SigningLab studies how well football clubs turn signings into performance. This page explains, in plain terms, what the signing-efficiency rankings measure and what the public data shows across leagues and seasons. The figures on this page are public market findings. The SigningLab model's own validated accuracy is published as well, summarized in the note below; what stays proprietary is the model's internal architecture and features, not the results.
The transfer market is one of the largest capital allocations in professional sport, and one of the least independently audited. The public data makes the scale of the problem visible: most signings do not return what was expected of them. That is not a verdict on any single club or player; it is the base rate the whole market operates within.
The signing-efficiency ranking asks a simple question that fans and directors both care about: which clubs and which leagues sign well. It measures the share of signings that performed at or above the baseline expected for the level of investment, rather than how much was spent.
The percentage of a club's or league's signings that delivered at or above the expected baseline. A higher rate means signings that worked, more often.
A signing is judged against what was expected for its cost and context. Finding undervalued players who outperform counts for more than spending heavily for the same result.
The ranking is released annually to clubs and the sports press, alongside the underlying league dataset, so the findings can be reproduced.
Pooling 2020 to 2025, the gap between leagues is consistent enough to be structural. Where clubs are well organized and competition is more stable, signings convert more reliably. Where rosters turn over heavily, coaching changes are frequent and tactical styles vary widely, even good signings are harder to anticipate.
Over the period, these leagues sit near or above 40 percent.
High turnover, tactical variety and dispute push these toward the lower end.
Values are weighted league averages across seasons 2020 to 2025 and are rounded. A league being "hard" is a comment on the competition, not on any individual club or player within it.
The league figures above are only the starting point. Beyond leagues, SigningLab publishes a club-by-club signing ranking for every competition and every season since 2016, showing how each club converted its signings into performance, signing by signing. Each archive page lists the clubs in a competition for a given year, so the comparison can be read at the level where decisions are actually made.
The full league-by-season table is published openly so journalists, clubs and researchers can reproduce the findings above. It contains league, year, club hit rate and the number of signings evaluated. It does not contain SigningLab's proprietary model outputs.
league, year, club_hit_rate, n_signingsOn accuracy figures: the benchmark above is the market's own hit rate, roughly one in three signings. The SigningLab model reaches a markedly higher, validated hit rate, about 81 percent on average in 2025, and it is more accurate than the clubs in every league we track. These accuracy results are published; what stays proprietary is the model's internal architecture, its features and their weighting, not the results. SigningLab publishes prediction against reality each season, whether the result is favorable or not.