Author(s)

Lei Zhao

Affiliation(s)

School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China.

Corresponding Author

Lei Zhao

ABSTRACT

As the world’s most influential sporting event bringing together the world's best athletes, the Olympic Games is the highest stage for competitive sports. It inspires more people to participate. In this paper, in order to predict the total medals and gold medals won by each country in the 2028 Olympic Games, a multiple linear regression model is constructed by considering the historical medal datas, the number of athletes' participation and the types and number of participating events and other characteristic variables as the indexes, and takes the evaluation coefficient R2and the mean squared error MSE as the model evaluation indexes. Through the established medal list and historical trend, the prediction interval of total medals and the prediction interval of gold medals are analyzed, and those countries that may progress or regress in the 2028 Olympic Games are analyzed and obtained, and by the prediction this paper selects the top 10 progressing countries and the 10 countries with obvious regression. In addition, the prediction of those countries that have never won a prize is also made to explore the possibility of winning a medal, and for this purpose, this paper adopts a binary classification model and logistical regression model, and the probabilities of winning a first medal are obtained by selecting the data of the countries that have never won the award.

KEYWORDS

Predicting the number of medals; Linear regression model; Model evaluation; Binary classification model and logistical regression model

CITE THIS PAPER

Lei Zhao. The prediction of Olympic medal table based on linear regression modeling. World Journal of Information Technology. 2025, 3(3): 26-32. DOI: https://doi.org/10.61784/wjit3039.

REFERENCES

[1] Abel Sowal. Olympic medal prediction is not just for "presence". Economic and Social Management Science, 2016, (20): 77.

[2] Nagpal Prince, Gupta Kartikey, Verma Yashaswa, et al. Paris Olympic(2024) Medal Tally Prediction. Banaras Hindu University,Varanasi, India, Lecture Notes in Networks and Systems, 2023, (662): 249-267.

[3] Cha Zheng. Economic Reflections on Olympic Medals. Shandong Radio and Television University, Social Science II Series, 2020, (05): 97.

[4] Mallappa Uday, Gangwar Pranav, Khaleghi Behnam, et al. TermiNETor:Early Convolution Termination for Efficient Deep Netural Networks. 2022 IEEE 40th International Conference on Computer Design (ICCD), Olympic Valley, CA, USA, 2022: 635-643. DOI: 10.1109/ICCD56317.2022.00098.

[5] Deng Rongrong, Fan Qingmin, Zhang Yinkai, et al. Analysis of Technical Evaluation of Male Boxing Athletes Based on Entropy and Multiple Linear Regression Model-Taking Excellent 81kg Athletes at the Tokyo Olympics as an Example. School of Competitive Sports,Beijing Sport University, Social Science II Series, 2023, (11): 4744-4746.

[6] Miyoshi Takemasa, Amemiya Arata, Otsuka Shigenori, et al. Big Data Assimilation:Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku during Tokyo Olympics and Paralympics. Meteorological Research Institute,Tsukuba, Japan, Association for Computing Machinery, Inc, 2023, (12): 8.

[7] Wang Yongjun, Chen Hong, Yang Yongfen. Fuzzy relationship,mediating variables,and dynamic models:A study on the trickle down effect of sports participation in heritage, School of Physical Education. Chongqing Technology and Business University,School of Management,Tianjin Sport University, Kunming University, Social Science II Series, 2021, (7): 28-30.

[8] Gupta Krishon Gopal, Arora Aditi. Olympic Data Analysis: Uncovering New Insights into Athletic Performance and Competition. ABES Engineering College, Ghaziabad, India, Grenze Scientific Society, 2024, (2): 4312-4319.

[9] Li Luyan, Gao Yong. Understanding of Beijing Winter Olympics Volunteers from a Sociological Perspective. School of Physical Education,Henan University of Science and technology, Social Science II Series, 2022, (10): 216-217.

[10] Ning Yixia. Observing the characteristics of China’s Competitive Sports Strength from the Tokyo Olympics. Shenzhen University, Social Science II Series, 2022, (09): 36-38.