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Given a set of sequences comprised of time-ordered events, sequential patternmining is useful to identify frequent subsequences from different sequences orwithin the same sequence. However, in sport, these techniques cannot determinethe importance of particular patterns of play to good or bad outcomes, which isoften of greater interest to coaches and performance analysts. In this study,we apply a recently proposed supervised sequential pattern mining algorithmcalled safe pattern pruning (SPP) to 490 labelled event sequences representingpassages 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 betweenscoring and non-scoring outcomes from both the team's and opposition teams'perspectives, with the most frequent patterns obtained with well-knownunsupervised sequential pattern mining algorithms when applied to subsets ofthe original dataset, split on the label. Our obtained results found thatlinebreaks, successful lineouts, regained kicks in play, repeatedphase-breakdown play, and failed exit plays by the opposition team wereidentified as as the patterns that discriminated most between the team scoringand not scoring. Opposition team linebreaks, errors made by the team,opposition team lineouts, and repeated phase-breakdown play by the oppositionteam were identified as the patterns that discriminated most between theopposition team scoring and not scoring. It was also found that, by virtue ofits supervised nature as well as its pruning and safe-screening properties, SPPobtained a greater variety of generally more sophisticated patterns than theunsupervised models, which are likely to be of more utility to coaches andperformance analysts.

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