Evaluating actions in football using machine learning

Before you start reading…

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How it works

We used data from hundreds of thousands of shots, passes, blocks, interceptions and every other action performed in three seasons of the top five leagues. We then use a statistical model to determine the value of every single match event.

More details here: https://analytics.twelve.football/analytics?analysisId=b3d82a07e72146309e741d5d01cdaea6&viewType=shot
More details here: https://analytics.twelve.football/analytics?analysisId=06a70b4e79e14d4b894c2c61c09ef675&viewType=attack
More details here: https://analytics.twelve.football/analytics?analysisId=551425d2e7884f7295d367430f2d6de8&viewType=defence
For more details see: https://analytics.twelve.football/analytics?analysisId=e3219e6e201749fe80cf666f41ebf9b9&viewType=shot

Professional scouting

We work with clubs in providing rankings that make sense for scouting. Here we use player radars, which don’t count just actions, but count the value-added to the team (in terms of points) by the actions. These are divided in terms of attack:

Technical Details

Twelve primarily make use of logistic regression and other supervised machine learning methods to calculate the probability that different actions lead to a goal. Here we outline the method we use for passes.



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David Sumpter

David Sumpter


Professor of Applied Mathematics. Books: The Ten Equations (2020); Outnumbered (2018); Soccermatics (2016) and Collective Animal Behavior (2010).