Prediction of Olympic medals based on GA-BP and logistic regression model - Sanglin Zhao (20599835) (2025)
Predicting the number and distribution of medals in the future Olympic Games has become a noticeable topic. However, it is not easy to predict the number of Olympic medals. We need to comprehensively consider historical data, athletes' performance, host country effect and other factors from multiple dimensions. This paper adopts GA-BP algorithm model, which is an optimization algorithm combining genetic algorithm (GA) and back propagation neural network (BPNN). It uses the global search ability of genetic algorithm to optimize the weight and bias parameters of BP neural network, so as to improve the training efficiency and prediction performance of neural network. By estimating the number of gold medals and total medals of each country in the Olympic Games, and verifying the accuracy of the model, the medal list of the 2028 Los Angeles Olympic Games is finally predicted. Based on the synthetic control model, this paper selects two countries, Estonia and China, as the research object. By constructing a virtual control group and two experimental groups, the change of the number of medals in different periods and the change of the number of medals with or without coaches in the same period were analyzed, and the difference between the actual medal growth and the synthetic control group (no coach effect team) was compared. The results show that the number of medals won by Estonia and China with a head coach is higher than that without a head coach. Under the guidance of excellent coaches, Estonia won 1 gold medal and 2 bronze medals in 1992, indicating that the great head coach effect has a significant role in promoting the performance of athletes. This paper explores the valuable opinions of the Olympic committee decision-making, reveals the key factors behind the distribution of medals, optimizes the allocation of national strategic resources, and predicts the performance of countries in the future Olympic Games. This research will help to improve the accuracy of the model more comprehensively, better understand the medal trend of the Olympic Games, and provide a scientific basis for the NOC to achieve better results in the future Olympic Games.