Temporal Match Analysis and Recommending Substitutions in Live Soccer Games

Soccer is one of the most complex and dynamic games. It is challenging to figure out the game’s pattern in real-time. We propose a novel network metric and entropy-based live soccer analytic framework (NMELSA) that identifies the opponent team’s tactics in a live soccer match by observing all the events until the specified minute of the game. We design a live game replacement model which recommends substitute players based on the on-field players’ live game ratings. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.