Analisis Kerapatan Pertahanan Sepak Bola Berbasis Computer Vision dan Transformasi Homografi

Defensive compactness and inter-player distance are crucial tactical aspects in football. However, their evaluation has traditionally relied on subjective visual observation, which is highly susceptible to bias caused by perspective distortion from television broadcast cameras. This research aims to develop an automated data-driven spatiotemporal analytics system to quantitatively extract tactical metrics from single broadcast camera videos. This system provides an objective solution for coaching staff to precisely evaluate the effectiveness of defensive strategies, replacing manual observation methods. The proposed method integrates Computer Vision and Deep Learning, utilizing the YOLOv11m model for player detection, the BoTSORT tracking algorithm, and a color space filter for team differentiation. The 2D pixel coordinates are then converted into real-world pitch distance metrics using Homographic Transformation supported by Global Motion Estimation (GME) to overcome camera movement. Testing on static (Indonesia National Team) and dynamic (Bayern Munich) defensive scenarios showed that the detection model achieved an mAP50 precision of 0.986, with a tracking accuracy (MOTA) of 89.78%. Field distance projection validation recorded a very low error rate (MAPE) of 7.93% in the static calibration area. This computational system is proven reliable as a Sports Analytics instrument to reduce tactical interpretation bias, map Convex Hull and dyadic distances, and objectively support coaches strategic decision-making.