A machine learning approach for player and position adjusted expected goals in football (soccer)

Abstract

Football is a very result-driven industry, with goals being rarer than in most sports, so having further parameters to judge the performance of teams and individuals is key. Expected Goals (xG) allow further insight than just a scoreline. To tackle the need for further analysis in football, this paper uses machine learning methods that are developed and applied to Football Event data. The proposed solution utilises StatsBomb as the data provider and an industry benchmark to tune the models in the right direction. The proposed ML solution for xG is further used to tackle the age-old cliche of: ‘the ball has fallen to the wrong guy there’. To investigate this, we tackle Positional Adjusted xG, splitting the training data into Forward, Midfield, and Defence to provide insight into player qualities based on their positional sub-group. Positional Adjusted xG successfully predicts and proves that more attacking players are better at accumulating xG. Finally, this study has further developments into Player Adjusted xG to prove that Lionel Messi is statistically at a higher efficiency level than the average footballer. Thanks to this analysis, we conclude that the Messi model performs 347 xG higher than the general model outcome.