Affiliations: [a] Department of Physical Education, China Jiliang University, Hangzhou, Zhejiang, China
| [b] Department of Physical Education, Beijing University of Chinese Medicine, Beijing, China
Correspondence:
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Corresponding author. Jian Wang, Department of Physical Education, Beijing University of Chinese Medicine, Beijing 102488, China. E-mail: [email protected].
Abstract: Currently, the athletes’ post-match scores are mostly manual methods, and artificial intelligence is still less used in athletes’ post-match scores. Based on this, this study is based on machine learning algorithms and combined with athletes’ scores for analysis. At the same time, this study uses the reptile technology to conduct real-time mining of athletes’ data and proposes a model-based regression algorithm in the construction of scoring algorithm. Moreover, based on the actual situation, a comprehensive model combining clustering and regression is proposed. In addition, in order to study the validity of the model, this paper designs a performance simulation test, compares the proposed algorithm model with the traditional algorithm model, and collects relevant experimental data and draws the corresponding statistical graph. The experimental results show that the combination of clustering and regression can improve the model’s effect and the results are like the expert scores, which verifies the practicality of the proposed algorithm and provides a theoretical reference for subsequent related research.
Keywords: Cluster analysis, regression analysis, comprehensive model, player rating
