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TacSIm: A Dataset and Benchmark for Football Tactical Style Imitation

arXiv 2603.25199 ↗First posted 26 Mar 2026cs.CVcited by 0

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Abstract (arXiv)

Current football imitation research primarily aims to opti mize reward-based objectives, such as goals scored or win rate proxies, paying less attention to accurately replicat ing real-world team tactical behaviors. We introduce Tac SIm, a large-scale dataset and benchmark for Tactical Style Imitation in football. TacSIm imitates the acitons of all 11 players in one team in the given broadcast footage of Pre mier League matches under a single broadcast view. Under a offensive or defensive broadcast footage, TacSIm projects the beginning positions and actions of all 22 players from both sides onto a standard pitch coordinate system. Tac SIm offers an explicit style imitation task and evaluation protocols. Tactics style imitation is measured by using spatial occupancy similarity and movement vector similarity in defined time, supporting the evaluation of spatial and tem poral similarities for one team. We run multiple baseline methods in a unified virtual environment to generate full team behaviors, enabling both quantitative and visual as sessment of tactical coordination. By using unified data and metrics from broadcast to simulation, TacSIm estab lishes a rigorous benchmark for measuring and modeling style-aligned tactical imitation task in football.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 26 Mar 2026.

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References · 20

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  3. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 257
  4. 2018SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videoscited by 148
  5. 2020SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videoscited by 129
  6. 2022SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videoscited by 80
  7. 2017Coordinated Multi-Agent Imitation Learningcited by 70
  8. 2019Multi-Agent Adversarial Inverse Reinforcement Learningcited by 60
  9. 2022Interactive Imitation Learning in Robotics: A Surveycited by 51
  10. 2020Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approachcited by 47
  11. 2023TacticAI: an AI assistant for football tacticscited by 43
  12. 2024SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimapcited by 34
8 more
  1. 2022TVCalib: Camera Calibration for Sports Field Registration in Soccercited by 22
  2. 2024Diffusion-Reward Adversarial Imitation Learningcited by 18
  3. 2024Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RLcited by 17
  4. 2020Multi-Agent Interactions Modeling with Correlated Policiescited by 13
  5. 2023Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTMcited by 12
  6. 2024Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Trackingcited by 12
  7. 2021Time-series Imputation of Temporally-occluded Multiagent Trajectoriescited by 7
  8. 2025CBIL: Collective Behavior Imitation Learning for Fish from Real Videoscited by 4

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