The higher-order PLS-SEM confirmatory approach for composite indicators of football performance quality

Supporting the strategic decisions of a football team’s management is becoming crucial. We create some new composite indicators to measure the performance quality, applying both Confirmatory Tetrad Analysis (CTA) and Confirmatory Composite Analysis (CCA) to a Third-Order Partial Least Squares Structural Equation Model (PLS-SEM). To do this, data provided by Electronic Arts (EA) Sports experts and available on the Kaggle data science platform has been used; in particular, the dataset was composed of 29 Key Performance Indices defined by EA Sports experts, concerning the top 5 European leagues. A PLS-SEM for each player’s role was developed, relying on the most recent season, 2021/2022. In order to improve each model, a CTA to evaluate the nature of the constructs (formative or reflective) and a CCA were applied. The results underline how some sub-areas of performance have different significance weights depending on the player’s role; as concurrent and predictive analysis, our third-order Player Indicator overall was compared with the existing EA overall and with some performance quality proxies, such as the player’s market value and wage, showing interesting and consistent relations.