Player-level Elo ratings for professional football
The only system rating 78K+ individual players and 6,469 coaches across 176 competitions worldwide.
Why this exists
A passion project. Built to be free. Running on coffee and a small server bill.
1. It started with the games
Every season I want to win at Voetbalpoules and Superbru. Picking Manchester City over Bournemouth is the easy part — the hard part is the exact scoreline, and gut feeling only gets you so far. I'd rather let data and analytics drive those calls, the way Pouletips does for its pool tips. I wanted a model that could answer that for any match, in any league, at any moment.
2. And with a fascination for ratings models
Privately and professionally I've been hooked on rating systems for years: Goalimpact, SciSports' SciSkill, the Glicko ratings chess uses, and the wonderful clubelo.com and worldratings.net for team-level Elo. They're all brilliant in their own way. None of them rated individual players and coaches in a form I could plug straight into a fantasy line-up.
3. So I built one
Combining the practical itch with the model fascination produced PlayerElo: a player-level Elo rating system that also rates coaches. Every starting eleven, every substitution, every new manager — all of it changes a team's strength the moment lineups are confirmed. Today the model tracks 78,102 players and 6,469 coaches across 176 competitions, and updates every day after the matches finish.
How it works, in plain English
Every player has their own Elo rating, and so does every coach. When two teams meet, we look at the actual lineup — who starts, who's on the bench, who's in the dugout — and weight each one by how much their position influences the result. Attackers carry the most weight. The coach, midfielders, defenders, goalkeepers and bench players each get smaller shares. That gives a team strength for that specific match. Two team strengths plus a per-league home advantage produce a probability for home win, draw, and away win.
After the final whistle every player's Elo moves up or down based on the gap between prediction and result — scaled by minutes played, margin of victory, and how big an upset it was. Big upsets shift ratings more than expected wins. Players age out gradually too, with attackers peaking around 24 and goalkeepers around 28, so the model never gets stuck rating someone for who they were five years ago.
A happy accident: Elo Above Replacement
Somewhere along the way I stumbled into a useful by-product: tracking how many Elo points each player adds beyond what an average player in their position would have generated. Elo Above Replacement (EAR). Only later did I realise this is essentially football's version of baseball's Wins Above Replacement (WAR) — same idea, different sport.
EAR doesn't just sharpen the predictions. Week after week it shows you which player is genuinely lifting his team above the level of his teammates — the kind of signal you can't see in goals or assists alone. The highest career EARs in football belong to exactly the names you'd hope they would — Messi, Ronaldo, Kimmich. The strongest sanity check this model has ever passed.
The discovery
football-data.co.uk is the best free archive of bookmaker closing odds anywhere on the internet. I started checking my predictions against Pinnacle Closing — the sharpest line in football. Across the 12 top European leagues my model scored a Brier of 0.576 against Pinnacle's 0.572: a four basis-point gap on the same matches. Close enough that I started to suspect this thing might actually be value-positive against bookmakers.
So I built this site to find out. Everything you see here is free and stays free — no paywalls, no email gates, no “upgrade to see more”. The rankings, the live predictions, the value bets, the historical track record: all public. If you'd like the betting tips by email the moment lineups confirm, fill in the form on the betting page. If you'd like to keep the lights on (about €30 per month of API + server bills), donations live on the support page.
Top 10 Global Players
View full rankings →| # | Name | Pos | Team | Elo |
|---|---|---|---|---|
| 1 | Pedri | MID | Barcelona | 2626.4 |
| 2 | Matheus Nunes | MID | Manchester City | 2626.0 |
| 3 | E. Haaland | ATT | Manchester City | 2596.1 |
| 4 | Raphinha | MID | Barcelona | 2555.9 |
| 5 | David Raya | GK | Arsenal | 2552.0 |
| 6 | D. Upamecano | DEF | Bayern München | 2550.7 |
| 7 | L. Díaz | MID | Bayern München | 2547.3 |
| 8 | Lamine Yamal | ATT | Barcelona | 2545.4 |
| 9 | Pau Cubarsí Paredes | DEF | Barcelona | 2541.5 |
| 10 | J. Gvardiol | DEF | Manchester City | 2534.7 |
Top 5 Coaches
View all coaches →| # | Name | Team | Elo |
|---|---|---|---|
| 1 | Pep Guardiola | Manchester City | 2514.4 |
| 2 | Vincent Kompany | Bayern München | 2496.6 |
| 3 | Mikel Arteta | Arsenal | 2477.6 |
| 4 | Hansi Flick | Barcelona | 2459.7 |
| 5 | Simone Inzaghi | Al-Hilal Saudi FC | 2455.1 |