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.