In a bid to give its national volleyball team an edge, Japan has enlisted the help of high-tech training robots. According to New Scientist, these bizarre-looking bots are used to mimic the opposing team's defense and are made up of three pairs of hands attached to a mobile torso. Mounted to a track, these new digital defense droids slide up and down to pre-set positions, allowing players to test out their spike shots against many different team formations. Known as the "block machine" these rapid robots can travel at speeds of up to 3.7 meters per second, easily outpacing human players. So far these training machines have been used successfully in several of training sessions for Japan's national woman's volleyball team.
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.