The problem of 7-in-10 failed signings — and what the data says about why
Analysis of the structural causes of signing failures in professional football, based on the 2018-2023 windows across the top five markets.
READ ANALYSIS →SigningLab turns historical data, competitive context and predictive modeling into an objective probability of success for every player your club evaluates — before the cheque is signed.
The transfer market is one of the largest capital allocations in professional sport — and one of the least audited. Decisions worth tens of millions are made based on video, opinion and personal relationships, with little independent statistical rigor.
SigningLab is not a scouting agency, a broker, or a betting platform. It is a decision lab: a team that combines machine learning, historical performance data, competitive context and public validation to answer a simple question — "does this signing have a real chance of working here?".
We don't promise perfect prediction. We promise a better hit rate than the status quo — and we prove it publicly, season after season.
The reason our hit rate beats the market is structural, not magic. Four principles separate a SigningLab projection from a gut feeling.
Where the industry reads ten or twenty indicators, our proprietary model cross-references hundreds — drawn from more than five thousand underlying variables, including behavioral, contextual and environmental signals that standard scouting software never captures.
Every projection is time-stamped and logged before the outcome is known — zero future data, no hindsight. The forecast is auditable by third parties, so the hit rate cannot be retrofitted.
A signing is never judged in a vacuum. Squad, coaching staff, playing style, competitive level and specific needs reshape the probability for each destination club — because fit, not talent alone, decides whether a signing works.
We don't claim perfect prediction. We deliver a markedly higher hit rate than the market's ~34% — and we publish prediction versus reality every season, favorable or not, for anyone to audit.
Football is the most popular sport on the planet precisely because it escapes complete predictability. Talent, instinct, grit and context meet on the pitch in ways no mathematical model will ever fully translate — and that is a virtue no dataset should erase.
We believe, above all, that talent comes first. Brilliant signings are born from exceptional players, and no software will replace the trained eye of experienced scouts and coaches. But in a market where roughly seven in ten signings fail to deliver the expected ROI within twenty-four months, it is the duty of any professional club to add the best analytical intelligence available alongside the human eye.
SigningLab exists to reduce uncertainty, not to eliminate it. We identify potential and probability of success before irreversible decisions are made — combining structured data the market already recognizes with behavioral, contextual, environmental and biological variables that no standard scouting software captures. Where the industry looks at ten or twenty indicators, our proprietary model cross-references hundreds, drawn from more than five thousand underlying variables.
We work with over two million matches and more than four hundred thousand transfers mapped across global football. We combine decades of research in statistics, machine learning and neural networks with frontier artificial intelligence models. It is at this intersection — standardized structured data plus behavioral, psychological and contextual signals that have only recently become systematically measurable — that our edge lives.
We draw inspiration from American sports management: baseball's Moneyball, the NBA's multidimensional analytics, the NFL's data-driven front offices. Professionalizing football is, above all, a question of institutional ROI — and professional clubs deserve an analytical layer comparable to the most mature American sports.
We stand openly against signing decisions based on internal politics, relationship-driven recommendations, opaque commissions or influence. Every dollar misspent on a wrong signing is money diverted away from the academy, the infrastructure or the community of the club. The football we defend is meritocratic on the pitch — and professional in management.
That is why we openly publish annual rankings of signing efficiency. Not to embarrass. To promote sector-wide capability, recognize clubs and professionals that apply rigorous methodology, and elevate the technical debate around what actually works. When a club consistently gets it right, the market needs to see it. When another systematically gets it wrong, it needs to look inward. This is the most honest — and most useful — way to raise the collective standard.
We are open to clubs, coaching staff, agencies, agents and the sports press. Our work only matters if it reaches the pitch, the decision room and the public debate.
Every SigningLab analysis follows the same dimensions — so directors, coaches and CEOs can read different reports with one shared grammar.
Overall signing rating. Combines expected success, sporting fit, risk, value-for-money and feasibility into a single number.
Probability the player will deliver performance above the baseline expected for the investment made.
Match between player and specific club — profile, playing style, competitive context and squad needs.
Real feasibility of the deal — contract, career moment, expected wages, and seller club's openness.
Risk classification in four levels: Low, Moderate, High, Critical — covering injury, adaptation, and performance decline.
Value-for-money and resale upside. Identifies undervalued players likely to outperform pricier signings.
Estimated fair value for the negotiation — based on recent comparables adjusted by age, contract and expected performance.
Historical hit rate of the model, validated and published every season. A metric of transparency and credibility.
Seven product lines across analysis, athlete development and platform — plus open annual releases — covering transfer-window decisions, squad management and individual development for clubs, coaching staff and agents who want to reduce uncertainty and maximize return.
