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.

应用模式挖掘算法提取运动大数据中的运动模式，可以通过促进对运动的更细致评估，改善训练的具体性。在研究中，使用三种模式挖掘算法，验证在职业橄榄球联赛中打者和边锋的运动轨迹中挖掘最佳运动模式的算法，并确定不同算法之间提取运动模式的相似性。结果表明，使用LCCspm挖掘的运动轨迹最能区分运动员的位置，而机器学习分类算法的多层感知器算法在预测中的精度最高。因此，建议在分组运动员时提取连续的运动模式。

基于运动模式的橄榄球联赛球员定位组分离的模式挖掘算法识别