Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques
Jan Van Haaren*, KU Leuven; Horesh Ben Shitrit, PlayfulVision; Jesse Davis, KU Leuven; Pascal Fua, EPFL
This paper proposes a relational-learning based approach for discovering strategies in volleyball matches based on optical tracking data. In contrast to most existing methods, our approach permits discovering patterns that account for both spatial (that is, partial conﬁgurations of the players on the court) and temporal (that is, the order of events and positions) aspects of the game. We analyze both the men’s and women’s ﬁnal match from the 2014 FIVB Volleyball World Championships, and are able to identify several interesting and relevant strategies from the matches.
Filed under: Frequent Pattern Mining