ArticleNature communications2024
TacticAI: an AI assistant for football tactics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of different approaches for performance enhancement in elite sport.Frontiers in artificial intelligence · 2026Pooled it
- Deep Learning for Sensor-Based Sport Performance and Health Monitoring: A Review of Wearable, Vision-Based, and Multimodal Sensing Approaches.Sensors (Basel, Switzerland) · 2026Review
- A graph-integrated reinforcement learning framework with graph neural networks for tactical decision modeling in professional football.Scientific reports · 2026Article
- Comparing human-led reflective dialogue and AI-based tactical guidance as innovative training approaches in soccer: effects on tactical performance and problem-solving.BMC sports science, medicine & rehabilitation · 2026Article
- Deep learning for dynamic tactical formation recognition in professional football.Scientific reports · 2026Article
- Comment on "Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care".Sports medicine (Auckland, N.Z.) · 2025Article
- Artificial Intelligence for Objective Assessment of Acrobatic Movements: Applying Machine Learning for Identifying Tumbling Elements in Cheer Sports.Sensors (Basel, Switzerland) · 2025Article
- Goal and shot prediction in ball possessions in FIFA Women's World Cup 2023: a machine learning approach.Frontiers in psychology · 2025Article
- The associations of AI assisted training on sport performance among student athletes based on a dual path chain mediation model with the moderating role of psychological adaptability.Frontiers in psychology · 2025Article
- Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management.Sensors (Basel, Switzerland) · 2024Review
- IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms.Sensors (Basel, Switzerland) · 2024Article
- The principles of tactical formation identification in association football (soccer) - a survey.Frontiers in sports and active living · 2024Review
Corrections and comments
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Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.