Embedding Space
Scalable Player Profiling
Every player’s movement signature, placed in one shared space. Scouting becomes similarity search: “find me players who move like this one.”
Where this shows upTeam Intelligence
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.
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; stable profiles across whole seasons are 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.
| Parameter | Value | Notes |
|---|---|---|
| Profile | distribution over signatures | Mean, spread, and trajectory in embedding space. |
| Similarity query | k nearest profiles | A ranked shortlist; each hit with per-window evidence. |
| Development view | profile drift over time | The same player, measured against their own past. |
| Scale today | within-match separation | Validated at proof-of-concept scale; see the status note below. |
Status & roadmap
Prototype. Similarity search over movement windows and their signatures runs in the internal workbench; the scouting workflow described here is how that capability surfaces in the product. Claims scale with the corpus — and are labeled accordingly.