Calvin Yeung
Papers
1
Total Citations
6
H-Index
1
About
Calvin Yeung is a leading researcher in sports analytics and multi-agent systems, with a focus on extracting and evaluating complex team strategies from spatiotemporal data. His most-cited work, "Unveiling Multi-Agent Strategies: A Data-Driven Approach for Extracting and Evaluating Team Tactics from Football Event and Freeze-Frame Data" (2024, 6 citations), pioneers a novel framework for decoding collective behavior in opposing teams—a challenge spanning game theory, robotics, and sports science. By integrating event and freeze-frame data, Yeung’s methodology reveals hidden tactical patterns, such as spatial formations and action sequences during possession, enabling quantitative assessment of team performance. This work bridges the gap between raw tracking data and actionable strategic insights, offering coaches and analysts a data-driven lens to understand opponent dynamics. Yeung’s contributions are particularly impactful in football analytics, where his approach has been recognized for its potential to transform training and match preparation. With a growing citation footprint, his research continues to influence both academic and applied domains, positioning him as a rising authority in computational sports science and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
- 1