Pass Advisor: An Interactive Pass Decision Simulator

 

Freeze both teams at one instant. Where is the best place to pass the ball?Pass Advisor is a small interactive app that answers this question live inyour browser: open the demo —no install, no back...

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Pass Advisor: An Interactive Pass Decision Simulator

Freeze both teams at one instant. Where is the best place to pass the ball?

Pass Advisor is a small interactive app that answers this question live in your browser: open the demo — no install, no backend, one HTML file.

What it does

Given a static snapshot of both teams’ positions, the app estimates, for every point on the pitch:

  1. Pass success probability P — can the ball get there?
  2. Advantage gain ΔV = reward − λ·risk — how worthwhile is it once the pass succeeds?
  3. Expected value EV = P × ΔV — the combined recommendation.

The surface is evaluated on an 84×54 grid; a white ★ marks the optimum and an arrow points to it from the passer.

Expected value surface

Try it

  • Drag any dot to move a player (touch works on mobile); pick from seven formation presets (4-3-3, 4-4-2, 5-3-2, …).
  • Double-click a red player to make them the passer, or drag the ball to them.
  • Switch between the three surfaces (P / ΔV / EV) and watch the recommended target move.
  • Three sliders expose the model: λ (risk weight), interception sensitivity, control softness.

Pass success surface

Advantage surface

The model in one paragraph

Pass success combines a soft-Voronoi control probability (sigmoid of nearest-teammate vs nearest-defender distance to the target), lane openness (sigmoid of the minimum perpendicular defender distance to the passing lane), and a distance decay. Reward is control × proximity to goal, weighted toward the central channel; risk is a Gaussian density of defenders around the target. The framework follows SoccerMap (Fernández & Bornn 2020), the reward/risk EPV split of Overmeer et al. (2025), and the pitch-control tradition of Spearman (2017, 2018).

⚠️ The coefficients are a hand-tuned analytic approximation for teaching purposes — not fitted to real tracking data. Good for building intuition and playing with formations; serious analysis needs calibration on real data.

Formation presets

Next steps on the roadmap: a “pass to whom” mode that evaluates only the 11 teammates, player velocity vectors, and calibrating the coefficients on StatsBomb 360 freeze frames.