An innovative xG Model for football analytics

In the field of football analytics, we want to improve (in terms of prediction performance) one of the emerging tool: the expected goal (xG) model. With this final goal, we merged match event data with some players’ performance composite indicators obtained using a Partial Least Squares - Structural Equation Model (PLS-SEM). Using a sample of match tracking data relying to season 2019/2020 of the Italian Serie A, composed by 660 shots and 25 features, a logistic regression model was applied on different scenarios for sample balanced techniques. Results seem to be interesting in terms of sensitivity, F1 and AUC indices, compared with a benchmark