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Improvements in tracking technology through optical and computer visionsystems have enabled a greater understanding of the movement-based behaviour ofmultiple agents, including in team sports. In this study, a Multi-AgentStatistically Discriminative Sub-Trajectory Mining (MA-Stat-DSM) method isproposed that takes a set of binary-labelled agent trajectory matrices as inputand incorporates Hausdorff distance to identify sub-matrices that statisticallysignificantly discriminate between the two groups of labelled trajectorymatrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectorymatrices representing attacks consisting of the trajectories of five agents(the ball, shooter, last passer, shooter defender, and last passer defender),were truncated to correspond to the time interval following the receipt of theball by the last passer, and labelled as effective or ineffective based on adefinition of attack effectiveness that we devise in the current study. Afteridentifying appropriate parameters for MA-Stat-DSM by iteratively applying itto all matches involving the two top- and two bottom-placed teams from the2015/16 NBA season, the method was then applied to selected matches and couldidentify and visualize the portions of plays, e.g., involving passing, on-,and/or off-the-ball movements, which were most relevant in rendering attackseffective or ineffective.

Multi-agent statistical discriminative sub-trajectory mining and an application to NBA basketball