Ballon d'Or 2026

How the score is built

The ranking is a calculation, not a poll, so all of it can be shown. This page is the whole method: the three criteria France Football votes on, every rule inside them, the published figure each rule reads, and the arithmetic that turns it into a score out of 100.

The award names three pillars, in order of importance: individual performance, team achievements, and class combined with fair play. The default weights descend in that same order — 50, 30 and 20 — and the sliders on the ranking let you disagree.

The figures below describe the default ranking — every rule on, criteria at 50 / 30 / 20 — so this page reads the same for everyone. On the ranking itself you can switch any rule off and move the weights, and the score rebuilds around what is left.

The calculation, in four steps

1. Each rule is placed against the field. A rule's raw figure is rescaled so the best nominee on that rule scores 100 and the weakest scores 0. Goals and cards therefore count on the same scale, whatever their units.
2. Rules are weighted inside their criterion. Club honours count for more than league finish, non-penalty goals for more than availability.
3. Criteria are weighted against each other. Currently individual performance 50.0%, team achievements 30.0%, class & fair play 20.0%. The three sliders always add up to 100, and our own split is 50 / 30 / 20.
4. Switching rules off rebalances both levels. The remaining rules share out the weight the disabled ones gave up, so the score stays on 0–100 and stays comparable. Turn off every rule in a criterion and its share goes to the others.

Every rule, in full

Each rule reads one figure per nominee. The figure is rescaled so the best in this field scores 100 and the weakest 0, weighted inside its criterion, and the criteria are weighted against each other. Everything below is live: the examples come from the thirty nominees currently ranked.

Individual performance 50.0% of the score

Decisive goals per 90 8.98%

What it measures: Goals in Champions League knockout ties, finals and other matches that settled something, per ninety minutes. It asks how reliably a player turns up in the matches that decide a season rather than how many such goals they happened to accumulate.
Where the data comes from: API-Football, derived from the goals scored in knockout fixtures inside the award window
What is read: Goals in knockout ties, and games.minutes
How it is calculated: decisive goals ÷ (minutes ÷ 90) measured in per 90
Example: Ousmane Dembélé scores best on 0.28 per 90 and takes 100 here; Willian Pacho is weakest on 0 per 90 and takes 0. Everyone else sits between them.

Goals per 90 7.78%

What it measures: Every goal, penalties included, per ninety minutes. A penalty counts on the scoreboard, so it counts here. If you think a regular spot-kick taker is flattered by that, the two non-penalty rules below take penalties back out, and you can weight them up or switch this one off.
Where the data comes from: API-Football, players endpoint, summed over every competition inside the award window
What is read: goals.total and games.minutes
How it is calculated: goals ÷ (minutes ÷ 90) measured in per 90
Example: Harry Kane scores best on 1.28 per 90 and takes 100 here; Pau Cubarsí is weakest on 0.02 per 90 and takes 0. Everyone else sits between them.

Non-penalty goals per 90 6.59%

What it measures: Goals from open play and set pieces per ninety minutes. It asks how often a player scores rather than how many they finished with, and takes penalties out so a regular spot-kick taker is not flattered.
Where the data comes from: API-Football, players endpoint, summed over every competition inside the award window
What is read: goals.total, penalty.scored and games.minutes
How it is calculated: (goals − penalties) ÷ (minutes ÷ 90) measured in per 90
Example: Julián Quiñones scores best on 0.97 per 90 and takes 100 here; Pau Cubarsí is weakest on 0.02 per 90 and takes 0. Everyone else sits between them.

Goal involvement per 90 5.39%

What it measures: Goals and assists per ninety minutes, so a player who produced the same numbers in far less football is not overlooked.
Where the data comes from: API-Football, players endpoint, summed over every competition inside the award window
What is read: goals.total, goals.assists and games.minutes
How it is calculated: (goals + assists) ÷ (minutes ÷ 90) measured in per 90
Example: Lionel Messi scores best on 1.54 per 90 and takes 100 here; Pau Cubarsí is weakest on 0.02 per 90 and takes 0. Everyone else sits between them.

