We Built It. It's Here.

We started Dead Ball Analytics because we believed football data could do more than it was doing.

It's time to see if we were right.

This is our launch post. We'll explain what we built, what problem it solves, and where we're taking it. But first thank you for our full organic 1763 followers and early believers. It genuinely means a lot!

There is a strange tension at the heart of modern football analysis. Tracking systems now capture the position of every player and the ball, dozens of times per second, across every minute of every match. The volume of data available to clubs today would have seemed impossible a decade ago. And yet, the most important questions in tactical analysis are still answered the same way they always were: someone watches video, takes notes, clips and creates a report.

Why does the defensive shape remain in some transitions and collapse in others? When does a team actually shift from a mid-block to a low block and what triggers it? How does a team’s attacking structure change in the 15 minutes after conceding?

These questions are not exotic. They are the core of what performance analysts and opponent analysts do every day. And they remain, almost entirely, outside the reach of data tools. This is the gap we built Dead Ball Analytics to close.

The problem is not a lack of data. It’s a lack of interpretation. Tracking data is positional. It tells you where players are. What it doesn’t tell you is what requires a significant layer of intelligence to extract or what is what those positions mean tactically. A defensive shape is not just 11 coordinates. It is a structure with height, compactness, width, and stability and properties that change continuously during a match, that respond to the ball, to the opponent, to the score.

Most analytical tools were not built to read that. They were built for event data: passes, shots, duels. Discrete moments. Countable things.Tactics are not discrete. They are continuous, spatial, and deeply contextual. And that is precisely why they have resisted quantification for so long.

Today, we are launching the Tactical Shape Intelligence Engine. TSIE is an AI system that reads tracking data and automatically extracts the tactical structures and transitions that analysts currently have to find by hand.

What it identifies:

— Defensive block structure, height, and spatial organisation
— Attacking shapes and how they form through transitions
— The exact moments a team changes its tactical state during a match
— A full structural timeline across every minute of the game

It works without synchronized event data. It scales across full fixture lists. And it is designed to give analysts back the hours currently spent on things a system should be doing.

This is version one.

We are building toward something larger: a full tactical intelligence platform that understands not just shape, but behaviour, the pressing structures, the positional exchanges, the strategic adjustments that define how teams actually play.

But that work starts here. With the question of structure. With the thing that, until now, data couldn’t see. If you work in football analytics or if you’re just curious about where this field is going, we’d like to show you what we’ve built.

Demo and early access link below.

https://sie.deadball-analytics.com/

This is just the beginning. We're building fast, and you'll hear about its first new features, research notes, things we're learning along the way.

More on that is coming soon!