Research Published 19 March 2024
Authors: By Zhe Wang and Petar Veličković
As part of our multi-year collaboration with Liverpool FC, we develop a full AI system that can advise coaches on corner kicks
‘Corner taken quickly… Origi!’
Liverpool FC made a historic comeback in the 2019 UEFA Champions League semi-finals. One of the most iconic moments was a corner kick by Trent Alexander-Arnold that lined up Divock Origi to score what has gone down in history as Liverpool FC’s greatest goal.
Corner kicks have high potential for goals, but devising a routine relies on a blend of human intuition and game design to identify patterns in rival teams and respond on-the-fly.
Today, in Nature Communications, we introduce TacticAI: an artificial intelligence (AI) system that can provide experts with tactical insights, particularly on corner kicks, through predictive and generative AI. Despite the limited availability of gold-standard data on corner kicks, TacticAI achieves state-of-the-art results by using a geometric deep learning approach to help create more generalizable models.
We developed and evaluated TacticAI together with experts from Liverpool Football Club as part of a multi-year research collaboration. TacticAI’s suggestions were preferred by human expert raters 90% of the time over tactical setups seen in practice.
TacticAI demonstrates the potential of assistive AI techniques to revolutionize sports for players, coaches, and fans. Sports like football are also a dynamic domain for developing AI, as they feature real-world, multi-agent interactions, with multimodal data. Advancing AI for sports could translate into many areas on and off the field – from computer games and robotics, to traffic coordination.
TacticAI is a full AI system with combined predictive and generative models to analyze what happened in previous plays and how to make adjustments towards making a particular outcome more likely.
Developing a game plan with Liverpool FC
Three years ago, we began a multi-year collaboration with Liverpool FC to advance AI for sports analytics.
Our first paper, Game Plan, looked at why AI should be used in assisting football tactics, highlighting examples such as analyzing penalty kicks. In 2022, we developed Graph Imputer, which showed how AI can be used with a prototype of a predictive system for downstream tasks in football analytics. The system could predict the movements of players off-camera when no tracking data was available – otherwise, a club would need to send a scout to watch the game in person.
Now, we have developed TacticAI as a full AI system with combined predictive and generative models. Our system allows coaches to sample alternative player setups for each routine of interest, and then directly evaluate the possible outcomes of such alternatives.
TacticAI is built to address three core questions:
- For a given corner kick tactical setup, what will happen? e.g., who is most likely to receive the ball, and will there be a shot attempt?
- Once a setup has been played, can we understand what happened? e.g., have similar tactics worked well in the past?
- How can we adjust the tactics to make a particular outcome happen? e.g., how should the defending players be repositioned to decrease the probability of shot attempts?
Predicting corner kick outcomes with geometric deep learning
A corner kick is awarded when the ball passes over the byline, after touching a player of the defending team. Predicting the outcomes of corner kicks is complex, due to the randomness in gameplay from individual players and the dynamics between them. This is also challenging for AI to model because of the limited gold-standard corner kick data available – only about 10 corner kicks are played in each match in the Premier League every season.
Learn more about TacticAI
This project is a collaboration between the Google DeepMind team and Liverpool FC. The authors of TacticAI include: Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini,…