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A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches.

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Summary
This summary is machine-generated.

This study introduces a new system for analyzing padel sports data from videos. It uses a specialized query language and enhanced visualizations to help coaches and analysts better understand player movements and strategies.

Keywords:
data analysisdata visualizationplayer trackingracket sportssport analyticssports sciencevideo-based analysis

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Area of Science:

  • Sports Science
  • Computer Vision
  • Data Analysis

Background:

  • Advances in video analysis enable automatic annotation of sports videos.
  • Deep learning methods allow robust player and ball tracking in racket sports.
  • Traditional data analysis methods struggle with complex spatio-temporal sports data and visualization.

Purpose of the Study:

  • To address limitations in analyzing video-based sports tracking data.
  • To develop a system for efficient analysis of padel match data.
  • To facilitate coaches and analysts in understanding complex game strategies.

Main Methods:

  • Utilized deep learning for player detection, tracking, and pose estimation from single videos.
  • Developed a domain-specific query language for compact representation of spatio-temporal queries.
  • Enriched data visualization by linking data points to specific video segments for detailed analysis.

Main Results:

  • Created a system capable of analyzing padel match video data.
  • Demonstrated the system's flexibility by converting literature-based padel strategies into readable queries.
  • Provided a more comprehensive view of game data, including player motion and court positioning.

Conclusions:

  • The proposed system overcomes limitations of traditional table-based methods for sports data analysis.
  • The domain-specific query language enhances usability for coaches and analysts.
  • Linking data to video segments provides richer insights into game dynamics and player performance.