Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union.

ABSTRACT: Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team's matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team's and opposition teams' perspectives using SPP, and com

SUBMITTER: Bunker R 

PROVIDER: S-EPMC8460049 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature