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TacticAI: an AI assistant for football tactics

Author

Listed:
  • Zhe Wang

    (Google DeepMind)

  • Petar Veličković

    (Google DeepMind)

  • Daniel Hennes

    (Google DeepMind)

  • Nenad Tomašev

    (Google DeepMind)

  • Laurel Prince

    (Google DeepMind)

  • Michael Kaisers

    (Google DeepMind)

  • Yoram Bachrach

    (Google DeepMind)

  • Romuald Elie

    (Google DeepMind)

  • Li Kevin Wenliang

    (Google DeepMind)

  • Federico Piccinini

    (Google DeepMind)

  • William Spearman

    (AXA Training Centre)

  • Ian Graham

    (Liverpool FC)

  • Jerome Connor

    (Google DeepMind)

  • Yi Yang

    (Google DeepMind)

  • Adrià Recasens

    (Google DeepMind)

  • Mina Khan

    (Google DeepMind)

  • Nathalie Beauguerlange

    (Google DeepMind)

  • Pablo Sprechmann

    (Google DeepMind)

  • Pol Moreno

    (Google DeepMind)

  • Nicolas Heess

    (Google DeepMind)

  • Michael Bowling

    (University of Alberta, Amii)

  • Demis Hassabis

    (Google DeepMind)

  • Karl Tuyls

    (Google DeepMind)

Abstract

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.

Suggested Citation

  • 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 &, 2024. "TacticAI: an AI assistant for football tactics," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45965-x
    DOI: 10.1038/s41467-024-45965-x
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    References listed on IDEAS

    as
    1. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    1. PURCAREA, Theodor Valentin, 2024. "Perceiving with Great Lucidity the Changing Times, Staying Informed and Responsible Managing AI’s Risk While Enabling Innovation," Romanian Distribution Committee Magazine, Romanian Distribution Committee, vol. 15(1), pages 10-15, March.

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