Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity
Tennis players have more physical training content, and the training items are complex. For athletes, training programs that adapt to their individual characteristics should be formulated according to their physical characteristics. The current development of big data has brought about changes in th...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/4759395 |
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| author | Haiye Qiu Chang Liu Xiaomin Zhang |
| author_facet | Haiye Qiu Chang Liu Xiaomin Zhang |
| author_sort | Haiye Qiu |
| collection | DOAJ |
| description | Tennis players have more physical training content, and the training items are complex. For athletes, training programs that adapt to their individual characteristics should be formulated according to their physical characteristics. The current development of big data has brought about changes in thinking, management, and business models. The combination of complex systems and big data can also make breakthroughs in the sports field. Based on this, this article proposes a tennis player training schedule intelligent formulation system based on complex system big data. First of all, this article adopts the literature data method, comparative analysis method, experimental analysis method, etc., in-depth study of the concepts of big data, complex system, and the physical structure characteristics of tennis sports. This paper designs an intelligent system for making tennis players’ training schedule, which collects, transforms, and integrates tennis training data through the characteristics of big data. Then, the dynamic time regulation of tennis is performed through a complex system, and finally, the experimental system is analyzed. This article mainly analyzes the comparison of physical indicators between the experimental group and the control group before and after the experiment, the evaluation indicators of sports events, the strength training effects of tennis events, and the analysis of shoulder joint tests. There is no significant difference between the experimental group and the control group in the items before the experiment, P>0.05, which suggests that the physical fitness of the two groups of athletes is similar; in the posttest data, the experimental group and the control group have significant differences, P<0.05, indicating an effect from the experiment. In particular, in fan running, forward and backward strokes, and the serve, the scores of the experimental group were higher than those of the control group, indicating that the use of the formulated training system demonstrated significant results. |
| format | Article |
| id | doaj-art-56c9c31de7c5481e85531b5fe41daa0c |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-56c9c31de7c5481e85531b5fe41daa0c2025-08-20T02:05:56ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/47593954759395Intelligent Design of Tennis Player Training Schedule Based on Big Data of ComplexityHaiye Qiu0Chang Liu1Xiaomin Zhang2Physical Education Department, Shandong University (Weihai), Weihai 264200, Shandong, ChinaCollege of Competitive Sports, Beijing Sport University, Haidian, Beijing 100084, ChinaCollege of Engineering, Zhejiang Normal University, Jinhua 321000, Zhejiang, ChinaTennis players have more physical training content, and the training items are complex. For athletes, training programs that adapt to their individual characteristics should be formulated according to their physical characteristics. The current development of big data has brought about changes in thinking, management, and business models. The combination of complex systems and big data can also make breakthroughs in the sports field. Based on this, this article proposes a tennis player training schedule intelligent formulation system based on complex system big data. First of all, this article adopts the literature data method, comparative analysis method, experimental analysis method, etc., in-depth study of the concepts of big data, complex system, and the physical structure characteristics of tennis sports. This paper designs an intelligent system for making tennis players’ training schedule, which collects, transforms, and integrates tennis training data through the characteristics of big data. Then, the dynamic time regulation of tennis is performed through a complex system, and finally, the experimental system is analyzed. This article mainly analyzes the comparison of physical indicators between the experimental group and the control group before and after the experiment, the evaluation indicators of sports events, the strength training effects of tennis events, and the analysis of shoulder joint tests. There is no significant difference between the experimental group and the control group in the items before the experiment, P>0.05, which suggests that the physical fitness of the two groups of athletes is similar; in the posttest data, the experimental group and the control group have significant differences, P<0.05, indicating an effect from the experiment. In particular, in fan running, forward and backward strokes, and the serve, the scores of the experimental group were higher than those of the control group, indicating that the use of the formulated training system demonstrated significant results.http://dx.doi.org/10.1155/2021/4759395 |
| spellingShingle | Haiye Qiu Chang Liu Xiaomin Zhang Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity Complexity |
| title | Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity |
| title_full | Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity |
| title_fullStr | Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity |
| title_full_unstemmed | Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity |
| title_short | Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity |
| title_sort | intelligent design of tennis player training schedule based on big data of complexity |
| url | http://dx.doi.org/10.1155/2021/4759395 |
| work_keys_str_mv | AT haiyeqiu intelligentdesignoftennisplayertrainingschedulebasedonbigdataofcomplexity AT changliu intelligentdesignoftennisplayertrainingschedulebasedonbigdataofcomplexity AT xiaominzhang intelligentdesignoftennisplayertrainingschedulebasedonbigdataofcomplexity |