Scouting AI
Player Analysis

How to Build a Data-Informed Player Shortlist

By Sarah Mitchell22 January 20269 min read

Effective shortlists balance structured data with professional judgment. Here is a framework recruitment teams can apply before the transfer window.

Define the requirement first

Before searching for players, recruitment teams should document the tactical role, age range, budget parameters, passport requirements and physical expectations. Without a clear requirement, data analysis produces interesting but irrelevant results.

Use data to create a longlist, not a final decision

Performance metrics, percentile rankings and market indicators help identify candidates worth deeper investigation. The longlist should be large enough to avoid premature narrowing, but structured enough to compare players against the same criteria.

  • Position-specific metrics relevant to the required role
  • Playing-time consistency and availability indicators
  • Estimated market value relative to budget
  • Tactical fit against the club's configured identity
  • Risk factors including injury history and data limitations

Integrate human scouting before shortlisting

Data reduces the search space. Scouts validate whether a player's on-pitch behaviour matches the statistical profile. Video scouting and live observation remain essential before a player enters the final shortlist.

Request a demo to see shortlist workflows in Scouting AI.

Frequently asked questions

How many players should be on a recruitment longlist?
This depends on the position and market, but longlists typically contain enough candidates to allow meaningful comparison before narrowing to a shortlist of three to six players.

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