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With the lack of causal knowledge and causal reasoning between variables, existing CBR-based sport training systems have limited effectiveness in elite sports. In this paper, we propose a case-based causal reasoning (CBCR) framework that generates causal knowledge beforehand from the case base, using Markov equivalence classes and structural equations, and then utilizes this knowledge for causal reasoning in each subsequent CBR process, including retrieve, reuse, revise, and retain. An application in elite 100-m sprinting training shows that the CBCR framework provides personalized speed rhythm solutions that are closer to the reasoning results of elite coaches and have better results for elite sprinters’ performance enhancement.

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Case-Based Causal Reasoning for Elite Sport Training

  • 会议论文

  • 发表日期：

    2025年

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  • Dandan Cui,
  • Jianwei Guo,
  • Ping Liu,
  • Xiangning Zhang,
  • Weijie Liu

摘要

With the lack of causal knowledge and causal reasoning between variables, existing CBR-based sport training systems have limited effectiveness in elite sports. In this paper, we propose a case-based causal reasoning (CBCR) framework that generates causal knowledge beforehand from the case base, using Markov equivalence classes and structural equations, and then utilizes this knowledge for causal reasoning in each subsequent CBR process, including retrieve, reuse, revise, and retain. An application in elite 100-m sprinting training shows that the CBCR framework provides personalized speed rhythm solutions that are closer to the reasoning results of elite coaches and have better results for elite sprinters’ performance enhancement.