Modelling team performance in elite women`s basketball through principal component analysis and generalized mixed model - Xing, W.; Shaoliang, Z.; Lorenzo, A.; Gomez, M. (27th Annual Congress of the European College of Sport Science (ECSS), Sevilla, 30. Aug - 2. Sep 2022, 2022)
INTRODUCTION: Multicollinearity and confounding bias were two difficulties that plagued researchers in interpreting key performance indicators and predicting basketball team success through performance regression analysis. Multicollinearity leads to high variance estimators of regression coefficients. Confounding bias, especially the team`s performance style misleads the causal inference and the estimation of indicator effects. Therefore, the aim of this study was to remove the multicollinearity of the game-related data and identify the key performance indicators that really affect the match outcomes of elite women`s basketball.