Football analytics: a Higher-Order PLS-SEM approach to evaluate players’ performance

Nowdays, data science is applied in several area of our life, and also many applications in sports fields are increasing. In this context, we are focusing on football (e.g. soccer); thanks to this work we have the aim to give a new approach in the evaluation of football players’ performance given from the EA Sports experts and available on Kaggle in the KES dataset. For this purpose, we adopt a Higher-Order PLS-SEM approach to the sofifa KPIs (e.g. Key Performance Indicators) in order to compute a composite indicator and compare it with the well-known overall index from EA Sports. The final goal is to suggest a new performance index for helping coaches and scouting staff of professional teams to take strategic decisions, in order to evaluate impartially players’ performance.