Author(s)

RunMo Liu^1*, YiWen Gu², Jing Zhang²

Affiliation(s)

¹College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China.

²School of Mathematical Sciences, Capital Normal University, Beijing 100048, China.

Corresponding Author

RunMo Liu

ABSTRACT

The Olympic Games serve as a global platform to showcase athletic performance and national competitiveness. Accurate forecasting of medal outcomes not only provides scientific support for sports policy and resource allocation but also contributes to understanding the dynamics of international competition. This study employs logistic regression to address two key research problems: (1) predicting the distribution of total and gold medals across countries in the 2028 Los Angeles Olympic Games, and (2) estimating the probability of countries historically without medals achieving their first Olympic success. The models integrate variables such as historical performance, number of participants, number of events, and geographical proximity to the host nation. Results indicate that the proposed framework achieves high predictive accuracy, with strong model fit and low error values, while also identifying emerging countries with significant potential for breakthroughs. The findings not only enhance medal prediction methodology but also provide broader insights into the evolving landscape of global sports competitiveness.

KEYWORDS

Olympic games; Logistic regression; Medal prediction; Sports analytics; Forecasting models; International competitiveness

CITE THIS PAPER

RunMo Liu, YiWen Gu, Jing Zhang. Prediction of Olympic medal distribution based on logistic regression models. World Journal of Information Technology. 2025, 3(5): 41-47. DOI: https://doi.org/10.61784/wjit3066.

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