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

TL;DR

This study compares pattern mining algorithms to identify the most effective method for profiling rugby league players' movement patterns, demonstrating that closed contiguous patterns best differentiate player positions with high classification accuracy.

Contribution

It validates and compares three pattern mining algorithms, recommending the use of closed contiguous patterns for effective player position separation in rugby.

Findings

LCCspm and LCS share low similarity (0.19 Jaccard score).

AprioriClose patterns show no significant similarity with others.

LCCspm-based patterns achieve over 91% classification accuracy.

Abstract

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. As there are various pattern mining algorithms, this study aimed to validate which algorithm discovers the best set of movement patterns for player movement profiling in professional rugby league and the similarity in extracted movement patterns between the algorithms. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Machine learning classification algorithms were used to identify which algorithm gives the best set of movement patterns to separate playing positions with Jaccard similarity score identifying the extent of similarity between algorithms' movement patterns. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant similarity with LCCspm and LCS patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive movement patterns for separating groups of players.