Enhancing Sports Strategy with Video Analytics and Data Mining: Automated Video-Based Analytics Framework for Tennis Doubles
Jia Wei Chen

TL;DR
This paper introduces an automated video analytics framework for tennis doubles, combining advanced machine learning techniques to improve tactical analysis, player localization, and match understanding, reducing manual effort and enhancing data quality.
Contribution
It presents a novel standardized annotation methodology and integrates GroundingDINO and YOLO-Pose with CNN models for superior doubles tennis analysis.
Findings
CNN models outperform pose-based methods in shot and position prediction
The framework reduces manual annotation effort significantly
Enhanced data consistency improves analysis accuracy
Abstract
We present a comprehensive video-based analytics framework for tennis doubles that addresses the lack of automated analysis tools for this strategically complex sport. Our approach introduces a standardised annotation methodology encompassing player positioning, shot types, court formations, and match outcomes, coupled with a specialised annotation tool designed to meet the unique requirements of tennis video labelling. The framework integrates advanced machine learning techniques including GroundingDINO for precise player localisation through natural language grounding and YOLO-Pose for robust pose estimation. This combination significantly reduces manual annotation effort whilst improving data consistency and quality. We evaluate our approach on doubles tennis match data and demonstrate that CNN-based models with transfer learning substantially outperform pose-based methods for…
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