Predicting Olympic Medal Distributions and Emerging Nation Breakthroughs: A Hybrid Negative Binomial and Logistic Regression Framework

Siyuan Wang; Zhaoyang Huang; Shuyi Zhang; Wenxi Wang; Yu Liu; Junyang Yu

doi:10.54097/j6v60681

Authors

Siyuan Wang
Zhaoyang Huang
Shuyi Zhang
Wenxi Wang
Yu Liu
Junyang Yu

DOI:

https://doi.org/10.54097/j6v60681

Keywords:

Negative Binomial Regression, Logistic Regression, Prediction Model.

Abstract

Olympic medal distribution is affected by multiple factors, and accurate prediction of medal distribution in future Olympic Games is of great significance to optimize the sports resource architecture of each country. By constructing an Olympic medal count prediction model based on negative binomial regression and logistic regression, this paper predicts that the United States and China will dominate with 46 gold medals and 142 total medals, and 44 gold medals and 98 total medals, respectively; countries such as France and Germany present a balanced multi-sport profile. Of the five small countries selected for discussion, Mali, Bolivia, and Myanmar show high probabilities. At the same time, Yemen and Angola are significantly lower, with the rise of sports underdogs highlighting the diversity of the sports competition landscape. The study reveals the dynamic changes in global sports' competitive strength and the driving factors behind it, providing a scientific basis for the National Olympic Committees to optimize the allocation of sports resources.

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References

[1] Ren Yilin, Chu Kequn, Zhu Fengshu. Research Progress on the Relationship Between Athletes' Receipt of Social Support and Athletic Performance [J]. Sichuan Sport Science, 2024, 43(5): 50-58.

[2] Ding Huanfeng, Zhu Yuxi, Sun Xiaozhe. Asymmetric Impact of Hosting the Olympic Games on the Economic Growth of Host Countries: New Evidence from a Quasi-Natural Experiment [J]. Journal of Shanghai University of Sport, 2022, 46(12): 82-93, 108.

[3] Shi Huimin, Zhang Dongying, Zhang Yonghui. Can Olympic Medals Be Predicted? ——From the Perspective of Explainable Machine Learning [J]. Journal of Shanghai University of Sport, 2024, 48(4): 26-36.

[4] Yang Biwei. Analysis and Future Prospects of China's Participation in the Three Major World Track and Field Events Since the Reform and Opening Up [D]. Hunan Normal University, 2020.

[5] Olympics.com. Paris 2024 Medals. Available from: https://olympics.com/en/paris-2024/medals.

[6] Nielsen. Virtual Medal Table Forecast 2024. Available from:

[7] https://www.nielsen.com/news-center/2024/virtual-medal-table-forecast/.

[8] Shi Jinyi, Fan Yuanyuan, Jia Peng, et al. Dynamic changes in the competitive sports pattern of countries along the Belt and Road Initiative based on Olympic medals [J]. Hubei Sports Science and Technology, 2020, 39(6): 514-518.

[9] Bernard A. B., Busse M. R. Who Wins the Olympic Games: Economic Resources and Medals Totals [J]. The Review of Economics and Statistics, 2004, 86(1).

[10] Li Xiang. Prediction of Olympic Medal Counts Based on Time Series Analysis [J]. Computer and Digital Engineering, 2018, 46(3): 533-536, 545.

[11] Schlembach Christoph, et al. Forecasting the Olympic medal distribution: A socioeconomic machine learning model [J]. Technological Forecasting and Social Change, 2022, 175: 121314.

[12] Cheng Hongren, Lv Jie, Yuan Tinggang. Predicting China's Athletic Performance at the Tokyo Olympics Based on the 2018 World Top 20 Rankings in Track and Field Events [J]. Bulletin of Sport Science & Technology, 2020, 28(4): 4-8.

[13] Luo Yubo, Cheng Yanfang, Li Mengyao, et al. Predicting China's Medal Count and Overall Strength at the Beijing Winter Olympics Based on the Host Effect and Grey Prediction Model [J]. Contemporary Sport Science and Technology, 2022, 12(21): 183-186.