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对橄球联赛球员的模式挖掘算法的识别 基于运动模式的位置组分离.

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科学领域:

  • 体育科学 运动科学 运动科学
  • 数据挖掘 数据挖掘
  • 机器学习 机器学习

背景情况:

  • 体育大数据分析为精细的运动评估提供了机会.
  • 玩家运动概况是提高训练特异性的关键.

研究的目的:

  • 在职业橄球联盟中识别最佳运动模式,以进行球员分析.
  • 为了比较三个模式挖掘算法 (LCCspm,LCS,AprioriClose) 的有效性.
  • 量化模式的相似性,并对球员的位置进行分类.

主要方法:

  • 应用LCCspm,LCS和AprioriClose算法,从319场职业橄球联盟比赛中提取运动模式.
  • 利用Jaccard相似性来测量算法之间的模式一致性.
  • 采用机器学习分类 (多层感知器) 来评估位置分离的模式实用性.

主要成果:

  • LCCspm和LCS模式显示中等相似性 (雅卡德得分为0.19);AprioriClose模式显示微不足道的相似性.
  • 由LCCspm提取的封闭连续运动模式显示出在区分球员位置方面具有卓越的能力.
  • 多层感知器模型实现了91.02%的准确性,精度,回忆和F1分数为0.91.

结论:

  • 对于玩家分析,建议采用封闭的连续运动模式,而不是非连续或非顺序的模式.
  • LCCspm算法有效地提取运动模式,以便在橄球中准确地进行位置分类.
  • 来自模式挖掘的数据驱动洞察力可以显著提高体育表现分析.