Characterizing Multi-Agent Team Behavior from Partial Team Tracings: Evidence from the English Premier League

Real-world AI systems have been recently deployed which can automatically analyze the plan and tactics of tennis players. As the game-state is updated regularly at short intervals (i.e. point-level), a library of successful and unsuccessful plans of a player can be learnt over time. Given the relative strengths and weaknesses of a player’s plans, a set of proven plans or tactics from the library that characterize a player can be identified. For low-scoring, continuous team sports like soccer, such analysis for multi-agent teams does not exist as the game is not segmented into “discretized” plays (i.e. plans), making it difficult to obtain a library that characterizes a team’s behavior. Additionally, as player tracking data is costly and difficult to obtain, we only have partial team tracings in the form of ball actions which makes this problem even more difficult. In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, w...

• Type: Journal
• Year: 2012
• Where: AAAI
• Authors: Patrick Lucey, Alina Bialkowski, Peter Carr, Eric Foote, Iain Matthews
• Keywords: AAAI 2012, English Premier League, English Premier League Soccer, Intelligent Agents, Premier League Soccer