A Study on Medal Prediction Based on CART Random Forest Algorithm and Moving Time Window Method

This study focuses on the innovative application of computer algorithms in the field of sports, aiming to realize the in-depth analysis and strategy optimization of complex problems related to sports events by constructing an advanced algorithmic system mainly based on the CART Random Forest Algorithm, the Moving Time Window Method, and the Causal Analysis Algorithm. The research comprehensively uses the CART Random Forest algorithm for event-specific prediction to accurately capture the dynamic characteristics of sports events; introduces the causal analysis algorithm to quantitatively analyze the causal relationship between complex factors; and combines the system dynamics method to construct a dynamic model to simulate the operation mechanism of the sports event system, which has successfully overcome the three major core problems. In the specific algorithmic practice, the CART Random Forest algorithm is used to build a multi-scale model, which deeply explores the potential laws within the sports programs, such as the correlation between the athletes' status and the event performance. Based on the model, it not only realizes the prospective prediction of the event results, but also provides powerful data support for the subsequent strategy development. When evaluating the influence of coaching on athletes' performance, 37 key influencing events were identified by using the moving time window method and causal analysis algorithm. Meanwhile, with the help of semi-parametric ROI modeling algorithm, the relationship between coaching inputs and athletes' performance enhancement was precisely quantified, revealing the differences in the effectiveness of different input strategies. Based on the above algorithmic research results, a series of targeted strategy suggestions are proposed, including optimizing training resource allocation by CART Random Forest algorithm, introducing causal analysis algorithm to assist coaches' decision-making, and designing personalized training programs by using Moving Time Window method in combination with other algorithms, so as to enhance the science and effectiveness of the strategy for sports events. The results of this research not only provide a scientific basis for decision-making in sports events, but also expand the application boundary of computer algorithms in complex system analysis. However, there is still room for optimization in the expansion of the generality of the algorithm and the fusion processing with real-time data, which needs to be further explored.