Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–26, 2023, Zhengzhou, China

Research Article

The Application of Data Mining Technology in the Analysis of Academic Sports Performance in Universities

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  • @INPROCEEDINGS{10.4108/eai.24-11-2023.2343719,
        author={Yunlong  Ma and Qun  Cui and Kang  Shao},
        title={The Application of Data Mining Technology in the Analysis of Academic Sports Performance in Universities},
        proceedings={Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24--26, 2023, Zhengzhou, China},
        publisher={EAI},
        proceedings_a={ITEI},
        year={2024},
        month={4},
        keywords={data mining; sports performance analysis; decision support; university},
        doi={10.4108/eai.24-11-2023.2343719}
    }
    
  • Yunlong Ma
    Qun Cui
    Kang Shao
    Year: 2024
    The Application of Data Mining Technology in the Analysis of Academic Sports Performance in Universities
    ITEI
    EAI
    DOI: 10.4108/eai.24-11-2023.2343719
Yunlong Ma1,*, Qun Cui1, Kang Shao1
  • 1: Weifang Engineering Vocational College Qingzhou
*Contact email: 1278407287@qq.com

Abstract

In the modern sports arena, data mining technology is becoming a key factor in improving training effectiveness and competitive performance. This article explores the application of data mining in sports performance analysis, including sports performance prediction, athlete selection, and training effectiveness evaluation. By analyzing data from a university's student-athletes, we have constructed models based on regression analysis, clustering algorithms, and association rules. These models not only effectively mine patterns in historical data and predict future trends but also reveal key factors influencing athlete performance, providing data support for personalized training plans and game strategies. This demonstrates that data mining technology is a powerful tool for handling large amounts of sports data and optimizing decision-making processes. However, to maximize its effectiveness in practical applications, continuous improvements in model interpretability, reliability, and robustness are still required. Continual refinement of these models will ensure their optimal performance in sports training and competition, driving technological advancements and performance enhancement in the field of sports.