Application of Data-Driven Prediction and Strategic Optimization in Olympic Medal Distribution

Authors

  • Zhaoyu Zhu School of Physics, Hangzhou Normal University Author
  • Jiajin Shen School of Physics, Hangzhou Normal University Author
  • Minghao Yu School of Physics, Hangzhou Normal University Author
  • Chengtian Liang School of Physics, Hangzhou Normal University Author

DOI:

https://doi.org/10.70731/c3q02875

Keywords:

XGboost, AHP model, logistic regression, multiple information regression equation, K-Means

Abstract

The Olympic medal list is an important indicator to assess the competitive strength of countries, and the prediction and analysis of the distribution of the number of medals provide a scientific basis for countries to formulate sports development strategies. This paper takes the 2024 Paris Olympic Games and the previous Olympic Games as the basic data, combines the historical medal data, the distribution of each Olympic Games and the special characteristics of the host country, constructs a number of mathematical models, explores the law of medal distribution, and proposes a strategy to improve the number of medals.The model in this paper is comprehensive, flexible and practical, which provides a new way of thinking for the analysis of medal distribution in the Olympic Games, and also provides data support for the sports development strategy of each country.

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Published

2025-02-28

How to Cite

Application of Data-Driven Prediction and Strategic Optimization in Olympic Medal Distribution. (2025). International Journal of Advanced Science, 1(1). https://doi.org/10.70731/c3q02875