Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning
Researchers have developed a new framework using Temporal Graph Attention Networks (T-GAN) to identify distinct in-possession match phases in association football. This method analyzes spatiotemporal tracking data from German Bundesliga matches to distinguish between tactical intentions like invading opponent space, keeping possession, and scoring. The T-GAN model achieved high F1 scores, demonstrating its effectiveness in translating continuous player movement data into tactically meaningful representations for applications such as automated match annotation and playing-style profiling. AI
IMPACT This framework offers a novel approach to analyzing sports data, potentially improving automated annotation and tactical analysis in football.