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 predicting shot types, player positioning, and formations. The CNN models effectively capture complex visual and contextual features essential for doubles tennis analysis. Our integrated system bridges advanced analytical capabilities with the strategic complexities of tennis doubles, providing a foundation for automated tactical analysis, performance evaluation, and strategic modelling in professional tennis.