The Playermetry Story
The hunt for hidden talent
My name is Marian Vanca. I’ve worked as a software developer for over thirteen years and I have a Computer Science degree and a master’s in Artificial Intelligence and Distributed Computing at the West University of Timișoara. My job has always been to take a messy problem and ask what the data underneath it actually says.
Away from work, football has been the constant. At some point the two started to overlap, helped along by an idea I’d picked up from Moneyball: that there’s value hiding in places the market has stopped paying attention to. The book is about baseball, but the thought fits football just as well, especially the corners of it nobody bothers to measure. And one question kept coming back to me: why do even some of the smartest, richest clubs get so many signings wrong?
The transfer coin flip
The answer is less flattering than clubs would like to admit. In How to Win the Premier League, former Liverpool Director of Research Ian Graham puts numbers on it: across 1992 to 2021, around 46% of €10million-plus transfers in the top leagues failed outright. Among the 100 biggest transfers in the “big five” leagues over that span, more than half didn’t work.
These were the best-funded decisions in the sport, and for years they landed barely better than a coin toss, with tens of millions on the line each time.
The hopeful part is that the top clubs have improved the success rate since. Optical tracking, expected-goals models and proper analytics departments genuinely improved how the biggest clubs recruit, and Graham’s own work was part of that change. The data did what it was supposed to do.
Which is exactly what got me thinking: if better data cleaned things up that much at the very top, what’s still happening everywhere the data never arrived?
The data black hole
I didn’t have to look far, though it took me a while to recognise what I was looking at.
I’d assumed bad signings were mainly a top-level story, the occasional huge flop that makes the news. Following football lower down the pyramid, I started seeing the same thing constantly, just more quietly. It wasn’t only the clubs fighting relegation. Sides built to go up would sign players, watch them fade, and rebuild half the squad the next summer or even the next transfer window. The same costly guesswork, repeated at every level, year after year.
My hometown club, Olimpia Satu Mare in the Romanian third tier, was what made me actually pay attention. Not because they were doing anything unusual. The opposite. They were living the same reality as almost every club at that level: big decisions made with almost nothing solid to base them on. Once I’d noticed it there, I saw it everywhere.
The cause never changed. In the lower and emerging leagues that make up most of professional football, the advanced data isn’t there. No xG, no tracking, no event feeds. Just the match sheet. Who played, who scored, who got sent off, the final result. For thousands of leagues and hundreds of thousands of careers, that’s the whole record.
And clubs still have to bet their season on it every window. With nothing objective to lean on, they go off instinct, a few highlight clips and whatever an agent is pushing. At that level the room for error is tiny, and one bad window can cost a club its momentum, its budget, sometimes more than that.
The idea
There wasn’t one big moment when it clicked. It was more that something I’d wanted for a long time finally turned into a plan.
It probably started with Moneyball years before: the idea that the underdog who spots overlooked value first can compete with budgets far bigger than its own. Underneath all of it, what I actually wanted was to give the smaller clubs a real shot at climbing. Graham’s book didn’t start that idea so much as back it up. If good data improved recruitment that much at the top, then the leagues still running on nothing but a match sheet were sitting on the same advantage, untouched.
So the starting point became obvious. If the advanced data doesn’t exist, stop waiting for it.
There’s one fact recorded in every league on earth, however small: what happened to the score while a given player was on the pitch. Basketball and ice hockey have judged players this way for decades, not by their personal stats but by how much better the team does with them on the floor. Football had barely used the idea at this level.
A raw version of it would be useless, though, because in football the context is everything. Taking a point off the league leaders is nothing like beating the bottom side. Conceding to a weak team should cost more than losing to a strong one. A number that ignored all that would do more harm than good.
The hard part, and the part that took years of building and testing, was teaching the system to read that context, and to do something a league table can’t: put a player from one competition on the exact same scale as a player from another, anywhere in the world. That’s Playermetry. A way for an ambitious club to find players who actually make a difference, usually in places no one else is looking, and often for a lot less than a more familiar name would cost.
A tool built for the underdog
Playermetry is built around one question: did this player make his team better, once you account for how good the opposition and his own team-mates were? The answer is the Playermetry Rating: one number, on one scale, that means the same thing whether it comes from the Premier League or a regional third division.
It’s not meant to replace scouts. It’s meant to point them in the right direction, save the weeks that get lost watching the wrong players, and prevent the kind of expensive mistake a smaller club can’t really absorb.
The mission
The aim is simple, and I mean it. Make scouting fairer. Give ambitious clubs, not just wealthy ones, the kind of objective read on players that used to belong only to the elite, across hundreds of leagues most people have written off as impossible to measure.
Good players turn up everywhere, including the leagues nobody is watching. They’ve always been there. The only thing missing was a way to find them.
If you believe scouting should be smarter in the leagues where advanced metrics are unavailable, welcome to Playermetry.
Start by taking a look at a single report on any player you want, and see what the impact data tells you that a highlight reel never will.