[3G4-OS-15b] 移動系列のデータマイニングと機械学習(2/2)

[3G4-OS-15b-05] Evaluation of soccer players to create scoring opportunities for teammates based on their trajectory prediction

Masakiyo Teranishi¹, Kazushi Tsutsui¹, Kazuya Takeda¹, 〇Keisuke Fujii^1,2,3 (1. Nagoya University, 2. RIKEN, 3. JST Presto)

Keywords:Sports, Movement sequences, Multi-agent

Soccer is a game in which many players and the ball interact in complex ways. Regarding the quantitative evaluation of soccer attackers, there have been many and few studies on the player with and without the ball, respectively. However, it is still difficult to evaluate an attacking player without the ball and intention to receive it, and to reveal how movement contributes to the creation of scoring opportunities compared to typical (or predicted) movements. In this paper, we evaluate players who create off-ball scoring opportunities by comparing the reference movements generated by trajectory prediction with actual movements. In the proposed method, first, the trajectory is predicted 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 existing off-ball evaluation index between the actual data and the predicted trajectory, we evaluate how the actual movement contributes to scoring opportunity compared to the predicted movement as a reference. In the verification, we show that the evaluation of the proposed method is intuitive, using the relationship with the scores with all 18 teams in the Japanese professional soccer league and the example of one game.