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Multi-Criteria Scout Filtering (Weighted Sliders)

RecruitmentLevel 2 — Intermediate

What It Is

Multi-criteria scout filtering uses weighted threshold sliders across multiple metrics to generate a ranked shortlist from a large player database. Rather than computing similarity to a single reference player, it applies minimum thresholds and relative weighting to each metric and returns every player who meets those criteria, ranked by composite score. This is the right tool when you have defined metric requirements but no single reference player — or when you want to see the full range of players who meet a profile rather than the nearest neighbors to one archetype.

Correct Execution

Setup: (1) select the position template (striker, CDM, etc.); (2) assign each relevant metric a minimum threshold and a relative importance weight (moving slider right = must have more of this); (3) optionally filter by age range, league coverage, and minutes played floor; (4) run the filter and review the output ranked list with metric breakdowns. The slider position translates to percentile thresholds. Typically use 5-8 metrics — more than 10 creates overfitting to the filter and returns no candidates.

Progression Levels

Diagnostic Tree

Coaching Cues

  • "The filter turns 5,000 players into 50. The scout turns 50 into 5. Data and scouts work together." — WFS 2019
  • "Set the non-negotiables high. Set the nice-to-haves as moderate. Leave room for the list to breathe."

Common Errors

  1. Setting thresholds too strictly on all dimensions simultaneously: Creates "no one in the world passes" outputs.
  2. Not including a minutes-played floor: Players with 300 minutes will have small-sample rate stats that look elite but are noise.
  3. Treating the ranked list as the final recommendation: The filter is a scouting input, not a decision. Scouts must then watch clips to validate.

Edges

💎 Elite-Only Behavior

The Best Recruitment Filter Is the Game Model — Not the Position

Most recruitment pipelines filter candidates by position first, then evaluate within position. Elite recruitment pipelines filter by game-model skill requirements first, which sometimes surfaces candidates from unexpected positions. A wide midfielder who profiles identically to your game model's fullback requirements — but has never played fullback — is a legitimate candidate that position-first filtering would eliminate. The skill profile is the constraint, not the positional label.

What most people do
Search for "right-backs" and evaluate all right-backs against the requirements.
What the best do
Define the skill requirements from the game model (progressive carrying, cross-field distribution, pressing intensity, defensive 1v1 rate) without specifying position. Search across all positions for players matching those requirements. Then check positional feasibility as a secondary filter.
Why it's an edge: Position labels are historical artifacts of where a player was deployed, not necessarily a description of their capabilities. Players are regularly "discovered" in new positions (Kimmich at right-back, Alaba at left-back, Walker at center-back) — data-first recruitment finds these transitions before they happen.
How to exploit: For each position to fill, define the skill vector from the game model. Run similarity search across ALL positions, not just the target position. Flag any cross-position candidates whose skill profile matches >80% of requirements.
Ted Knutson, Barcelona Coach Analytics Summit, 2018-11-18. Game model as the anchor for all recruitment decisions.

Sources

  • Ted Knutson & Siqur Arshad, WFS 2019 StatsBomb presentation, YouTube, 2019-10-02 — demonstrated StatsBomb IQ scout feature with weighted metric sliders; showed striker search returning 55 candidates including Mbappé at top; described as complementary to similarity scores