Once Upon a Goal: Towards orientation-based shot metrics in football
Marc Gutiérrez-Pérez, Calvin Yeung, Keisuke Fujii, Antonio Agudo · Computer Vision and Image Understanding · 2026
Sports analytics has been revolutionized by advanced tracking technologies, yet the integration of human pose estimation into performance metrics remains underexplored in football. Estimating the probability of scoring from a shot is a central task in football analytics and is commonly approached through expected goals (xG) models. Progress in this area, however, is often constrained by the limited availability of publicly accessible datasets that include fine-grained biomechanical information. In this work, we present xGHub, an open-source dataset of football shots enriched with player pose estimation, body orientation, and contextual features extracted from broadcast video. The dataset is generated using an automated pipeline for player detection and tracking, followed by an external verification process to ensure annotation reliability. As a use case, we analyze how pose- and orientation-related features can be incorporated into a standard xG modeling framework. Our results indicate that 3D orientation information is informative for specific subsets of shots, while its contribution is limited in others, reflecting the inherently non-linear nature of angular representations. This analysis serves to illustrate the potential and limitations of the released annotations. By making this dataset publicly available, we aim to support future research on the role of player biomechanics in shot analysis and related football analytics tasks.Sports analytics has been revolutionized by advanced tracking technologies, yet the integration of human pose estimation into performance metrics remains underexplored in football. Estimating the probability of scoring from a shot is a central task in football analytics and is commonly approached through expected goals (xG) models. Progress in this area, however, is often constrained by the limited availability of publicly accessible datasets that include fine-grained biomechanical information. In this work, we present xGHub, an open-source dataset of football shots enriched with player pose estimation, body orientation, and contextual features extracted from broadcast video. The dataset is generated using an automated pipeline for player detection and tracking, followed by an external verification process to ensure annotation reliability. As a use case, we analyze how pose- and orientation-related features can be incorporated into a standard xG modeling framework. Our results indicate that 3D orientation information is informative for specific subsets of shots, while its contribution is limited in others, reflecting the inherently non-linear nature of angular representations. This analysis serves to illustrate the potential and limitations of the released annotations. By making this dataset publicly available, we aim to support future research on the role of player biomechanics in shot analysis and related football analytics tasks.