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

• Published in: PloS one Vol. 19; no. 5; p. e0301608
• Main Authors: Adeyemo, Victor Elijah, Palczewska, Anna, Jones, Ben, Weaving, Dan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2024; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Analysis; Athletes; Athletic Performance - physiology; Athletic recruitment; Big Data; Biology and Life Sciences; Business metrics; Classification; Computer and Information Sciences; Data mining; Data Mining - methods; Engineering and Technology; Football - physiology; Global positioning systems; GPS; Health aspects; Humans; Investigations; Machine Learning; Male; Methods; Movement - physiology; Multilayers; Pattern analysis; Physical Sciences; Players; Research and Analysis Methods; Rugby; Rugby football players; Sensors; Similarity; Soccer; Social Sciences; South Africa
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0301608

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. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms' movement patterns and machine learning classification modelling identified the best algorithm's movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification 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 and non-sequential movement patterns for separating groups of players.