Design and Application of Basketball Tactical Drill System Based on Motion Trajectory Analysis Algorithm

Abstract

A basketball tactical drill system based on motion trajectory analysis algorithms employs advanced computational methods to enhance training effectiveness and strategy development. By utilizing these algorithms to analyze player movements and interactions during drills, coaches can gain valuable insights into team dynamics, spatial awareness, and decision-making processes. This analysis enables coaches to tailor drills to specific tactical objectives, such as improving defensive rotations, offensive spacing, or fast break execution. Additionally, the system can provide real-time feedback to players, helping them understand their positioning and movement patterns to refine their skills and decision-making abilities. This paper presents the design and implementation of a Basketball Tactical Drill System leveraging advanced object detection and motion trajectory analysis algorithms. The system aims to enhance basketball training by providing coaches and players with real-time insights into tactical drills and player performance. Two key algorithms, Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT), are utilized to accurately identify and track basketballs, players, and other relevant objects during training sessions. Experimental results demonstrate the effectiveness of the proposed system, showcasing high detection accuracy, minimal false positives, and competitive processing times across various scenarios and tactical drills. The system leverages Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT) algorithms to achieve exceptional detection accuracy, with an average of 93% across various scenarios and tactical drills. False positives are minimized, with an average of 12 per scenario. Processing times remain competitive, averaging 27 milliseconds per scenario. Integration of motion trajectory analysis provides coaches with invaluable real-time insights into player movements and interactions, facilitating data-driven coaching strategies. The system's versatility is demonstrated through successful application in a range of drills, resulting in performance improvements averaging 20%.