  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 from the 2018 Japan Top League.
We compare the SPP-obtained patterns that are the most discriminative between
scoring and non-scoring outcomes from both the team's and opposition teams'
perspectives, with the most frequent patterns obtained with well-known
unsupervised sequential pattern mining algorithms when applied to subsets of
the original dataset, split on the label. Our obtained results found that
linebreaks, successful lineouts, regained kicks in play, repeated
phase-breakdown play, and failed exit plays by the opposition team were
identified as as the patterns that discriminated most between the team scoring
and not scoring. Opposition team linebreaks, errors made by the team,
opposition team lineouts, and repeated phase-breakdown play by the opposition
team were identified as the patterns that discriminated most between the
opposition team scoring and not scoring. It was also found that, by virtue of
its supervised nature as well as its pruning and safe-screening properties, SPP
obtained a greater variety of generally more sophisticated patterns than the
unsupervised models, which are likely to be of more utility to coaches and
performance analysts.
