Experts in racket sports like tennis and badminton use tactical analysis to gain insight into competitors' playing styles. Many data-driven methods apply pattern mining to racket sports data -- which is often recorded as multivariate event sequences -- to uncover sports tactics. However, tactics obtained in this way are often inconsistent with those deduced by experts through their domain knowledge, which can be confusing to those experts. This work introduces RASIPAM, a RAcket-Sports Interactive PAttern Mining system, which allows experts to incorporate their knowledge into data mining algorithms to discover meaningful tactics interactively. RASIPAM consists of a constraint-based pattern mining algorithm that responds to the analysis demands of experts: Experts provide suggestions for finding tactics in intuitive written language, and these suggestions are translated into constraints to run the algorithm. RASIPAM further introduces a tailored visual interface that allows experts to compare the new tactics with the original ones and decide whether to apply a given adjustment. This interactive workflow iteratively progresses until experts are satisfied with all tactics. We conduct a quantitative experiment to show that our algorithm supports real-time interaction. Two case studies in tennis and in badminton respectively, each involving two domain experts, are conducted to show the effectiveness and usefulness of the system.
翻译:网球和羽毛球等电动体育专家利用战术分析来深入了解竞争者玩耍的风格。许多数据驱动的方法将模式采矿方法应用于电动体育数据 -- -- 通常记录为多变事件序列 -- -- 以发现体育战术。然而,以这种方式获得的战术往往与专家通过其领域知识推导出来的战术不一致,这可能会令这些专家产生混淆。这项工作引入了RASIPAM,这是一个RACKET-Sport-Sport 互动的Pattern Mining系统,让专家将其知识纳入数据开采算法中,以便以互动的方式发现有意义的策略。RASIPAM由一种基于约束性的采矿算法组成,响应专家的分析要求:专家们提供了以直观书面语言寻找战术的建议,这些建议被转化为对算法的制约。RASIPAM还引入了一种定制的视觉界面,让专家将新战术与原始策略进行比较,并决定是否应用某种调整。这种互动的工作流程反复进行,直到专家对策略感到满意。我们进行了定量实验,以显示我们的算法支持实时互动互动。在网球和雅度上进行两个领域的案例研究,分别显示两个领域。