Uncovering new ways to break down tight football defenses through AI

Breaking down tightly organized defenses ("low blocks") is one persistent challenge in modern football. In these situations, defenders crowd their own penalty area, leaving attackers with little space and time to create scoring opportunities. While existing data-driven approaches have improved the analysis of passes and shots, they often overlook a crucial aspect of the game: how players move and coordinate without the ball.

In a new study published in Intelligent Sports and Health, researchers from China and France developed an artificial intelligence (AI) model that learns from real-world match data to better understand and optimize attacking play against such compact defensive structures. Using large-scale event and tracking data from professional matches, the framework models each attacking player as an individual decision-maker while capturing how all players interact as a coordinated unit.

"Our goal was to move beyond analyzing isolated actions and instead understand football as a truly collective decision-making process," says Yi Pan, corresponding author of the study, "In particular, we wanted to capture how off-ball movements, which are often invisible in traditional statistics, contribute to creating space and breaking defensive lines.”"

The model simultaneously evaluates both on-ball actions, such as passing and carrying, and off-ball movements, such as runs that stretch or disrupt the defensive structure. "By learning from historical match data, it can assess the effectiveness of different strategies and even suggest alternative actions that could have led to better outcomes," says Pan.

One of the main findings is that the AI model tends to recommend more proactive and coordinated attacking behaviors compared to those typical observed in real matches. "Human players often favor safer, lower-risk decisions, but the model identifies opportunities where more dynamics movements and coordinated positioning could create space and increase scoring potential," explains Pan.

Importantly, the generated strategies remain tactically realistic and consistent with professional play, while introducing creative solutions that are rarely seen in practice. This balance between realism and innovation makes the approach particularly valuable for practical applications.

"This framework provides a new way for coaches and analysts to evaluate not just what happened in a match, but what could have happened," Pan adds. "By enabling counterfactual analysis of decisions and movements, it supports more informed tactical planning and offers deeper insights into how coordinated team behavior emerge on the pitch."

Beyond football, the research also contributes to the broader field of artificial intelligence by advancing methods for multi-agent decision-making in complex real-world environments, where coordination, uncertainty, and limited data pose significant challenges.

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Contact the author:

Yi Pan

Affiliation: Institute of Automation, Chinese Academy of Sciences

Email address: yi.pan@ia.ac.cn

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