For the past few months, I've been obsessed with the idea of teaching a machine to see a football pitch like a coach. We all hear about "pockets of space," but they're hard to quantify. So, I built a tool that does exactly that.

**What you're seeing in the video:**

This is my "Tactical Sandbox." It's a 3D reconstruction of a real match. I've trained a hybrid CNN (a ResNet-34 backbone + MLP) to identify "Leakages" (exploitable weaknesses in a team's defensive structure) and assign a score based on

\- threat: space quality in relation to creating a chance. e.g Distance/angle to goal, is space behind line, e.tc.

\-exploitability : space quality in relation to control of the space e.g fastest player to space, overload, e.t.c.

\-feasibility: how feasible it is to get the ball into the leakage quadrant. e.g number of defenders in passing lane, pressure factor, distance to LQ etc.

In Example 2, When I drag a player out of position, It sends the new game state to a prediction server(running on my M1) in real-time. The AI analyzes the scene and sends back a prediction, including:

* **Where** the leakage is (the heatmap).
* **How big** it is (the box size).
* **How dangerous** it is (the "Leakage Score" or LS, colored from green to red).

The LS score isn't just a raw model output; it's a "data-driven heuristic" that combines the AI's learned intuition with objective factors like distance to goal, angle, and whether a player can win the race to the ball.

**The Tech Stack:**

* **Frontend/3D:** Three.js
* **Backend Servers:** Flask (Python)
* **AI Model:** PyTorch (ResNet-34 backbone)
* **Data:** My own hand-labeled data + synthetic data from the simulator, plus the open-source SkillCorner dataset for testing.

This moves analysis from "what happened" to "what if?" You can instantly see the tactical consequence of a single player being two meters out of position. I'm hoping to build this out as a tool for coaches and analysts to test tactics and train players.

premature ideas for use cases:

* **Live, in-game analysis (Coach’s tablet)** *Today:* Sideline staff rely on intuition and a few replays. *MODEL:* Live tracking flags recurring leakages (e.g. every time their #8 drifts wide an LS > 0.7 appears between RB and R-CB). Result: precise instruction. “Right-back, stay five yards narrower.”
* **Half-time tactical adjustments** *Today:* Coaches watch 2–3 clips and guess priorities. *MODEL:* A processed timeline of leakage events reveals patterns (e.g. buildup leakages LS ≈ 0.5 caused by lack of pressure on the deep-lying playmaker), enabling specific, time-efficient fixes for the second half.
* **Deep opposition analysis (pre-match)** *Today:* Hours of footage and manual tagging to identify patterns. *MODEL:* Process multiple matches into a data-rich report. Query examples: “Show Immediate Threat leakages with LS > 0.8 from counters” or “Who most often exploits time\_advantage in the final third?” Use the simulator to probe tactical tweaks.
* **Player development & training** *Today:* Show a clip and say “you were out of position.” *MODEL:* Load the state in the simulator, move the player two meters, and show LS drop (e.g. 0.75 → 0.15). Immediate visual + numeric feedback = faster learning and clearer coaching.



Happy to answer any questions about the process!

I trained an AI model to detect positional vulnerabilities(Leakages) in a Football game. Here's it running on a Live game.