As the level of tennis improves, the ability and strategy to play in the game determine the responsibility for the outcome of the game. It is committed to improving the professional and strategic level of Asian tennis players and narrowing the gap with high-level European and American tennis players. The purpose of this paper is to study the application of machine learning in the study of the evaluation model of the technical and tactical effectiveness of tennis matches, and proposes the decision tree algorithm, artificial neural network, reinforcement learning algorithm, and related concepts of tennis matches. Therefore, this paper selects Federer’s technical and tactical games from 2013 to 2017 as the research object. And by paying attention to the application characteristics of Federer’s methods and strategies in each stage, a detailed statistical analysis of the data is carried out point by point. The exploratory outcomes show that through the AI calculation, it is found that the incredible skill and vital sufficiency of Federer’s hard court game change around 0.600, and the typical worth is 0.594. Particular and vital efficiency showed a sluggish recuperation in 2017.

中文翻译：

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基于机器学习的网球比赛技战术效果评估模型

随着网球水平的提高，在比赛中发挥的能力和策略决定了对比赛结果的责任。致力于提高亚洲网球运动员的职业和战略水平，缩小与欧美高水平网球运动员的差距。本文旨在研究机器学习在网球比赛技战术有效性评价模型研究中的应用，提出决策树算法、人工神经网络、强化学习算法以及网球相关概念。火柴。因此，本文选取费德勒2013-2017年的技战术比赛作为研究对象。并且通过关注费德勒的方法和策略在各个阶段的应用特点，逐点对数据进行详细的统计分析。探索结果表明，通过AI计算发现，费德勒硬地比赛的不可思议的技巧和生命力在0.600左右变化，典型值为0.594。2017 年特别重要的效率显示出缓慢的恢复。