At present, most of the prediction studies related to antagonistic event-group competitions focus on the prediction of competition results, and less on the prediction of the competition process, which can not provide real-time feedback of the athletes' state information in the actual competition, and thus can not analyze the changes of the competition situation. In order to solve this problem, this paper proposes a prediction model based on Random Forest for the turning point of the antagonistic event-group. Firstly, the quantitative equation of competitive potential energy is proposed; Secondly, the quantitative value of competitive potential energy is obtained by using the dynamic combination of weights method, and the turning point of the competition situation of the antagonistic event-group is marked according to the quantitative time series graph; Finally, the random forest prediction model based on the optimisation of the KM-SMOTE algorithm and the grid search method is established. The experimental analysis shows that: the quantitative equation of competitive potential energy can effectively reflect the dynamic situation of the competition; The model can effectively predict the turning point of the competition situation of the antagonistic event-group, and the recall rate of the model in the test set is 86.13%; the model has certain significance for the future study of the competition situation of the antagonistic event-group.

翻译：目前，针对对抗性事件组竞赛的预测研究大多集中于比赛结果的预测，较少关注比赛过程的预测，无法在实际比赛中实时反馈运动员的状态信息，因而难以分析竞赛态势的变化。为解决这一问题，本文提出一种基于随机森林的对抗性事件组态势转折点预测模型。首先，提出竞技势能的量化方程；其次，通过权重动态组合方法获得竞技势能量化值，并依据量化时序图标注对抗性事件组竞赛态势转折点；最后，建立基于KM-SMOTE算法与网格搜索法优化的随机森林预测模型。实验分析表明：竞技势能量化方程能有效反映竞赛动态态势；该模型能有效预测对抗性事件组竞赛态势转折点，在测试集中的召回率达到86.13%；该模型对未来对抗性事件组竞赛态势研究具有一定意义。