Assists per 90 4.19%

What it measures: Passes that directly produced a goal, per ninety minutes. It asks how often a player creates rather than how many they finished the season with, which matters most for a creator who missed part of it.
Where the data comes from: API-Football, players endpoint, summed over every competition inside the award window
What is read: goals.assists and games.minutes
How it is calculated: assists ÷ (minutes ÷ 90) measured in per 90
Example: Michael Olise scores best on 0.64 per 90 and takes 100 here; Marquinhos is weakest on 0 per 90 and takes 0. Everyone else sits between them.

Pass accuracy 3.44%

What it measures: The share of a player's passes that reached a team-mate. Compared only against others in the same position, because a centre-back recycling possession and a winger trying to split a defence are not attempting the same pass.
Where the data comes from: Sportmonks, accurate passes over total passes across the award window
What is read: Accurate Passes and Passes
How it is calculated: accurate passes ÷ passes measured in per cent
Example: Marquinhos scores best on 136.88 per cent and takes 100 here; Sadio Mané is weakest on 40.35 per cent and takes 0. Everyone else sits between them.

Big chances created per 90 3.29%

What it measures: Clear scoring opportunities handed to a team-mate, per ninety minutes, counted whether or not they were finished. An assist depends on someone else putting the ball in; this does not, so it measures the creator rather than the finisher in front of them.
Where the data comes from: Sportmonks, summed over every competition inside the award window
What is read: Big Chances Created and Minutes Played
How it is calculated: big chances created ÷ (minutes ÷ 90) measured in per 90
Example: Lamine Yamal scores best on 1.06 per 90 and takes 100 here; Pau Cubarsí is weakest on 0.02 per 90 and takes 0. Everyone else sits between them.

Defensive actions per 90 2.99%

What it measures: Tackles, interceptions and clearances per ninety minutes, and saves for goalkeepers. Attacking players score low here by the nature of the job.
Where the data comes from: API-Football, players endpoint, summed over every competition inside the award window
What is read: tackles.total, tackles.interceptions, tackles.blocks and goals.saves. API-Football carries no clearances
How it is calculated: (tackles + interceptions + blocks + saves) ÷ (minutes ÷ 90) measured in per 90
Example: Dayot Upamecano scores best on 4.32 per 90 and takes 100 here; Kylian Mbappé is weakest on 0.21 per 90 and takes 0. Everyone else sits between them.

Clearances per 90 2.84%

What it measures: Balls cleared from danger, per ninety minutes. The most common defensive action there is, and one the other defensive rule could never count because API-Football does not carry it.
Where the data comes from: Sportmonks, summed over every competition inside the award window
What is read: Clearances and Minutes Played
How it is calculated: clearances ÷ (minutes ÷ 90) measured in per 90
Example: Gabriel Magalhães scores best on 6.21 per 90 and takes 100 here; Lionel Messi is weakest on 0.02 per 90 and takes 0. Everyone else sits between them.

Possession lost per 90 counts in reverse 2.69%

What it measures: Times a player was dispossessed while carrying the ball, per ninety minutes. Fewer is better, so this rule counts in reverse. It is weighted lightly on purpose: carrying the ball into trouble is part of a dribbler's job, so a heavy version of this would punish the players it is not aimed at.
Where the data comes from: Sportmonks, summed over every competition inside the award window
What is read: Dispossessed and Minutes Played
How it is calculated: dispossessions ÷ (minutes ÷ 90) measured in per 90
Example: Pau Cubarsí scores best on 0.09 per 90 and takes 100 here; Luis Díaz is weakest on 2.26 per 90 and takes 0. Everyone else sits between them.

Availability 1.80%

What it measures: Minutes played across the season. A player who was fit and picked all year carried more of the load than one who was not.
Where the data comes from: API-Football, players endpoint, summed over every competition inside the award window
What is read: games.minutes
How it is calculated: minutes played measured in minutes
Example: Sadio Mané scores best on 5,370 minutes and takes 100 here; Fabián Ruiz is weakest on 2,478 minutes and takes 0. Everyone else sits between them.

