Publication Date:
2022
Abstract:
Nowadays, PLS-SEM is a trend-topic, whereas football is moving towards
a data-driven approach; by combining these two worlds, we aim to show a new way
for measuring football goalkeepers’ performance, by using data provided from EA
Sports experts and available on the Kaggle data science platform. Furthermore, another
objective is to refine the model, supporting football experts from a statistical
point of view. For this purpose, we adopt a confirmatory tetrad analysis (CTA-PLS)
to validate and evaluate the nature (e.g. formative or reflective) of each latent variable.
Then, a second-order PLS-SEM model is built. We validate and compare this
new indicator with a benchmark (the EA overall). The final goal is to prove the CTA
approach on a real case study and to refine a composite performance indicator for
helping football policy makers taking strategic decisions.
a data-driven approach; by combining these two worlds, we aim to show a new way
for measuring football goalkeepers’ performance, by using data provided from EA
Sports experts and available on the Kaggle data science platform. Furthermore, another
objective is to refine the model, supporting football experts from a statistical
point of view. For this purpose, we adopt a confirmatory tetrad analysis (CTA-PLS)
to validate and evaluate the nature (e.g. formative or reflective) of each latent variable.
Then, a second-order PLS-SEM model is built. We validate and compare this
new indicator with a benchmark (the EA overall). The final goal is to prove the CTA
approach on a real case study and to refine a composite performance indicator for
helping football policy makers taking strategic decisions.
CRIS type:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
CTA-PLS, PLS-SEM, Latent variables, Football, Performance
List of contributors:
Cefis, M.; Brentari, E.
Book title:
Book of the Short Papers