A Two-Stage Spatio-Geometrical clustering of football team shape for post-match review

The increasing availability of spatio-temporal data in football has enabled advanced Post-Match Review techniques that analyze team performance from both individual and collective perspectives. While traditional approaches focus on individual player metrics, this study introduces a novel spatio-geometrical method for analyzing team shape dynamics. The proposed approach defines the team shape as a convex hull at each time frame, capturing its overall spatial structure. A comprehensive set of spatial, geometric, zone-based, and event-based descriptors is extracted to quantify the team’s shape and movement patterns. A two-stage clustering framework is employed to categorize team behavior. First, spatial clustering identifies broad positioning trends based on pitch location and zone overlap. Second, geometric clustering refines these clusters by analyzing shape variations, enabling the detection of distinct tactical patterns in both in-possession and out-of-possession scenarios. This process facilitates a data-driven interpretation of tactical strategies, helping analysts understand team behavior leading to goal-scoring opportunities, passing efficiency, and spatial control. The main contributions of this study include the development of a fully vector-based clustering approach that eliminates the need for computationally expensive image-processing techniques, the introduction of novel geometric descriptors tailored for team shape analysis, and the implementation of a two-stage clustering strategy that enhances the interpretability of tactical adjustments. The findings provide actionable insights for coaches and analysts, offering a quantitative framework for evaluating and optimizing team strategies.