Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms - Liu, Meng; Chen, Yan; Guo, Zhenxiang; Zhou, Kaixiang; Zhou, Limingfei; Liu, Haoyang; Bao, Dapeng; Zhou, Junhong (Frontiers in Psychology, 2022)
Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance. Objective: The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies. Methods: The data, consisting of sixteen seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the game's rules, distinct features are filtered using lasso regression, and a 5000m race model and a medal model are built using a 5-fold cross-validation method. Results: The results showed that the support vector machine model was the most stable among the 5000m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3000m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5000m, the second lap of the 500m, and the fourth lap of the 1500m are the main characteristic factors that affect the athlete's ability to win medals. Conclusion: Support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3000m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500m event are more likely competitive for medals.