Every SigningLab analysis follows the same dimensions, so directors, coaches and executives read different reports with a single grammar. This page defines each metric and separates the public index from the proprietary model.
The overall grade for a signing. It combines expected success, squad fit, risk, value for money and deal viability into a single number.
The probability that the player delivers performance at or above the baseline expected for the level of investment.
How well the player matches a specific destination club: profile, playing style, competitive context and squad needs. Fit, not talent alone, decides whether a signing works.
Expected availability across a season, reading injury history and physical profile. A player only returns value on the pitch.
A four-level rating (Low, Moderate, High, Critical) covering injury risk, adaptation risk and the risk of a performance drop.
Cost-benefit and resale potential. It identifies undervalued players with a chance to outperform more expensive signings.
An estimated fair price for the deal, based on recent comparables adjusted for age, contract and expected performance.
The model's historical hit rate, validated and published each season. A transparency and credibility metric. See accuracy.
A signing counts as a success when the player performed at or above the baseline expected for the level of investment, read on minutes, performance, availability and collective contribution. Outcomes at the expected level count as neutral; clear underperformance counts as a miss. The same definition is applied across leagues and seasons.
The share of all signings in a league that worked, with no model involved. Roughly one in three across the leagues we track.
The same measure for a single club, used in the public rankings to compare how well clubs convert signings into performance.
A retrospective, public evaluation index used in the annual reports. A simplified, transparent version meant for general understanding, not the commercial model.
The validated, ex-ante hit rate of the SigningLab predictive model, about 81 percent on average in 2025. The commercial models are distinct from the public index, and their internal architecture, features and weighting are not disclosed.