Once player tracking has been established as one of the main data sources in soccer, many challenges have emerged for data scientists, who attempt to recognize patterns from 2D trajectories in order to build tools that might help coaches to improve the performance of their teams. For instance, pass models predict where the ball should go next during pass events. However, existing models are mainly fed with players’ location and prior data, hence omitting critical pieces of information such as players’ ...
Once player tracking has been established as one of the main data sources in soccer, many challenges have emerged for data scientists, who attempt to recognize patterns from 2D trajectories in order to build tools that might help coaches to improve the performance of their teams. For instance, pass models predict where the ball should go next during pass events. However, existing models are mainly fed with players’ location and prior data, hence omitting critical pieces of information such as players’ body orientation. This paper presents a computational model to obtain pass feasibility maps, where player orientation is exploited and analysed. As a matter of fact, orientation proves to be crucial when modelling field-of-view and correct positioning of players, since it limits the potential receiving area of all candidates. Different proposals are given to evaluate the proposed pass feasibility map, reaching 0.46 and 0.79 in Top1 and Top3 accuracy, respectively, with a + 0.2 boost obtained after merging positional data with orientation.
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