A worked example of the player-scouting analysis behind the Data Scouting tile: three ways of using Eredivisie player data to surface names worth a closer look: a style map built with PCA, an anomaly-detection pass that flags players whose numbers don't fit their peer group, and a contextual scatter for a specific statistical profile. 255 outfield players, 900+ minutes, Opta/StatsPerform event data, Waltzing Analytics house style.
Similar players cluster together: PCA of 5 composite performance scores, reducing attacking output, passing quality, defensive quality, creativity and press resistance to a 2D style map.Finding the players whose numbers don't fit the pattern: an Isolation Forest model flags the players whose statistical profile is least like everyone else's.Progressive on the ball, active without it: a contextual scouting scatter built around a specific profile: progressive passing and defensive work rate together, not traded off against each other.
