Random Forest Regression-Based Olympic Medal Prediction Model

DOI

10.2991/978-94-6463-845-5_74How to use a DOI?

Keywords

Olympic medal; prediction model; random forest regression model; sports development

Abstract

As one of the most influential sporting events in the world, the Olympic medal table has always been an important benchmark for national sports development. To enhance the accuracy and interpretability of Olympic medal predictions, this study constructs a comprehensive Olympic medal prediction framework based on a random forest regression model. The model extracts rich features from multiple dimensions, including historical performance, host country advantage, event scale, and athlete strength, providing a comprehensive reflection of each country's Olympic participation strength. Analysis of the prediction results for the 2028 Los Angeles Summer Olympics indicates that the United States will continue to lead the medal table, with China and Japan also emerging as major medal-winning nations. Traditional powerhouses such as Great Britain, Australia, and France, as well as emerging strong competitors like Germany and the Netherlands, are also expected to achieve remarkable results in this Olympics. This prediction model provides scientific decision-making support for national sports management departments, helping optimize national sports strategies and resource allocation.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xu Wang
AU  - Tianyu Wang
AU  - Zhenyu Li
PY  - 2025
DA  - 2025/09/16
TI  - Random Forest Regression-Based Olympic Medal Prediction Model
BT  - Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025)
PB  - Atlantis Press
SP  - 730
EP  - 736
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-845-5_74
DO  - 10.2991/978-94-6463-845-5_74
ID  - Wang2025
ER  -