  Improvements in tracking technology through optical and computer vision
systems have enabled a greater understanding of the movement-based behaviour of
multiple agents, including in team sports. In this study, a Multi-Agent
Statistically Discriminative Sub-Trajectory Mining (MA-Stat-DSM) method is
proposed that takes a set of binary-labelled agent trajectory matrices as input
and incorporates Hausdorff distance to identify sub-matrices that statistically
significantly discriminate between the two groups of labelled trajectory
matrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectory
matrices 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 the
ball by the last passer, and labelled as effective or ineffective based on a
definition of attack effectiveness that we devise in the current study. After
identifying appropriate parameters for MA-Stat-DSM by iteratively applying it
to all matches involving the two top- and two bottom-placed teams from the
2015/16 NBA season, the method was then applied to selected matches and could
identify and visualize the portions of plays, e.g., involving passing, on-,
and/or off-the-ball movements, which were most relevant in rendering attacks
effective or ineffective.
