FORECASTING OLYMPIC MEDAL COUNTS: A MULTIPLE LINEAR REGRESSION MODEL
With the successful conclusion of the 2024 Summer Olympics in Paris, the Olympic medal table rankings have been finalized. The medal table is not only the personal honor of athletes, but also a symbol of the comprehensive strength and national cohesion of countries. Therefore, the research on the prediction of Olympic medal list has received wide attention. However, the current Olympic performance prediction research mainly focuses on macro factors and ignores micro variables, while multiple linear regression can deal with the relationship between multiple independent variables and one dependent variable, which becomes an ideal solution to this complex prediction problem. First and foremost, this paper develops multivariate linear regression models to predict the number of gold medals and the total number of medals for athletes, respectively. These models are used to predict their performance in the 2028 Los Angeles Olympic Games. After obtaining the predicted value of medals won by athletes in 2028, the predicted medal counts of athletes from each country are summed up to initially obtain the medal predictions of each country. In addition, considering that the actual number of national medals will be affected by the host country and the type of program, so this paper establishes a multiple linear regression model to predict the interference of the host country and the type of program on the actual number of medals of each country, and thus, constructs a more accurate medal prediction model. The final prediction result is: the total number of medals and the total number of gold medals is ranked first by the United States as the host country, followed by the United Kingdom, Germany, China and so on, of which France and Australia have the same number of medals and are tied for the fifth place. According to the above rankings, the United Kingdom, France and Germany have improved compared with the previous Olympic Games, while China, Australia and Japan have declined compared with the previous Olympic Games.
Institutions
- East China University of Science and Technology (CN)
Publication Details
- Journal
- World Journal of Information Technology
- Published
- 2025-06-24
- DOI
- https://doi.org/10.61784/wjit3042
- Primary Topic
- Sports Analytics and Performance
- Type
- article
- Field-Weighted Citation Impact
- 0.00