6 papers
Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Valuing player actions in football requires accounting for strategic interactions among 22 players, including off-ball movements and defensive positioning. Existing reinforcement-l…
Prediction-based evaluation of back-four defense with spatial control in soccer
Soujanya Dash, Kenjiro Ide, Rikuhei Umemoto +2
Defensive organization is critical in soccer, particularly during negative transitions when teams are most vulnerable. The back-four defensive line plays a decisive role in prevent…
Expandable Decision-Making States for Multi-Agent Deep Reinforcement Learning in Soccer Tactical Analysis
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Invasion team sports such as soccer produce a high-dimensional, strongly coupled state space as many players continuously interact on a shared field, challenging quantitative tacti…
Interpretable Low-Dimensional Modeling of Spatiotemporal Agent States for Decision Making in Football Tactics
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Understanding football tactics is crucial for managers and analysts. Previous research has proposed models based on spatial and kinematic equations, but these are computationally e…
OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer
Calvin Yeung, Kenjiro Ide, Taiga Someya +1
Sports analytics has become both more professional and sophisticated, driven by the growing availability of detailed performance data. This progress enables applications such as ma…
AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements
Calvin Yeung, Kenjiro Ide, Keisuke Fujii
Image understanding is a foundational task in computer vision, with recent applications emerging in soccer posture analysis. However, existing publicly available datasets lack comp…