The crowd sees what data misses.
Turn your match knowledge into a visible collective signal. Pick a team, read the moment, rate performance, and compare fan instinct with an explainable AI model.
Fictional clubs and a recorded match created for product demonstration.
Contribute in context.
Move through the recorded match timeline. Each phase captures a different kind of fan knowledge without presenting it as a live feed.
Your pre-match selection
Experiment freely. Formation changes preserve your current eleven.
Build the Harbor City lineup
Width from both forwards with three central passing lanes.
Select any player, then choose an eligible substitute.
Nothing is added to the aggregate until the server accepts the signal. A sandbox success confirms storage behavior, not live match data.
See the crowd without losing the individual.
Every bar below is an aggregate of recorded signals. Your choices remain separate until the contribution API confirms them.
Checking for a connected fan-signal snapshot…
What the crowd is feeling
Sentiment describes fan perception, not an objective match-state measurement.
The aggregate leans positive, but the concerned and frustrated minorities remain visible rather than being averaged away.
Fan instinct vs. tactical model
Illustrative 0–100 priority scores for the same fictional match. The AI series is a fixed sandbox model output, not a live recommendation.
Where they diverge
Fans weight repeated wide attacks more strongly than the model.
The model places greater value on protecting central transitions.
Both agree on pressure, while the model flags its energy cost.
Every voice stays visible
Distributions preserve agreement and disagreement, so minority readings are not hidden by one score.
Context travels with the signal
Match phase and recorded moment accompany each contribution, keeping reactions interpretable later.
Continue with FET AI
Ask the personal assistant to explain the shapes, roles, and trade-offs behind your selection.