U23 'Potential Score' screens
Scouting has shifted to PCA/clustering archetypes and 'Potential Score' screeners for U23s—examples show candidates with long passes at the 100th percentile and aerial duels at the 83rd (x.com). Cricket analytics are following suit: PSL groups are running ball‑by‑ball 'Runs Above Average Replacement' models to quantify batter impact (x.com).
A public u23 scouting codebase using PCA and k‑means to group young players across European leagues has been posted on GitHub and documents the exact variables and preprocessing used for its 'player archetype' screens. (github.com) Commercial scouting platforms already surface a single "Potential" index on player cards and let users filter by that 0–100 score in their recruitment products. (scoutwise.ai) (scoutwise.ai; scisports.com) The analytics stack shown in recent screens mixes dimensionality reduction and clustering—PCA for score vectors plus archetypal or k‑means clusters to label playing‑style groups—techniques explained in public tutorials and PCA vignettes used by practitioners. (archetypes.readthedocs.io) (archetypes.readthedocs.io; cran.r-project.org) Percentile displays such as "long passes 100th" or "aerial duels 83rd" mirror the normalization used in club indices that convert per‑action rates to league‑adjusted 0–100 scales for positional comparison. (scisports.com) (scisports.com; github.com/TouziOmar/u23-football-player-analysis) In the Pakistan Super League, public leaderboards and data pages now feature advanced impact metrics and Smart Stats summaries, and PSL teams are publishing match‑level data that enable ball‑by‑ball modelling. (espncricinfo.com) (espncricinfo.com; psl-t20.com) Academic and open‑source cricket work has adapted sabermetric concepts—papers and projects such as cricWAR and independent "Runs Above Average" tools compute ball‑by‑ball run values, RAA and VORP analogues for limited‑overs cricket. (sloansportsconference.com) (sloansportsconference.com; cricketraa.com) Several PSL‑focused dashboards and repos show the practical pipeline: ball‑by‑ball ETL feeding Streamlit or dashboard layers that output expected‑runs and runs‑above‑average metrics for batters and innings. (github.com)
