How AI and Deep Learning Is Changing Football Tactics

Heres the thing, Football has always been a game of strategy, instinct, and lightning-fast decisions and very subjective and very human. But in today’s data-driven world, the beautiful game is evolving and we can’t ignore it. My recent research explores how deep learning and video analysis can transform football analysis—giving coaches, analysts, and fans unprecedented insights into match dynamics, player positioning, and tactical transitions.

In this blog, I'm showing Askdata in action and excited to share some of the key takeaways from my a personal project, where I built and tested multiple AI models to detect and differentiate between attacking and defending scenarios using real league and non league football footage.

The Problem: Making Sense of the Chaos

Football is fluid. Players switch roles in a heartbeat, and key moments—like a successful high press or a defensive lapse—can change the outcome of a game. Traditional analysis tools rely heavily on manual tagging, human judgment, and limited post-match review.

But what if a computer could watch the match in real time and tell you not only where each player is, but also whether your team is attacking, defending, or transitioning—and how effectively?

That was the challenge I set out to solve.

The Approach: Deep Learning Meets the Pitch

Using cutting-edge deep learning models (including YOLOv8 and StrongSORT), I trained four different systems—Run4, Run13, Run22, and Run43—on a curated football dataset. Each model had its own strengths and weaknesses in terms of accuracy, speed, and real-time viability.

Key goals included:

• Automatically detecting players, referees, and the ball

• Classifying whether the team was in an attacking or defending phase

• Understanding positional play and identifying strategic patterns

• Comparing performance metrics across different training setups and resolutions

The Results: Trade-Offs and Tactical Insights

The best performing model, Run43, achieved 62% average precision, excelling at identifying goalkeepers and field players with near 90% accuracy. However, it was too computationally heavy for real-time use—especially when tracking fast-moving objects like the ball.

Interestingly, Run4, despite being slower and slightly less accurate overall, proved to be the most tactically insightful. It was the only model that consistently differentiated between attacking and defending players, providing the kind of information that coaches and analysts dream about.

Run13, on the other hand, was lightning fast and showed promise for live feedback systems, albeit at the cost of some tactical nuance.

From Lab to Dugout: Real-World Use Cases

Here’s where it gets exciting.

Imagine a coaching team with an AI dashboard during a match. While watching the game, they get real-time updates on team shape, player roles, and even patterns of attack and defence. The system highlights when a team is playing out from the back or launching a central attack—and does it without any manual input.

Now imagine the same tool being used post-match, to break down key transitions, player decision-making, or identify moments of tactical breakdown.

We’re not just talking about fancy stats—we’re talking about actionable tactical insight, powered by AI.

Challenges and Future Work

No system is perfect, and mine wasn’t either. Let’s face it 62% is no way good enough, but it’s a start in a small space of time done at the side of a desk. Ball tracking proved the hardest to master, with all models showing reduced accuracy in detecting the ball—especially during fast-paced plays or crowded scenes.

Future improvements could include:

• Using object tracking algorithms like Kalman filters

• Augmenting datasets with synthetic examples

• Building hybrid models that combine tactical awareness with fast object detection

• Expanding the system to include pitch-wide zone analysis, player movement trends, and predictive analytics

Why This Matters for the Future of Football

AI won’t replace coaches, but it will empower them.

This project shows that with the right models, real-time tactical feedback isn’t just possible—it’s achievable today. By automating scenario recognition, we can help analysts work faster, make better decisions, and uncover patterns that might otherwise go unnoticed.

From grassroots clubs to elite European sides, AI-driven football analysis is on the rise, and I’m proud that my work is a small part of that journey.

Interested in the tech or the results? Want to collaborate or discuss? Get in touch—I’d love to hear from you.