Abstract
Tennis is a very explosive, continuous, and intense sport, including many continuous short‐term explosive actions. It has the characteristics of short‐term, high‐intensity, high‐density training, and it belongs to the category of purely competitive skills. In the competition, athletes must maintain good physical condition, physical fitness, and long‐term endurance in order to demonstrate outstanding technical and tactical skills. Therefore, this paper proposes a mobile processor performance data mining framework MobilePerfMiner, which uses hardware counters and iteratively uses the XGBoost algorithm to build a performance model, ranks the importance of the microarchitecture events of the big data task, and reduces the performance big data dimension, so as to optimize the big data algorithm according to the performance characteristics described. Undoubtedly, the comprehensive monitoring of the sports training process is complex system engineering. The main monitoring includes three aspects: physical condition, technical and tactical skills, and intelligence. Sports technology is reflected in the ultimate load. According to the convenience and actual needs of the research, this article will discuss the methods of evaluating tennis training load and the actual technical and tactical parameter characteristics that can be obtained by studying the characteristics of tennis, namely, kinematics. Parameters for noncontact testing, the next step is to discuss the appropriateness and necessity of the load, as well as the technical and routine monitoring of tennis training ability. The final experimental results show that it can improve the physical energy of tennis players by more than 17%.
Highlights
The rapid development of big data is profoundly changing people’s production and life and changing the world
The factor analysis method is used to test the planned health evaluation model of volleyball player data mining, and the results show that the evaluation model is reliable
This article firstly deals with the collected club technical statistics data and builds a mining model with the club’s win or loss as the target attribute and the score as a decision attribute and analyzes the relationship between scores and wins
Summary
The rapid development of big data is profoundly changing people’s production and life and changing the world. New tennis players are the hope and new strength of the development of tennis in our country. Tennis training for young people is the top priority, and the country has begun to pay attention to it This has helped to carry out in-depth youth tennis training, and won many competitions and opportunities, and provided young tennis players with more opportunities. F ðxiÞ, ð1Þ k=1 q represents each tree structure and maps a sample to the corresponding page index. Each regression tree contains a continuous score on each leaf node, and w represents i-th leaf score
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