Six tiers from Elite to Below Standard

Each grade places a player on a six-point scale relative to position peers across 20+ leagues. The percentile ranges reflect where a player sits within their position group after league adjustment.

Grade
SW-1
Elite
98th+ percentile
Among the best at this position. Sustained elite output across dimensions.
Grade
SW-2
Excellent
90th–97th percentile
Consistent top-league starter quality. Exceptional in one or more dimensions.
Grade
SW-3
High Quality
70th–89th percentile
Strong professional. Reliable across assessment windows.
Grade
SW-4
Functional
40th–69th percentile
Competent at their level. Value dependent on deployment context.
Grade
SW-5
Limited
15th–39th percentile
One or more dimensions significantly constrained at this level.
Grade
SW-6
Below Std
<15th percentile
Multiple dimensions underperforming. Significant quality concerns.

Within each tier, grades carry a modifier: + (top third), no modifier (middle third), or (bottom third) — producing 18 effective grade levels. An SW-2+ sits at the boundary of Elite; an SW-2− is closer to High Quality.

What each grade measures

Every grade is a composite of four computed dimensions. The dimensions are weighted by position — the blend for a centre-forward is not the same as for a centre-back. The composite produces a continuous score (0–100) that maps to the discrete grade tiers above.

STAT
Statistical Profile
Position-specific peer ranking using multiple per-90 metrics, smoothed across seasons and adjusted for league difficulty. A centre-back's statistical profile is built from different metrics than a winger's.
MKT
Market Signal
Where the market positions this player relative to position peers. Captures valuation context including age, contract status, and availability.
PTN
Pattern Intelligence
Multi-detector convergence from automated detection routines. When multiple independent signals align — structural role changes, output anomalies, valuation discrepancies — the assessment carries more weight.
FWK
Framework Assessment
Data-derived dimensions from STATSWING's analytical frameworks — qualities that the published research demonstrates are structurally invisible to event-based analytics. Available when the data supports it; when absent, weight redistributes to other dimensions.

The statistical dimension alone does not constitute the grade. A player with a high statistical percentile but low pattern convergence and limited framework evidence receives a lower composite than the statistics alone would suggest. The system is designed to avoid false confidence from any single signal.

Position awareness and team context

A grade describes a specific, position-relative assessment — not a universal ranking. The system is designed to make like-for-like comparisons meaningful.

Position-specific peer groups
A centre-back is assessed against centre-backs. A goalkeeper against goalkeepers. The statistical dimension uses position-specific metrics — the qualities that define an elite centre-back are not the same as those that define an elite winger. Dimension weights also vary by position.
League adjustment
All statistics are normalised to a common reference before percentile ranking. This means a player in the Eredivisie is compared on an adjusted basis to a player in the Premier League. Grades for players in non-Big-5 leagues carry a confidence cap (see Section 04) because league adjustment coefficients outside the top five are estimated, not empirically derived.
Team context
Individual statistics in football are mediated by team context — a defender on a possession-dominant side will have structurally different per-90 profiles than an equally capable defender on a low-block side. The pattern intelligence dimension partially addresses this through multi-signal convergence rather than isolated metrics. The framework dimension incorporates qualities that are not captured by event-based statistics. Neither fully resolves the team-context confound; the confidence band reflects this uncertainty.
Multi-season smoothing
Statistical inputs are smoothed across seasons with a recency weighting. This reduces the influence of outlier seasons — both positive and negative — and increases the stability of assessments for players with sustained track records.

How certain is the grade, and which direction is the player moving

Confidence Bands
HIGH Comprehensive multi-season data, multiple providers, empirical league calibration
MED Adequate data with some gaps, or non-Big-5 league (estimated league adjustment)
LOW Limited data — the grade is provisional and should be interpreted with caution
Factors that determine confidence
Minutes played · Seasons covered · Data providers
League calibration quality · Signal reliability
Trajectory

Trajectory describes the direction of a player's performance relative to what would be expected for their age and position. A 33-year-old centre-forward holding steady is a different signal than a 22-year-old doing the same.

Improving
Stable
Declining

Trajectory compares observed season-over-season change to the expected change from age-curve models, adjusted for league difficulty changes. It requires a minimum of two consecutive seasons of data.

Does the system identify quality before the market prices it in?

The grade system was retrodiction-tested by applying the current model to historical data across nine seasons. This tests whether the grades, as currently computed, would have distinguished quality when applied to the past.

Retrodiction Evidence
3.11×
Top-tier assessments produce 3.11× the following-season per-90 output of the lowest tier — and the separation holds when each position is measured on its own terms. All metrics are per-90 rates with a 450-minute minimum qualification.
15,975 player-grades · 10,744 outcomes · 9 seasons
Position-Specific Tier Separation · SW-1 vs SW-6 · Following Season
CFxG/902.94× WAxG/903.00× AMxA/902.33× CMkey passes/902.39× CBtackles + passes/901.53× FBtackles + int/901.39×
Monotonic in 6 of 7 positions. CDM excluded (sparse SW-1 sample).
Grades computed retroactively from historical data using current model coefficients. Per-90 rate metrics, 450+ minutes qualification. This is a backtesting validation, not a real-time prediction.

What a grade is and is not

A STATSWING grade answers one question: how good is this player relative to position peers, adjusted for league context, with what confidence? It is a present-tense assessment of current quality in context. It does not answer several adjacent questions that it may appear to answer.

Current scope

18,000+
Players Graded
20+
Leagues
4
Dimensions
9
Seasons Validated

The assessment model is periodically recalibrated as additional empirical data becomes available. Dimension weights are derived from regression against validated outcomes, not editorial assignment. When the model is recalibrated, STATSWING publishes a research note documenting what changed and why. Published research examining the measurement conventions and frameworks behind these assessments is available at statswing.com/research.

Current assessment model effective March 2026 · Model reference: AGS-1.0