Prediction of sports strength based on linear regression and random forest model - Zhang, Yipeng; Li , Zhuoyu; He, Haosheng (Transactions on Computer Science and Intelligent Systems Research, 2025)
This paper constructs a dynamic weighted comprehensive model that integrates linear regression and random forest algorithms. This model is used to predict the distribution of competitive performance indicators in international comprehensive sports events and analyze the relevant influencing factors. The model innovatively integrates multiple factors. It incorporates historical data and uses a mechanism to assign higher weights to recent performances, so as to better reflect the current situation. The model also takes into account geographical advantages, which may affect a country’s performance in certain sports. Additionally, it includes the effectiveness of training strategy decision - making entities and the scale of participants. In the quantification process, the model first uses linear regression to predict the number of entities that are likely to achieve the best results for the first time. Then, it quantifies the competitiveness index of each project. Specifically, competitive achievements are converted into scores, and a dynamic weighting method is adopted to adjust the scores according to the time when the achievements are obtained, with more emphasis on recent achievements. Finally, the random forest algorithm is used to comprehensively consider various factors and predict the final results of participating units. This model has high application feasibility. Its unique dynamic weighting mechanism and multi - factor integration make its predictions more accurate. The model also predicts potential new - achiever units and evaluates prediction uncertainty. Future research could incorporate real - time individual data and socio-economic variables to enhance the model.