The Design Framework of a Table Tennis Technique and Tactics Intelligent Analysis System Based on Data Mining Technology

This paper describes the design framework of an intelligent analysis system based on data-mining technology for improving table tennis skills and strategies. This work employs advanced association rules, the FP-Growth algorithm and AI methods to explore the case of table tennis competitions. The proposed system integrates the predictions of decision tree and artificial neural network, thus, providing an in-depth analysis of past performance and predicts future outcomes. Decision trees allow for extracting meaningful features from data and neural networks fit highly based on non-linear data. AI also supports high-level tactical optimization and assists coaches and players in making optimal decisions during training and competitions. By analyzing a player's rhythms and styles, the system can improve performance predictions, adapt to different game strategies, deliver tailored training recommendations, and modify on-the-fly. The system performs classification, utilizing basic decision trees and neural network models based on the player's previous specific performance data, thus enabling to further tune the match predictions and devise more sophisticated and personalized trainings.