Team achievements 30.0% of the score

Club honours 7.83%

What it measures: Major trophies won with the club this season: the league, the domestic cup and the Champions League.
Where the data comes from: API-Football, derived from final league standings and the Champions League final
What is read: Whether the club finished first, and whether it won the Champions League. The trophies endpoint held nothing for 2025/26, so domestic cups are missed
How it is calculated: league title + Champions League measured in trophies
Example: Khvicha Kvaratskhelia scores best on 2 trophies and takes 100 here; Lionel Messi is weakest on 0 trophies and takes 0. Everyone else sits between them.

International honours 7.83%

What it measures: Trophies won with the national team, the 2026 World Cup among them. It is the rule that most separates a World Cup year from any other.
Where the data comes from: API-Football, derived from the World Cup and Africa Cup of Nations finals
What is read: Who won each final, and whether the nominee appeared in the tournament
How it is calculated: tournaments won measured in trophies
Example: Marc Cucurella scores best on 1 trophies and takes 100 here; Ousmane Dembélé is weakest on 0 trophies and takes 0. Everyone else sits between them.

Champions League run 5.22%

What it measures: How far the club went in Europe, from not qualifying at all through to lifting the trophy.
Where the data comes from: API-Football, derived from the rounds the club actually played
What is read: Every finished Champions League fixture, and who won the final
How it is calculated: 0 absent · 1 league phase · 2 playoff · 3 last 16 · 4 quarter · 5 semi · 6 final · 7 winners measured in stage
Example: Khvicha Kvaratskhelia scores best on 7 stage and takes 100 here; Lionel Messi is weakest on 0 stage and takes 0. Everyone else sits between them.

World Cup run 5.22%

What it measures: How far the player's country went at the World Cup, from not qualifying through to lifting it. Only counts in a World Cup year; in any other season every nominee scores the same and the rule moves nobody.
Where the data comes from: API-Football, derived from the rounds the country actually played
What is read: Every finished World Cup fixture, and who won the final. Credited only to nominees who appeared
How it is calculated: 0 absent · 1 group · 2 last 32 · 3 last 16 · 4 quarter · 5 semi or third · 6 final · 7 winners measured in stage
Example: Marc Cucurella scores best on 7 stage and takes 100 here; Khvicha Kvaratskhelia is weakest on 0 stage and takes 0. Everyone else sits between them.

League finish counts in reverse 3.91%

What it measures: Where the club finished domestically. A lower position is a better one, so this rule counts in reverse.
Where the data comes from: API-Football, standings endpoint
What is read: Final position in the domestic league
How it is calculated: league position, counted in reverse measured in position
Example: Ousmane Dembélé scores best on 1 position and takes 100 here; Marc Cucurella is weakest on 10 position and takes 0. Everyone else sits between them.

Class & fair play 20.0% of the score

Disciplinary record counts in reverse 11.43%

What it measures: Cards per ninety minutes, with a red counting three times a yellow — the same ratio UEFA applies in its own fair play ranking. Fewer cards score higher, so this rule counts in reverse.
Where the data comes from: API-Football, players endpoint, summed over every competition inside the award window
What is read: cards.yellow and cards.red
How it is calculated: (yellows + 3 × reds) ÷ (minutes ÷ 90) measured in cards per 90
Example: Ousmane Dembélé scores best on 0 cards per 90 and takes 100 here; Luis Díaz is weakest on 0.32 cards per 90 and takes 0. Everyone else sits between them.

Fouls committed per 90 counts in reverse 8.57%

What it measures: Fouls conceded per ninety minutes. Fewer fouls score higher, so this rule counts in reverse. Midfielders and defenders foul more than forwards by the nature of the job, which is worth knowing when you read it.
Where the data comes from: API-Football, players endpoint, summed over every competition inside the award window
What is read: fouls.committed and games.minutes
How it is calculated: fouls ÷ (minutes ÷ 90) measured in per 90
Example: Ousmane Dembélé scores best on 0.28 per 90 and takes 100 here; Lautaro Martínez is weakest on 1.92 per 90 and takes 0. Everyone else sits between them.

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