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Using Player's Body-Orientation to Model Pass Feasibility in Soccer

arXiv 2004.07209 ↗First posted 15 Apr 2020cs.CVcited by 20

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Abstract (arXiv)

Given a monocular video of a soccer match, this paper presents a computational model to estimate the most feasible pass at any given time. The method leverages offensive player's orientation (plus their location) and opponents' spatial configuration to compute the feasibility of pass events within players of the same team. Orientation data is gathered from body pose estimations that are properly projected onto the 2D game field; moreover, a geometrical solution is provided, through the definition of a feasibility measure, to determine which players are better oriented towards each other. Once analyzed more than 6000 pass events, results show that, by including orientation as a feasibility measure, a robust computational model can be built, reaching more than 0.7 Top-3 accuracy. Finally, the combination of the orientation feasibility measure with the recently introduced Expected Possession Value metric is studied; promising results are obtained, thus showing that existing models can be refined by using orientation as a key feature. These models could help both coaches and analysts to have a better understanding of the game and to improve the players' decision-making process.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 2; 2023: 6; 2024: 5; 2025: 0; 2026: 2.

20 papers.

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  1. 2022SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videoscited by 80
  2. 2023SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentariescited by 57
  3. 2021Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spottingcited by 43
  4. 2023VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Viewscited by 38
  5. 2024SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimapcited by 34
  6. 2021Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcastscited by 30
  7. 2021Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detectioncited by 27
  8. 2022Semi-Supervised Training to Improve Player and Ball Detection in Soccercited by 25
  9. 2024X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Modelcited by 22
  10. 2023Towards Active Learning for Action Spotting in Association Football Videoscited by 14
  11. 2023A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decisioncited by 11
  12. 2024Towards AI-Powered Video Assistant Referee System (VARS) for Association Footballcited by 8
8 more
  1. 2021DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Cameracited by 2
  2. 2021Learning Football Body-Orientation as a Matter of Classificationcited by 2
  3. 2023A Survey of Advanced Computer Vision Techniques for Sportscited by 2
  4. 2024OSL-ActionSpotting: A Unified Library for Action Spotting in Sports Videoscited by 2
  5. 2024Investigating Event-Based Cameras for Video Frame Interpolation in Sportscited by 1
  6. 2026Towards Athlete Fatigue Assessment from Association Football Videoscited by 1
  7. 2026SoccerNet 2026 Challenges Resultscited by 1
  8. 2023Passing Heatmap Prediction Based on Transformer Model and Tracking Datacited by 0