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Space evaluation based on pitch control using drone video in Ultimate
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Ultimate is a sport in which teams of seven players compete for points by passing a disc into the end zone. A distinctive aspect of Ultimate is that the player holding the disc is unable to move, underscoring the significance of creating space to receive passes. Despite extensive research into space evaluation in sports such as football and basketball, there is a paucity of information available for Ultimate. This study focuses on the 3-on-3 format, which is widely practiced in Ultimate, and evaluates space during offensive play. The data collection process entailed the use of drones for filming and the subsequent correction of the angles for the purpose of obtaining positional data. The model is derived from the pitch control model of soccer and adapted to the rules of Ultimate, where the player holding the disc is stationary. The integration of position and distance weights with pitch control values enables the derivation of space evaluation metrics. The findings of this study indicate that movement to create space and accurate passing into that space are both significant factors in scoring. The code is available at https://github.com/shunsuke-iwashita/USO.
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Cited by 1 Pith paper
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Velocity Completion Task and Method for Event-based Player Positional Data in Soccer
A graph recurrent neural network can estimate player velocities from event-time positions in soccer, outperforming a rule-based baseline and yielding more accurate pitch control and off-ball scoring opportunity values.
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