Abstract
Dominant regions are defined as regions of the pitch where a player can reach before any other and are commonly determined without considering the free-spaces in the pitch. We presented an approach to football players’ dominant regions analysis, based on movement models created from players’ positions, displacement, velocity, and acceleration vectors. 109 Brazilian male professional football players were analysed during official matches, computing over 15 million positional data obtained by video-based tracking system. Movement models were created based on players’ instantaneous vectorial kinematics variables, then probabilities models and dominant regions were determined. Accuracy in determining dominant regions by the proposed model was tested for different time-lag windows. We calculated the areas of dominant, free-spaces, and Voronoi regions. Mean correct predictions of dominant region were 96.56%, 88.64%, and 72.31% for one, two, and three seconds, respectively. Dominant regions areas were lower than the ones computed by Voronoi, with median values of 73 and 171 m2, respectively. A median value of 5537 m2 was presented for free-space regions, representing a large part of the pitch. The proposed movement model proved to be more realistic, representing the match dynamics and can be a useful method to evaluate the players’ tactical behaviours during matches.
Highlights
Dominant regions are defined as regions of the pitch where a player can reach before any other and are commonly determined without considering the free-spaces in the pitch
A total of 109 Brazilian male professional football players was analysed in this study during four official matches of the 2014 Serie A2 of the São Paulo State league resulting in 15,112,163 positional data samples
The mean percentage of correct prediction found for T1 was 96.56%, for T2 88.64%, and for T3 72.31%
Summary
Dominant regions are defined as regions of the pitch where a player can reach before any other and are commonly determined without considering the free-spaces in the pitch. We presented an approach to football players’ dominant regions analysis, based on movement models created from players’ positions, displacement, velocity, and acceleration vectors. A movement model was created considering the position, displacement, and velocity of the players improving the determination of the dominant region, making it more compatible with human physical c apacity[8,14]. The probabilistic movement model p roposed[17] considers the players’ position, displacement, and velocity to determine the dominant regions in a time-lag window of one second. Our purpose was to determine a novel dominant region calculation based on a movement model that considers players’ positions, displacement, velocity, and acceleration vectors obtained from official matches data. We believe that displacements with different accelerations, such as positive and negative accelerations, cannot fit into the same movement model, in our approach several movement models would be generated
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