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 compare these 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. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.

Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns

“…Meanwhile, the movement patterns extracted by AprioriClose and LCS algorithms were later filtered to exclude patterns containing more than 20 items. The studies [ 12 , 17 ] used similar parameter values to enable the extraction of large and longer-length frequent patterns from rugby union and rugby league data.…”

“…Frequent pattern mining algorithms have been applied in sports in various contexts such as automatic tactics detection in soccer matches [ 10 ], athlete performance monitoring [ 11 ] and discrimination of non-scoring and scoring outcomes between attacking and defending rugby union teams [ 12 ]. Nowadays, sequential pattern mining algorithms [ 13 , 14 ] are applied to sports data to profile players’ movements.…”

An Efficient Probabilistic Algorithm to Detect Periodic Patterns in Spatio-Temporal Datasets

“…There are three types of sequential pattern-mining algorithms: machine learning algorithms, algorithms based on mathematical techniques, and algorithms based on association rules. Machine learning algorithms require an objective function and a training dataset to define "correct" patterns [16,17]. This approach often involves a complex model selection process and hyperparameter tuning, which can be challenging for users who lack sufficient domain knowledge and experience.…”

Efficient model selection for predictive pattern mining model by safe pattern pruning

“…In this paper, we consider a class of machine learning models called predictive pattern mining. 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 …”