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Understanding Machine Football's Attribute Scores

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Fergus Sowery

February 3, 2026

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Comparisons

Machine Learning

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Within our model, we rank both teams and individual outfield players across three core areas of performance: defensive, build-up, and final-third play. These rankings are based on historical data spanning 1.1 billion on pitch events, enabling the comparison of 320,000+ players.

Machine Football Attributes

Team and player attributes are expressed as percentile rankings from 1-100, measuring how effectively individuals and squads performs across different areas of the pitch. All scores are calculated relative to every other team/player included in our model.

Player scores can be adjusted relative to:

  • Other players in the same position/all positions

  • Other players within the same league/the world

This allows for more accurate, like-for-like comparisons.

Within each attribute category, players are assessed across three to four underlying metrics, which combine to produce their overall score for that category.

Defensive attributes include:

  • Tackling

  • Defensive Headers

  • Ball Recovery

Build-up play attributes include:

  • Creativity

  • Passing Accuracy

  • Dribbling

  • Crossing

Final-third play attributes include:

  • Finishing (shots)

  • Finishing (aerial)

  • Attacking Positioning

For every player in our system, these attributes can then be viewed over time, allowing for precise tracking and analysis, updated on a game-by-game basis.

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