A player similarity score computes how statistically close a candidate player is to a target archetype, using a weighted distance metric across the relevant profile features. Given a departing player (or archetype), the model surfaces the most similar players in the broader dataset — including players no scout has currently flagged. The primary value: surfacing unknown candidates who match the profile but aren't on anyone's radar.
Build a similarity score by: (1) selecting the metrics that define the role profile; (2) normalizing each metric to a common scale (z-score or percentile within position-peer group); (3) computing weighted Euclidean distance from the target player's profile vector. Lower distance = more similar. Filter by age range, league level, and contract status before presenting results. Always present the full profile comparison side-by-side, not just the score — the score is a shortlist tool, not a decision.
Similarity search across multi-dimensional player profiles (20+ metrics, position-adjusted, weighted by game-model importance) systematically identifies candidates from leagues and clubs that scouts don't cover. The most valuable output of similarity search isn't confirming the scouting shortlist — it's surfacing players from lower leagues, smaller clubs, or unfashionable positions who profile identically to the target but are invisible to the scouting network. These are consistently the highest-ROI transfers.