[2E1-GS-10] AI application:

[2E1-GS-10-02] Velocity Completion Task and Method for Event-based Player Positional Data in Group Sports

〇Rikuhei Umemoto¹, Keisuke Fujii^1,2 (1. Nagoya University, 2. RIKEN)

Keywords:Information Completion, Sports, Machine Learning

Recently, new team sports data, including event-based player positional data, has been publicly released in soccer, raising expectations for advancements in analytical methods for scouting and recruitment. However, this data does not include full-time player positional information, making it challenging to calculate player velocities for each event and to apply space evaluation methods. This study proposes a method to complete player velocity using only event-based positional data. Furthermore, we evaluate the applicability of existing space evaluation methods based on the completed velocity. Experiments using soccer data show that neural network methods considering graph structures and temporal information outperform rule-based methods, achieving more minor velocity completion errors and producing space evaluation results closer to complete tracking data.