Author(s)

XinLei Wang^1*, ZiHan Gao², ZiYe Chen³

Affiliation(s)

¹Overseas Chinese College, Capital University of Economics and Business, Beijing 100070, China.

²Accounting School, Capital University of Economics and Business, Beijing 100070, China.

³School of Artificial Intelligence, Capital University of Economics and Business, Beijing 100070, China.

Corresponding Author

XinLei Wang

ABSTRACT

This research focuses on the prediction of the 2028 Los Angeles Olympic medal tables, constructing a predictive model based on multiple linear regression. Through systematic quantitative analysis of individual athlete performance data and national-level sports development factors, a comprehensive medal count prediction system has been constructed. This study thoroughly considers key aspects of athlete performance, including medal-winning records, number of participations, and recent competitive performance. At the same time, crucial variables at the national level, such as home-field advantage and historical performance, are incorporated. By integrating technical means such as time-decay functions, error term settings, and normalization processing, the accuracy and stability of the prediction model have been significantly enhanced, systematically addressing the 2028 Los Angeles Olympic medal count. By combining rigorous statistical validation with complex predictive modeling, the study demonstrates the significant advantages of the constructed model in terms of predictive effectiveness and analytical comprehensiveness, revealing its universal applicability across various sports scenarios. Finally, the research integrates its findings to provide decision-making references for national Olympic committees, facilitating strategic planning for sports events and optimal allocation of resources.

KEYWORDS

Medal prediction; Multiple linear regression; Resource allocation; Home-field advantage

CITE THIS PAPER

XinLei Wang, ZiHan Gao, ZiYe Chen. Medal prediction based on regression models. World Journal of Information Technology. 2025, 3(4): 17-22. DOI: https://doi.org/10.61784/wjit3049.

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