a lag score is defined which tell the user how strong the lagged correlation is.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-269457 |
| Date | January 2015 |
| Creators | Rosén, Emil |
| Publisher | Uppsala universitet, Avdelningen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 15068 |
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