期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：314-319
出版社：Newswood and International Association of Engineers
摘要：Recently, it has been regarded as important in
many sports fields to evaluate the tactics and athletes using
actual play records. In this paper, as a first step towards
a quantitative evaluation of the strategies in football games,
we propose an algorithm for discovering frequent patterns on
simultaneous trajectories of multiple football players. In the
algorithm, given trajectories are firstly converted into a set
of labeled sub-trajectories corresponding to the interval-based
events. A pattern enumeration algorithm is then applied to the
obtained interval-based events with a consideration of the order
of events, the time difference and the spatial spread of subtrajectories.
We introduce variables for subjects of events (subtrajectories)
in the pattern. By using variables, we can recognize
which events were played by the same player and which events
were played differently. In addition, it is possible to extract
a pattern which absorb the difference of concrete players.
To evaluate the proposed algorithm, we conduct experiments
using real trajectory datasets on nine matches in Japanese
professional football league. The results on the computation
time and the number of extracted patterns show the feasibility
and effectiveness of the algorithm. In addition, we succeeded
in extracting meaningful patterns representing certain offensive
and defensive strategies formed by multiple football players.
关键词：trajectory mining; frequent patterns; sequential;
patterns; football
