Not all shots are equal. Expected goals rates each chance by the probability an average player would score from that spot — the metric that revolutionized soccer analysis.
Expected Goals (xG)Live
the quality of a chance
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Expected goals rates every shot by the probability an average player would score from that spot, learned from thousands of historical shots. The two biggest drivers are distance and angle to goal — a tap-in near the six-yard box is worth 0.8 xG, a long-range effort barely 0.03. Summing xG over a match reveals who truly deserved to win, beyond the scoreline. Click to place a shot.
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How it works
An xG model estimates the scoring probability of a shot from features like distance and angle to goal, trained on thousands of historical attempts. A close-range tap-in may be worth 0.8 xG, a long-range strike just 0.03. Summing xG across a match reveals which team created the better chances, cutting through the noise of a fluky scoreline. Click to place a shot.
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Ask the AI about this model
The math, the assumptions, real-world uses, or a code translation — explained for this exact simulation.
Is this expected goals xG soccer tool really free?▾
Yes. Expected Goals (xG) runs entirely in your browser using your device's own compute, so local use is free forever. You only pay Compute Tokens if you scale a job to the cloud.
Do I need to install anything?▾
No. Everything runs client-side in a modern browser — no downloads, no license, no account required to start.
Can I save or share my simulation?▾
Create a free account to save projects, and use a shareable embed or minted DOI to publish a live, interactive version anywhere.
How accurate are the results?▾
The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.