Sports Strategy Analytics Using Probabilistic Reasoning

Abstract

The advance of analytics technology has attracted more attention and adoption from sports, although modeling and analyzing the dynamic (and uncertain) behaviors of sports are challenging. Formal methods have been strongly recommended to deal with complex systems by their rigorous semantics and powerful reasoning capabilities. In this paper, we present our initiative as the first to apply probabilistic model checking techniques to strategy analytics for tennis based on Markov Decision Processes (MDP). Our approach can derive insights such as prediction of winning chances and identification of best improvement. We evaluate the effectiveness of our approach through real-life case study.