Talent Discovery AI reads verified performance, skill and fitness signals across regions and stages to surface athletes who would otherwise go unseen — with explainable scores, explicit confidence, and a human in the loop. It is guidance to act on, not a verdict on a career.
Four models, one job: turn verified athlete data into honest, actionable signals. Where an output is an estimate, we say so — and we show the confidence behind it.
Surfaces emerging and hidden talent across regions, sports and stages — ranked candidates a human still chooses to pursue.
Maps current level → next milestone → the gap between them → a concrete development plan, computed per athlete.
A probability of selection with an explicit confidence band. It is a modelled estimate from limited data — never a promise of selection.
Technique scoring and auto-highlights from match footage. In development — early outputs will ship clearly flagged as experimental.
The engine is only as honest as its data. Every signal below is drawn from verified records — and weighted by how complete and recent that data is.
The measurable outputs of how an athlete actually competes, tracked over time rather than from a single standout day.
Sport-specific technical and tactical markers that describe what an athlete can do, not just what they scored.
Physical capacity indicators that put performance in context and flag development headroom.
The same numbers mean different things in different settings — so context is part of every signal.
The Talent Index places an athlete in context against a relevant peer group rather than an abstract scale.
Signals only count when their source can be trusted — the Verification Network proves where each record came from.
An AI that decides careers would be reckless. Ours is built to inform the people who do — transparently, with its limits in plain sight.
Every estimate ships with a confidence indicator. Thin or stale data lowers it visibly, so no one mistakes a guess for a fact.
No black boxes. Each score can be traced to the signals that drove it, so athletes and scouts can see the why.
AI suggests; humans decide. The engine ranks, flags and explains — coaches, scouts and selectors make the call.
Put the talent-ID engine to work — surface the athletes, then make the call yourself.