paper-with-me

홈 › Papers

TacticAI: an AI assistant for football tactics

2023-10-16 · Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, William Spearman, Ian Graham, Jerome Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess, Michael Bowling, Demis Hassabis, Karl Tuyls

Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI's model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.

📄 PDF Abstract BibTeX arXiv:2310.10553

Code (0)

등록된 구현이 없습니다.

Tasks

Retrieval

Methods 이 논문이 사용한 방법론

Focus 설명 없음

Similar Papers 제목 키워드 기반

Optimising Game Tactics for Football

2020-03-23 · Ryan Beal, Georgios Chalkiadakis, Timothy J. Norman, Sarvapali D. Ramchurn

In this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer). We model the game of football as a multi-stage game which is made up from a Bayesian game to model the pr…

Decision MakingGame of Football

TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics

2026-04-20 · Sheng Xu, Guiliang Liu, Tarak Kharrat, Yudong Luo 외 arxiv

Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep learning have enabled predictive analyses like trajectory forecasting, t…

Trajectory Forecasting

TacEleven: generative tactic discovery for football open play

2025-11-17 · Siyao Zhao, Hao Ma, Zhiqiang Pu, Jingjing Huang 외 arxiv

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 …

Learning to Prove Theorems via Interacting with Proof Assistants

2019-05-21 · Kaiyu Yang, Jia Deng

Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order lo…

Automated Theorem ProvingMathematical ProofsMathematical Reasoning

Towards AI-Powered Video Assistant Referee System (VARS) for Association Football

2024-07-17 · Jan Held, Anthony Cioppa, Silvio Giancola, Abdullah Hamdi 외

Over the past decade, the technology used by referees in football has improved substantially, enhancing the fairness and accuracy of decisions. This progress has culminated in the implementation of the Video Assistant Re…

Fairness