Exploring the Effect of Core Tactics and Demographics on Squash Gameplay Patterns Using Computer Vision
This paper presents a computer vision system for analyzing common tactical and training pat-
terns in squash using player locations and movement dynamics. Leveraging convolutional neural
networks (CNNs) such as YOLO and TrackNet, we extract player coordinates on a squash court
through a lightweight, single-camera framework. Match footage and detections are segmented by
gender, skill level, and match phase to enable contextual comparisons. From 2D coordinates, we
generate heatmaps of player locations, court coverage percentages, and distance-over-time graphs
to visualize movement tendencies. Our results show that women demonstrate greater ball control
and accuracy than men across all levels, while professional players exhibit more aggressive court
usage than amateurs. We also identify that games 2 and 3 are the most physically demanding,
highlighting a balance between slow starts and fatigue.
terns in squash using player locations and movement dynamics. Leveraging convolutional neural
networks (CNNs) such as YOLO and TrackNet, we extract player coordinates on a squash court
through a lightweight, single-camera framework. Match footage and detections are segmented by
gender, skill level, and match phase to enable contextual comparisons. From 2D coordinates, we
generate heatmaps of player locations, court coverage percentages, and distance-over-time graphs
to visualize movement tendencies. Our results show that women demonstrate greater ball control
and accuracy than men across all levels, while professional players exhibit more aggressive court
usage than amateurs. We also identify that games 2 and 3 are the most physically demanding,
highlighting a balance between slow starts and fatigue.
