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October 11, 2025Deleted Journal

Research on Olympic Medal Prediction Based on Random Forest Regression

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Authors

YZYe ZhuYZYuhang ZhengYMY. J. Mao

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Overview

Predictive modeling reveals a strong relationship between host country status and medal counts, suggesting strategies for sports governance.

Key Points

  • The random forest regression model achieved a coefficient of determination (R²) of 0.96, indicating high predictive accuracy.
  • Mean squared errors were 0.94 for gold medals and 3.09 for total medals, demonstrating the model's superior performance against alternative methods.
  • Data preprocessing identified key features such as athlete participation and host country status, contributing to model stability validated by 5-fold cross-validation.
  • Analysis indicates a 5%-10% advantage for host countries, highlighting the potential for improved outcomes through targeted resources and coaching.

Cite This Study

Zhu et al. (2025) studied this question.

synapsesocial.com/papers/68e9b1d0ba7d64b6fc132a6ahttps://doi.org/10.54097/9b1hfh31
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Implementation of Random Forest Algorithm Based Medal Count Prediction2025
  2. 2Prediction on Olympic Medal Based on Random Forest and Logistic Regression2025
  3. 3Research on Medal Prediction Model for 2028 Olympic Games Based on Linear Regression and Random Forests2025
  4. 4Uncovering the Secret of Olympics Medals2025 · 1 citations
  5. 5Prediction Of Medals Based on Machine Learning and OLS Statistical Regression Models2025