Multi-dimensional dynamic visualization of spatiotemporal data for tactical analysis in table tennis

Area of Science:

• Sports Analytics
• Data Visualization
• Human-Computer Interaction

Background:

• Table tennis generates complex, multidimensional data (stroke sequences, landing points, shot techniques).
• Traditional visualizations struggle to represent dynamic landing patterns and categorical relationships effectively.
• A need exists for advanced tools for post-match technical and tactical analysis in table tennis.

Purpose of the Study:

• To develop a visual analytics framework for exploratory post-match technical and tactical diagnosis in table tennis.
• To integrate methods for analyzing multidimensional categorical data and spatiotemporal landing patterns.

Main Methods:

• Developed a multimodal framework combining cluster-enhanced parallel coordinates and a spatiotemporal cube module.
• Utilized K-Modes clustering for tactical profiles and sub-trajectory clustering for landing-path patterns.
• Evaluated the framework using two independent match cases and expert assessments.

Main Results:

• The parallel coordinates module enhanced readability and identified player-specific tactical profiles.
• The spatiotemporal cube module revealed recurring landing paths and stroke-sequence-based landing distributions.
• Expert evaluation confirmed the framework's analytical usefulness and interpretability.

Conclusions:

• The framework effectively combines categorical and spatiotemporal analysis for table tennis.
• It provides a domain-adapted approach for examining technical-tactical relationships and dynamic landing patterns.
• Offers methodological insights for analyzing spatiotemporal data in racket sports.