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.
The higher-order PLS-SEM confirmatory approach for composite indicators of football performance quality
Cefis, Mattia
;Carpita, Maurizio
2024-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
