Research Article
A PLS-SEM Approach for Composite Indicators: An Original Application on the Expected Goal Model
@INPROCEEDINGS{10.1007/978-3-031-55722-4_10, author={Mattia Cefis}, title={A PLS-SEM Approach for Composite Indicators: An Original Application on the Expected Goal Model}, proceedings={Intelligent Technologies for Interactive Entertainment. 14th EAI International Conference, INTETAIN 2023, Lucca, Italy, November 27, 2023, Proceedings}, proceedings_a={INTETAIN}, year={2024}, month={3}, keywords={Expected Goal Logistic Regression Imbalanced Sample PLS-SEM}, doi={10.1007/978-3-031-55722-4_10} }- Mattia Cefis
Year: 2024
A PLS-SEM Approach for Composite Indicators: An Original Application on the Expected Goal Model
INTETAIN
Springer
DOI: 10.1007/978-3-031-55722-4_10
Abstract
In the field of football analytics, the goal is to improve (in terms of prediction performance) one of the emerging tools: the expected goal (xG) model. With this final aim, data from different sources have been merged: tracking data, match event data and some players’ performance composite indicators obtained using a Partial Least Squares - Structural Equation Model (PLS-SEM) approach. Using a sample of match data relying to season 2019/2020 of the Italian Serie A, composed by 1 outcome variable (i.e. the GOAL) and 22 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 metrics, compared with a benchmark. In addition, some original performance composites and tracking variables introduced are significant for the classification model.
