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Table tennis players need to have tactical awareness, that is, to choose the most appropriate tactical strategy to deal with the opponent's situation and the game situation. But in practice, many athletes tend to neglect the training of tactical awareness, resulting in a disconnect between training and competition. In order to solve this problem, this paper introduces a spatial dynamic image planning method based on wearable optoelectronic devices, and applies it to the tactical simulation of table tennis match. By analyzing the characteristics and demands of table tennis competition, an optical sensing system is designed and integrated into wearable devices. The system uses optical sensors to capture the position and movement trajectory of ping-pong balls in real time, and carries out real-time image processing and analysis based on this information. In the process of tactical simulation, the system can predict the opponent's tactical intention according to the position and trajectory of the ping-pong ball, and give corresponding coping strategies. The experimental results show that the system achieves good results in both accuracy and real-time performance. The application of tactical optimization model in table tennis competition will help to improve the competitive level of athletes and promote the development of table tennis. [ABSTRACT FROM AUTHOR]

To become an elite table tennis player, aside from continually practicing, players must know their strengths and weaknesses to plan their strategy beforehand and increase their winning rate. The main problems with previous research were that the data collected were incomplete and imprecise. To address these problems, we established "The Intellectual Tactical System in Competitive Table Tennis", using video analysis to collect competitive data. Additionally, we proposed a machine learning method using a combination of feature-selection and association rules to discover interesting rules from the data. The international matches of the Taiwanese table tennis single player Yun-Ju Lin were used as research samples by applying 3 S (speed, spin, spot) theory to collect and analyze data. The critical factors and scenarios were analyzed to identify the winning tactical models. The results of this study may provide useful suggestions for Yun-Ju Lin on training and building tactics in competitions. The similar approach may be essential for elite players and coaches to have appropriate tactical analysis. [ABSTRACT FROM AUTHOR]

In this study, we tested the theory of angles that was proposed almost a century ago by the tennis player Henri Cochet. This theory proposes that expert tennis players should position themselves on the bisector of the angle of the opponent's possibilities in order to optimize shot return, suggesting a geometric occupation of the court relative to the opponent's affordances; namely what he/she is capable of doing. We tested this hypothesis by analysing player and ball positioning data from professional tennis matches recorded with a Hawk-Eye system. We compared this hypothesis with two alternative computational and probabilistic hypotheses which would consist in positioning oneself on the average or the median of the shots usually played from a given location. The results show that expert tennis players apply the principles of the theory of angles and thus confirm Henri Cochet's intuition. That is, for lateral court positioning, a geometric strategy is deemed optimal by expert players. It also appears that the more experienced the players are, the more precise their application of this strategy becomes. [ABSTRACT FROM AUTHOR]

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]