Every completed signing represents a positive decision by the club. SigningLab evaluates those same signings independently and indicates which should be recommended and which should be avoided. While clubs average roughly 36% success on the signings they make, SigningLab reaches about 81% accuracy evaluating those same signings. Results are published every season, while the model’s internal architecture remains proprietary.
While clubs get, on average, roughly 36% of the signings they make right, SigningLab reaches about 81% accuracy when evaluating those same signings — identifying both the ones with good prospects and the ones that should be avoided. The strongest point of the methodology: SigningLab did not choose the players evaluated. The study covers the signings clubs actually made — not the players its technology rated highest — so the 81% does not come from selecting favorable cases. Full definitions and protocol are on the verification page; the per-league table is below.
| League | 2024 | 2025 | SigningLab Model, period |
|---|---|---|---|
| Saudi Pro League | 90% | 88% | 89% |
| Primera A (Colombia) | 90% | 86% | 88% |
| Liga MX (Mexico) | 86% | 84% | 85% |
| Liga 1 (Peru) | 85% | 85% | 85% |
| Super Lig (Turkey) | 83% | 86% | 84% |
| Primera Division (Chile) | 85% | 83% | 84% |
| Brasileirao Serie B | 84% | 84% | 84% |
| Serie A (Italy) | 86% | 81% | 83% |
| Liga Profesional (Argentina) | 85% | 81% | 83% |
| Bundesliga | 81% | 81% | 81% |
| Premier League | 82% | 80% | 81% |
| Brasileirao Serie A | 80% | 81% | 80% |
| J1 League (Japan) | 79% | 81% | 80% |
| La Liga (Spain) | 79% | 81% | 80% |
| Eredivisie (Netherlands) | 82% | 78% | 80% |
| Ligue 1 (France) | 80% | 79% | 80% |
| Primeira Liga (Portugal) | 76% | 82% | 79% |
| MLS (USA) | 78% | 78% | 78% |
| Championship (England, 2nd tier) | 75% | 80% | 78% |
| Pro League (Belgium) | 78% | 76% | 77% |
| Premiership (Scotland) | 74% | 75% | 74% |
Percentages rounded. The figure reported corresponds to SigningLab’s validated accuracy evaluating the signings clubs made in each league and period. The recommendation is always binary; the later outcome has three classes, and neutral is a tolerance band compatible with either signal. No model is correct every time; the commitment is reduced uncertainty and a record that can be audited.
A signing is counted 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 level expected count as neutral; clear underperformance counts as a miss. The same definition is applied across leagues and seasons so the comparison is consistent.
Every projection is recorded and time-stamped before the outcome is known, so there is no future data and no hindsight. Validation is out-of-sample and walk-forward: the model is trained only on seasons before the one it is scored on, and each competition is calibrated on its own terms. The record is published season after season, whether the result is favorable or not.
How to read the main figures: the roughly 36% is clubs’ average hit rate on the signings they make. The roughly 81% is SigningLab’s accuracy evaluating those same signings and indicating which should be recommended or avoided. Both are hit-rate indicators under the published criteria of the analysis.
Last updated: 2026. Source: SigningLab annual validation. Machine-readable league data at facts.html and rankings.json.