Browsing by Author "Tate, Mason J."

In the fast-growing and fast-paced sport of collegiate men’s volleyball, a strong defensive system is critical to team success. Central to this defense is the positioning of backcourt players, who attempt to ”dig” the opponent’s attacks and prolong rallies by transitioning possession to their own team. While sports analytics has advanced rapidly in recent decades, men’s collegiate volleyball has seen relatively limited development in data-driven performance analysis, particularly in the area of defensive positioning. This project aims to bridge that gap by using computer vision, specifically object classification models, to detect and map defender positions from match footage. These spatial coordinates are then paired with outcome-based statistics to explore the relationship between positioning and dig success. Using homographic transformation techniques, player locations are projected onto a standardized 2D court model to enable comparison across venues. The resulting dataset is visualized through both frame-level position plots and aggregate heatmaps filtered by team, play outcome, or attack location. The data collection process achieved a usable frame conversion rate of 69.9%, indicating that the proposed methodology is viable for scalable, automated defensive analysis. This work demonstrates the potential of computer vision in volleyball analytics and provides a foundation for future research into tactical trends and optimization.