Research on Olympic Medal Distribution Prediction Model Based on Historical Data

Liangxi Tu

doi:10.54097/n0hvsr19

Authors

Liangxi Tu

DOI:

https://doi.org/10.54097/n0hvsr19

Keywords:

Multiple Linear Regression, Binomial Distribution, Random Forest Model, Difference-in-difference Analysis.

Abstract

With the wide application of data science in the field of sports, although many scholars have devoted themselves to the research of Olympic medal prediction, there still exists the problem of insufficient model accuracy in dealing with the correlation of multiple factors such as economic development, population base, and the effect of host country. Specifically, considering that the number of medals in previous years, as well as the number of events, has a logical relationship with the number of medals in the future, this paper chooses to use the multiple regression analysis model to analyze and predict the number of medals of each country in the future, and at the same time, predicts whether some of the countries are progressing or regressing in terms of winning medals compared with the Olympic Games of 2024, and this paper predicts that the total number of medals of the U.S.A. in 2028 will be 131, and that of China's will be 105.

Downloads

Download data is not yet available.

References

[1] Bernard, A. B., & Busse, M. R. Who wins the Olympic Games: Economic resources and medal totals [J]. Review of Economics and Statistics. 2004, 86(1), 413–417.

[2] Chen, Y., Xu, W., & Huang, Q. Temporal dynamics in Olympic performance: The role of annual training investments. [J]. Journal of Sport. 2021, 86(3), 357-359.

[3] Kim, S., & Park, D. Addressing skewness in Olympic medal data: A robust regression approach. [J]. Statistical Modelling, 2022, 22(5), 621–638.

[4] Wu, X., Liu, H., & Wang, J. Quantifying the host-country advantage in the Olympic Games: A machine learning perspective. [J]. International Journal of Sport Management, 2020, 21(4), 501–517.

[5] García-Pérez, M., Sánchez-Sánchez, J., & Fernández-Luna, Á. The impact of new Olympic events on medal distributions: A case study of skateboarding. [J]. Sport in Society, 2022, 25(8), 1632–1650.

[6] Zhang, Q., Li, Z., & Wang, F. Quantile regression for Olympic medal prediction: Handling heavy-tailed distributions. [J]. Computational Statistics & Data Analysis, 2023, 177, 107583.

[7] Lee, K., Tanaka, H., & Kim, M. Multicollinearity reduction in sports analytics: A case study of Olympic medal types. [J] Journal of Applied Statistics,2022, 48(6), 1120–1135.

[8] Li, T., Zhang, R., & Zhou, Y.A deep learning approach to Olympic medal prediction with industrial investment variables. [J]. Journal of Sports Analytics, 2021, 7(3), 145–160.

[9] Smith, L., & Jones, P. Nonlinear effects of GDP and population on Olympic success: A threshold regression analysis. [J]. Economics of Sports Review, 2023, 18(1), 22–40.

[10] International Olympic Committee. Olympic Agenda 2030: Sustainable Development through Sports. 2021

[11] Olympic Strategy Report. IOC 2028 Medal Projections and Policy Implications. Lausanne: International Olympic Committee. 2021