Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction
Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

TL;DR
This paper introduces a trajectory prediction-based method to evaluate off-ball scoring opportunities in soccer, providing a new indicator that correlates with player value and performance metrics.
Contribution
The study proposes a novel approach using graph variational recurrent neural networks to assess off-ball contributions in soccer, enhancing understanding of teamwork and scoring opportunities.
Findings
The proposed indicator correlates significantly with player salary.
The method effectively evaluates off-ball movement contributions.
Results outperform existing evaluation metrics.
Abstract
Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game do not have the ball for about 87 minutes on average. However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates. In this paper, we evaluate players who create off-ball scoring opportunities by comparing actual movements with the reference movements generated via trajectory prediction. First, we predict the trajectories of players using a graph variational recurrent neural network that can accurately model the relationship between players and predict the long-term trajectory. Next, based on the difference in the modified off-ball evaluation index between the actual and the predicted…
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Taxonomy
TopicsSports Performance and Training · Sports Analytics and Performance
