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Introduction
Artificial intelligence (AI) has revolutionized many fields, and sports analytics is one area where AI has immense potential. Recently, a team of researchers from Google DeepMind and Liverpool FC has developed an AI system called TacticAI that can provide expert coaches with tactical insights to improve their team’s performance. In this article, we will explore how TacticAI works, its capabilities, and its potential impact on the world of sports.
Developing a Game Plan with Liverpool FC
Five 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.
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.
TacticAI successfully predicts corner kick play by applying a geometric deep learning approach. First, we directly model the implicit relations between players by representing corner kick setups as graphs, in which nodes represent players (with features like position, velocity, height, etc.) and edges represent relations between them. Then, we exploit an approximate symmetry of the football pitch. Our geometric architecture is a variant of the Group Equivariant Convolutional Network that generates all four possible reflections of a given situation (original, H-flipped, V-flipped, HV-flipped) and forces our predictions for receivers and shot attempts to be identical across all four of them. This approach reduces the search space of possible functions our neural network can represent to ones that respect the reflection symmetry — and yields more generalizable models, with less training data.
Providing Constructive Suggestions to Human Experts
By harnessing its predictive and generative models, TacticAI can assist coaches by finding similar corner kicks, and testing different tactics.
Traditionally, to develop tactics and counter tactics, analysts would rewatch many videos of games to look for similar examples and study rival teams. TacticAI automatically computes the numerical representations of players, which allows experts to easily and efficiently look up relevant past routines. We further validated this intuitive observation through extensive qualitative studies with football experts, who found TacticAI’s top-1 retrievals were relevant 63% of the time, nearly double the 33% benchmark seen in approaches that suggest pairs based on directly analyzing player position similarity.
Advancing AI for Sports
TacticAI is a full AI system that could give coaches instant, extensive, and accurate tactical insights – that are also practical on the field. With TacticAI, we have developed a capable AI assistant for football tactics and achieved a milestone in developing useful assistants in sports AI. We hope future research can help develop assistants that expand to more multimodal inputs outside of player data, and help experts in more ways.
Conclusion
In this article, we have explored the development and capabilities of TacticAI, an AI system that can provide expert coaches with tactical insights to improve their team’s performance. TacticAI uses geometric deep learning to predict corner kick outcomes and provides constructive suggestions to human experts. This technology has the potential to revolutionize the world of sports and provide a new level of accuracy and efficiency in game planning and strategy.
Frequently Asked Questions
Q: What is TacticAI?
TacticAI is an AI system that provides expert coaches with tactical insights to improve their team’s performance. It uses geometric deep learning to predict corner kick outcomes and provides constructive suggestions to human experts.
Q: How does TacticAI work?
TacticAI works by representing corner kick setups as graphs, in which nodes represent players and edges represent relations between them. It then uses a geometric architecture to generate all possible reflections of a given situation and forces its predictions for receivers and shot attempts to be identical across all four of them.
Q: What are the benefits of using TacticAI?
The benefits of using TacticAI include providing instant, extensive, and accurate tactical insights, reducing the need for extensive video analysis, and providing a new level of accuracy and efficiency in game planning and strategy.
Q: Who developed TacticAI?
TacticAI was developed by a team of researchers from Google DeepMind and Liverpool FC as part of a multi-year collaboration to advance AI for sports analytics.
Q: What are the potential applications of TacticAI?
The potential applications of TacticAI include developing assistants that expand to more multimodal inputs outside of player data, helping experts in more ways, and providing a new level of accuracy and efficiency in game planning and strategy.
