What it is

One window describes a moment; a distribution of windows describes a player. Player profiling aggregates thousands of movement signatures per athlete into a stable profile — a location and a shape in embedding space that captures how that player moves.

Because every profile lives in the same space, comparison stops being an exercise in adjectives. 'Explosive', 'economical', 'press-resistant' become measurable neighborhoods — and 'find me players who move like this one' becomes a query with a ranked answer.

ExampleA profile as a neighborhood query: the nearest profiles by movement, ranked. Illustrative rendering.

How it works

Profiles are running aggregates over a player's windows: mean signature, spread, and drift over time. The mathematics on top is deliberately simple — the power sits in the representation underneath. Profiles sharpen as the corpus grows: at proof-of-concept scale the model separates players within a match; robust fingerprinting across seasons is a data-scale milestone, not an algorithmic leap.

What you get

For scouting, profiling compresses hours of video review into a shortlist worth watching — every candidate arrives with the movement evidence that put them there. For development staff, it turns 'they look sharper this month' into a measured statement against the player's own history.

ParameterValueNotes
Profiledistribution over signaturesMean, spread, and trajectory in embedding space.
Similarity queryk nearest profilesA ranked shortlist; each hit with per-window evidence.
Development viewprofile drift over timeThe same player, measured against their own past.
Scale todaywithin-match separationValidated at proof-of-concept scale; see the status note below.

Status & roadmap

Prototype

Prototype. Similarity search over movement windows and embeddings runs in the internal workbench; the scouting workflow described here is the product framing of that capability. Claims scale with the corpus — and are labeled accordingly.