Each task and its evaluation protocol is described, the challenge leaderboards are presented, and the leading submissions are summarized, with the aim of documenting the current state of each task as measured on held-out challenge data.
Abstract
The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video understanding. This year's challenges span five vision-based tasks: (1) Ball Action Anticipation, predicting the timing and class of ball-related actions within a short future window from a preceding observation window; (2) Player-Centric Ball Action Spotting, temporally localizing and classifying ball-related actions while assigning each action to the acting player through team affiliation and jersey number; (3) Novel View Synthesis, rendering images from unobserved camera poses in multi-view football scenes; (4) Spiideo SoccerNet Synloc, localizing athletes in real-world pitch coordinates from a single calibrated static-camera image; and (5) Visual Question Answering, answering multiple-choice questions about football broadcasts across text, image, and video inputs. For each task, participants were provided with annotated data, a unified evaluation protocol, and a public baseline. This edition saw broad participation, with 427 teams submitting 1,129 entries across the five tasks and 28 teams contributing reviewed technical reports. This paper describes each task and its evaluation protocol, presents the challenge leaderboards, and summarizes the leading submissions, with the aim of documenting the current state of each task as measured on held-out challenge data.
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Experiments show that current MLLMs struggle particularly on questions requiring the integration of multiple capabilities, whereas BasketballSkills outperforms them, highlighting the effectiveness of explicitly composing domain-specific capabilities for comprehensive basketball understanding.
Yi-Rong Hu, Jia-Yuan Rao, Yu Zhang et al.· 1 citation
The results suggest that global temporal context and explicit player-position cues are useful for generating more detailed soccer commentaries, while also revealing remaining trade-offs in precise timestamp localization.
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The first possession-level basketball video dataset (PL-NBA), in which each sample is composed of a complete NBA offensive possession, is constructed, demonstrating that PL-NBA is a challenging benchmark for sports video understanding.
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A real-time basketball scoring system that combines you only look once version 8 (YOLOv8) for hoop detection, a vision transformer (ViT) for spatiotemporal motion modeling, and deep simple online and realtime tracking (DeepSORT) for object tracking, in order to overcome the accuracy loss that classical systems suffer d...
Faisal Alzyoud, M. Tarawneh, Mohammad Alkhazaleh et al.· Bulletin of Electrical Engin...· 0 citations
Football video object detection is a fundamental task underpinning applications such as intelligent broadcasting, tactical analysis, and player tracking. Existing methods face two key challenges in football scenarios. (1) Localization bias in crowded regions: football players frequently gather in dense formations and o...
Jianjun Li, Hui Wei· Journal of King Saud Univers...· 0 citations
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