For clubs that need objective evidence before signing.
On-demand analysis of specific players. Complete dossier with success probability, squad fit, competitive context, risk and fair value. Ideal for clubs in active negotiation.
Active recommendation engagement on an annual contract: SigningLab maps the market for your club and recommends players aligned with technical profile, financial constraints and squad needs. Includes opportunity rankings and undervalued names.
Loan evaluation per window: identifies destinations with the highest development probability for academy or squad players, and recommends loan-in candidates with strong fit to the project.
Three versions of the same proprietary methodology — more than six hundred attributes assessed across five thousand-plus technical, physical, biological, psychological and environmental variables. Ten-month on-site programs with individualized development plans.
Ten-month immersion within the club's academy. Continuous evaluation of U-17 to U-20 athletes across all dimensions — technique, tactics, set pieces, 24-hour life, nutrition, supplementation, lab work and DNA, body composition and applied fitness. Individual development plan, with a growing proprietary calibration base.
Continuous accompaniment of key squad athletes: rising prospects, high-cost signings, post-injury return cases, veterans in maintenance. Focus on performance optimization, injury risk reduction, career extension and market value maximization.
Ten-month individual program with five bimonthly checkpoints. Client is the agent or intermediary: 360° assessment, development plan, prescribed training with video, and the SigningLab app for real-time tracking between agent and athlete. Private program — data is not shared with the club.
Self-serve access to SigningLab intelligence — explore, compare and track signings without waiting for a report.
Dashboard to filter by position, league and profile, rank opportunities, compare athletes side by side and follow prediction-versus-reality over time. The same proprietary metrics from our reports, on demand. Open the app →
Our public commitment to validate and disclose results — and to elevate the technical debate about what actually works in athletic signings.
Ranking of clubs by signing hit rate, financial efficiency and deviation between predicted and actual outcomes. Released annually to sports press and clubs — to recognize those who apply methodology and broaden the sector debate.
Out-of-sample validation: timestamped predictions compared with actual outcomes. Hit rate, false positives, false negatives and comparison against market baseline — auditable by third parties.
Cross-league comparison of how well clubs sign — average signing hit rate per league, revealing where the transfer market is strongest and weakest. A public benchmark for press and clubs.
SigningLab publishes annual rankings of signing efficiency — a public benchmark that recognizes clubs and professionals who apply rigorous methodology, and elevates the technical debate across global football.
Worldwide cross-league ranking. Every club covered by SigningLab is evaluated by signing hit rate, financial efficiency and prediction–outcome deviation. The single, unified scoreboard for clubs that take signing seriously.
Individual ranking per league, comparing only clubs that share the same competitive context. Each report is released both in English and in the league's primary local language, for the local press and clubs.
SigningLab structures every analysis on an auditable methodology — traceable, reproducible and publicly validated in retrospect.
Over two million matches and more than four hundred thousand transfers mapped across global football, including performance, market evolution, injuries, valuation history and the actual outcome of each signing. The past is our laboratory.
Every analysis incorporates the destination club's context — squad, coaching staff, infrastructure, playing style, competitive level and specific needs. The same signing can carry very different probabilities at different clubs.
Every prediction is recorded before the outcome is known. Recent seasons are our out-of-sample validation, and results are openly published — no cherry-picking, with third-party-auditable hit rates.
Our transparency commitment sustains methodological credibility and raises the collective standard of the market. We publish annual rankings of club signing efficiency — to recognize methodology and broaden the debate around what actually works.
Every public forecast is recorded with a cryptographic hash in an open repository before the outcome is known — eliminating any suspicion of post-hoc adjustment.
End-of-season publication with hit rate, false positives, false negatives and comparison against market baseline — auditable by journalists and researchers.
Annual ranking of clubs by signing hit rate. To recognize professionals who apply rigorous methodology, promote sector-wide capability, and elevate the technical debate across global football.
Validation datasets are made available for auditing by journalists, researchers and clubs interested in verifying the methodology.
We publish what's behind the decision — from the statistics to the systematic mistakes of the transfer market.
Analysis of the structural causes of signing failures in professional football, based on the 2018-2023 windows across the top five markets.
READ ANALYSIS →How the same signing can carry vastly different probabilities depending on the destination club — and why this changes how to read the report.
COMING SOONFirst public ranking of signing efficiency, based on out-of-sample validation of our model in the previous season.
COMING SOONWe are open to professional and academy clubs, agents, coaching staff and sports press. A limited number of pilot engagements available for the 2026/27 season with special terms.
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