More Works from Our Lab

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI: A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence (AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings game-changing approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action (OODA) loop. In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decision-making models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

IEEE/CAA Journal of Automatica Sinica 2024

TacEleven: generative tactic discovery for football open play

Siyao Zhao†, Hao Ma†, ‡, Zhiqiang Pu* Jingjing Huang
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences
Arxiv 2025
†Indicates Equal Contribution, ‡Indicates Project Leader, *Indicates Correspondence
Paper Supplementary Code arXiv

A tactical sequence generation process of TacEleven for the game between PSG and Monaco in the 2022 season.

Abstract

Creating offensive advantages during open play is fundamental to football success. However, due to the highly dynamic and long-sequence nature of open play, the potential tactic space grows exponentially as the sequence progresses, making automated tactic discovery extremely challenging. To address this, we propose TacEleven, a generative framework for football open-play tactic discovery developed in close collaboration with domain experts from AJ Auxerre, designed to assist coaches and analysts in tactical decision-making. TacEleven consists of two core components: a language-controlled tactical generator that produces diverse tactical proposals, and a multimodal large language model-based tactical critic that selects the optimal proposal aligned with a high-level stylistic tactical instruction. The two components enables rapid exploration of tactical proposals and discovery of alternative open-play offensive tactics. We evaluate TacEleven across three tasks with progressive tactical complexity: counterfactual exploration, single-step discovery, and multi-step discovery, through both quantitative metrics and a questionnaire-based qualitative assessment. The results show that the TacEleven-discovered tactics exhibit strong realism and tactical creativity, with 52.50% of the multi-step tactical alternatives rated adoptable in real-world elite football scenarios, highlighting the framework's ability to rapidly generate numerous high-quality tactics for complex long-sequence open-play situations. TacEleven demonstrates the potential of creatively leveraging domain data and generative models to advance tactical analysis in sports.

Overflow of the TacEleven

An overflow of TacEleven. (A), An intuitive example of LTG. The model takes an event description and a historical spatiotemporal graph as input and outputs a future spatiotemporal graph; both graphs are visualized as tactical sketches. In the tactical sketches, the attacking direction is from left to right, with red representing teammates, blue representing opponents, and yellow representing the ball. Trajectories are illustrated from light to dark, culminating at the circles. Players performing events are highlighted with yellow hexagons and their corresponding targets are highlighted with yellow crosses. In this example, the input of the generator shows that Sanches passes to Verratti, and the output shows that Verratti passes to Neymar. Note that minor abrupt bends in the trajectories are attributable to noise in the data. (B), The generator–critic framework integrates three tasks with progressive tactical complexity: counterfactual exploration, single-step discovery and multi-step discovery. In counterfactual exploration, the generator use counterfactual tactical search to produce tactical proposals aligned with high-level event descriptions. In single-step discovery, the critic selects the most effective proposals with a high-level instructions. Multi-step discovery selects proposals iteratively, with the generator producing successive steps in an autoregressive manner. Together, these tasks create a counterfactual tactical tree search, enabling long-sequence tactic generation.

Multiple counterfactual outcomes generated under identical historical conditions

BibTeX

@article{YourPaperKey2024,
  title={TacEleven: generative tactic discovery for football open play},
  author={Siyao Zhao, Hao Ma, Zhiqiang Pu},
  journal={Arxiv},
  year={2025},
  url={https://arxiv.org/pdf/2511.13326}
}