We help clubs to
earn more money.
To maximize the money they spend and convert it into wins, more money, or both.
We help clubs to be efficient.
Our scientifically grounded models find the combination of player assets that maximises sporting output, financial return or both for the club’s available capital and risk.
95.3% of clubs are not operating efficiently.
Clubs building with SoccerSolver.
MIT Sloan Sports
Analytics Conference
Selected among the Top 8 sports science startups.
Alejandro Sánchez, CEO of SoccerSolver, presented SoccerSolver at MIT SSAC, where football, science and investment meet.
Watch the presentation ↗
€850K raised. Before revenue.
A €700K pre-seed plus €150K family-and-friends round, closed before the company had revenue or announced clubs. The market launch begins in 2026.
Read articleInvest more. Grow more.
In 2017, Florentino Pérez described the club as a company and its players as its defining financial assets. SoccerSolver turns that thesis into an operating system.
“Treat a sports club like a company. And this club’s assets are not machines. They are players.”
Players are sporting talent. Players are financial assets.
SoccerSolver evaluates each player as an asset through more than 50 financial metrics, while preserving the sporting context that makes every club different.
K. Mbappé Detected 2016 
E. Haaland Detected 2019 
M. Olise Detected 2020 
J. Musiala Detected 2019 
A. Isak Detected 2019 
C. Palmer Detected 2021 
M. Caicedo Detected 2020 
J. Neves Detected 2022 
E. Camavinga Detected 2019 
F. López Detected 2023 
A. Tchouaméni Detected 2019 
D. Doué Detected 2022 “He scored five goals. He looks good. Let’s sign him.”
Observation without a quantified investment thesis.
“+25% expected ROI. 32% downside volatility. Positive return at 90% confidence.”
A sporting decision translated into risk, return and timing.
The market moves. Know before it does.
SoccerSolver reads player assets like a market: expected appreciation, depreciation and one-year value, continuously updated around the club’s context.
Pedri €165.7M 1Y expected 
Vitinha €144.9M 1Y expected 
K. Kvaratskhelia €143.3M 1Y expected D. Doué €135.7M 1Y expected 
O. Dembélé €100.1M 1Y expected 
D. Szoboszlai €105.3M 1Y expected F. López €113.9M 1Y expected 
W. Saliba €95.6M 1Y expected C. Palmer €78.7M 1Y expected J. Musiala €95.1M 1Y expected 
F. Wirtz €97.3M 1Y expected 
E. Fernández €95.1M 1Y expected 
P. Cubarsí €84.8M 1Y expected 
F. Valverde €87.9M 1Y expected SoccerSolver product snapshot · Model outputs are probabilistic, not guarantees of future value.
From club without SoccerSolver to club with SoccerSolver.
This is a club’s financial history across multiple seasons, and the projected gap created when investment decisions are optimised by SoccerSolver.
Illustrative, anonymised model output. Forecasts are probabilistic, depend on context and execution, and are not guaranteed financial results.
The price of promotion. The cost of relegation.
SoccerSolver connects squad value with the points and probabilities required to protect the downside or reach the upside.
Product screenshots. Probabilities change with the selected market value, season assumptions and competitive context; they do not guarantee sporting outcomes.
The xG for your career. The xV for your value.
Owners speak money. xV turns an entire sporting-director career into evidence of the financial value created.
Real Betis*
Getafe CF*
Tottenham*
RCD Espanyol*
Leeds United*
Sevilla FC*
Real Valladolid*
Al-Ittihad*
Deportivo Alavés*
Elche CF*
Middlesbrough*
Racing Santander*
Rayo Vallecano* * The crests represent the club environments contained in those directors’ professional portfolios, not necessarily current club employment.
Every product starts with a scientific paper.
SoccerSolver developed 15 research projects. 3 became MIT semifinalists. This year, our quantitative team will submit more than 10 new papers.
Redefining Scouting Intelligence
A quantitative framework for player similarity and tactical fit in football.
Read on ResearchGate ↗
The Football Efficient Frontier
Balancing profitability and performance in squad optimization.
Read on ResearchGate ↗
The OI Model
A multi-criteria utility framework for wage-value efficiency in football.
Read on ResearchGate ↗
