关键词: 世界女排, 高水平, 精英运动员, 多源数据, 进攻表现, 核心指标, 解析

Abstract: Based on the big data collected from the attack performance indicators of the world’s top women’s volleyball teams in the Tokyo Olympic cycle, using the methods of high-speed video analysis, data volley 4 data detection and literature research, the attack performance of the world’s high-level women’s volleyball teams and elite players is tracked. Conclusion: (1) the attack performance of the world’s top women’s volleyball teams is outstanding, showcased by both the strength type represented by China and Serbia, and the speed type represented by Brazil and the United States. (2) The selection of attack area by the world’s top women’s volleyball teams has the same tendency, that is, making full use of the width of the net and the depth of the field to make a breakthrough; top attack players and receiving players have outstanding attack performance in different locations, and elite middle hitter often forms a collaborative cover with outsider hitter and the corresponding back row attack, making the collective attack show the combination of height and speed and three-dimensional characteristics. (3) The core attack index of the world’s top women’s volleyball players has advantages and the attack route is divided delicately. (4) Compared with Chinese women’s volleyball team, European and American women’s volleyball teams have the advantages of attack height, attack speed and deep cover of the front and back row, and there is a gap in the attack index between wing spiker and some outside players in Chinese women’s volleyball team. (5) The application of big data can provide reference for Chinese women’s volleyball team’s targeted training guidance, pre-match preparation, in-match technical and tactical adjustment and post-match technical and tactical performance feedback, and provide feedback and evaluation for elite players to understand their own match performance scientifically.

Key words: World Women’s Volleyball Team, high level, elite athletes, multi-source data, attack performance, core indicators, analysis