TacticAI: an AI assistant for football tactics (2310.10553v2)

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.

• The paper demonstrates TacticAI’s capability to predict the most likely corner kick receiver with notable top-3 test accuracy using graph neural networks.
• It employs geometric deep learning to model player interactions and pitch symmetries, ensuring robust predictions and effective tactical recommendations.
• TacticAI’s guided generation adjusts player positions and velocities, offering actionable insights to optimize set piece outcomes.

TacticAI: Enhancing Football Tactics with AI

Overview

Recent works in football analytics have increasingly benefited from advancements in AI and machine learning, and the paper detailed in this paper introduces TacticAI, an innovative AI-powered assistant designed for the analysis of football tactics, with a focus on corner kicks. Developed in collaboration with domain experts from Liverpool FC, TacticAI leverages geometric deep learning and graph neural networks to offer predictive insights and generate tactical recommendations, enhancing the strategic analysis of set pieces in football.

Predictive and Generative Components

TacticAI incorporates both predictive and generative aspects to assess and improve corner kick strategies. It predicts the most likely receiver of a corner kick and the probability of a subsequent shot attempt. These predictions are used as the basis for generating tactical adjustments that aim to either increase the likelihood of successful outcomes for the attacking team or decrease it for the defending team.

• Receiver Prediction involves identifying the player most likely to receive the ball after a corner is taken. TacticAI achieves a notable top-3 test accuracy, indicating its ability to accurately predict potential receivers from a set of twenty-two players on the pitch.
• Shot Prediction focuses on forecasting whether a shot attempt will follow the corner kick reception. By considering the identified receiver as a conditional factor, TacticAI significantly improves its predictions regarding shot attempts compared to an unconditional approach.
• Guided Generation of player positions and velocities facilitates the exploration of tactical setups. By adjusting player positions and velocities with the aim of achieving a desired outcome (e.g., a shot or no shot), TacticAI enables the evaluation and refinement of corner kick tactics.

Methodology

At the core of TacticAI is a graph-based representation of players and their interactions during corner kicks, modeled through graph neural networks (GNNs) and geometric deep learning. This representation captures the complex dynamics and strategic elements inherent in football. The application of group convolutions ensures that TacticAI's outputs respect the symmetrical nature of the football pitch, enhancing the model's data efficiency and enabling robust predictions and tactical suggestions.

• The use of Graph Neural Networks (GNNs) allows TacticAI to model the interactions between players effectively, treating each player as a node within a graph. This approach captures the relational dynamics that are crucial in understanding set piece execution.
• Geometric Deep Learning principles are applied to encapsulate pitch symmetries, ensuring the model's predictions and generative outputs remain consistent under transformations like reflections. This significantly contributes to the reliability and applicability of TacticAI’s insights, accommodating the spatial considerations vital in football tactics.

Implications and Future Directions

This research underscores the potential of integrating AI into football analytics, providing a foundation for future advancements in the strategic analysis of set pieces and beyond. TacticAI’s ability to offer actionable insights and tactical adjustments can serve as a valuable tool for coaches and analysts, enabling a data-informed approach to improving team performance.

The validation of TacticAI, through both quantitative metrics and a qualitative paper with football domain experts, highlights its practical utility and accuracy. The overwhelmingly positive feedback from the case paper with Liverpool FC experts attests to TacticAI's capacity to simulate real tactical scenarios and generate improvements recognized by professionals.

Looking ahead, there are opportunities to expand the scope of TacticAI and similar systems, potentially covering other set pieces and integrating more granular player data. Moreover, exploring natural language interfaces could make such tools more accessible, fostering interactive analysis and discussion among coaching staff and analysts.

Conclusion

TacticAI represents a significant step forward in the application of AI to football tactics, particularly in the nuanced and critical aspect of corner kicks. By combining geometric deep learning with graph neural networks, it provides precise predictive insights and practical tactical recommendations, offering a new dimension to the strategic planning and analysis capabilities available to football teams.