Construction of Women's All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Meng Liu; Yan Chen; Zhenxiang Guo; Kaixiang Zhou; Limingfei Zhou; Haoyang Liu; Dapeng Bao; Junhong Zhou

doi:10.3389/fpsyg.2022.915108

Construction of Women's All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Front Psychol. 2022 Jul 12:13:915108. doi: 10.3389/fpsyg.2022.915108. eCollection 2022.

Authors

Meng Liu¹, Yan Chen¹, Zhenxiang Guo², Kaixiang Zhou^{1

3}, Limingfei Zhou⁴, Haoyang Liu⁵, Dapeng Bao⁶, Junhong Zhou⁷

Affiliations

¹ Sports Coaching College, Beijing Sport University, Beijing, China.
² Department of Physical Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
³ College of Sports, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
⁴ School of Strength and Conditioning Training, Beijing Sport University, Beijing, China.
⁵ AI Sports Engineering Lab, School of Sports Engineering, Beijing Sport University, Beijing, China.
⁶ China Institute of Sport and Health Science, Beijing Sport University, Beijing, China.
⁷ Harvard Medical School, Hebrew SeniorLife Hinda and Arthur Marcus Institute for Aging Research, Boston, MA, United States.

Abstract

Introduction: 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 16 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 competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method.

Results: The results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete's ability to win medals.

Conclusion: Compared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, 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 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

Keywords: elite athletes; machine learning; model construction; performance prediction; speed skating.