A Study on the Dynamic Relationship between Performance Scoring and Outcomes in Sports Competitions
DOI:
https://doi.org/10.54097/rp2p5954Keywords:
MICE algorithm, PCA, Entropy Weight Method, Tennis game strategy.Abstract
In this paper, the relationship between kinetic energy and match results in tennis matches was explored by using principal component analysis (PCA) and the entropy weighting method. By analyzing the match data of Wimbledon 2023, the paper describes the data processing methods in detail, including the processing of missing data and the application of the MICE algorithm. It was found that there is a correlation between changes in kinetic energy during matches and match success, thus providing practical guidance for tennis coaches and players in adjusting their strategies. In addition, the role of kinetic energy in determining sports outcomes was analyzed, demonstrating the validity of the methodology employed. This study provides a new research idea and framework for the field of sports science.
Downloads
References
Wan G, Maung C, Schweitzer H. Improving the accuracy of principal component analysis by the maximum entropy method [C]//2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2019: 808 - 815.
Zhu Y, Tian D, Yan F. Effectiveness of entropy weight method in decision-making [J]. Mathematical Problems in Engineering, 2020, 2020: 1 - 5.
Xu X, Wen C. Fault diagnosis method based on information entropy and relative principal component analysis [J]. Journal of Control Science and Engineering, 2017, 2017.
Ye C, Wilson R C, Hancock E R. Network analysis using entropy component analysis [J]. Journal of Complex Networks, 2018, 6 (3): 404 - 429.
Liang-chen W, Xue-feng Z, Hui W. Method of synthetic evaluation based on the principal component analysis and entropy weight[C]//2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010, 8: V8 - 312 - V8 - 315.
He R, Hu B, Yuan X T, et al. Principal component analysis based on non-parametric maximum entropy[J]. Neurocomputing, 2010, 73(10-12): 1840 - 1852.
Jenssen R. Kernel entropy component analysis [J]. IEEE transactions on pattern analysis and machine intelligence, 2009, 32 (5): 847 - 860.
Vásquez-Correa M C, Rodas H L. A robust approach for principal component analyisis [J]. arXiv preprint arXiv: 1903.00093, 2019.
Deng X, Tian X. Entropy principal component analysis and its application to nonlinear chemical process fault diagnosis [J]. Asia‐Pacific Journal of Chemical Engineering, 2014, 9 (5): 696 - 706.
Long Y, Shi X, Xiao Z, et al. An Outlier Fault Diagnosis Method Based on Principal Component of Entropy Weight[C]//2021 2nd International Conference on Computer Engineering and Intelligent Control (ICCEIC). IEEE, 2021: 158 - 161.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
