How do European and non-European players differ: Evidence from EuroLeague basketball with multivariate statistical analysis - Cene, Erhan; Ozdalyan, Firat; Parim, Coskun; Manci, Egemen; Inan, Tugbay (SAGE PUBLICATIONS LTD, 2026)
This study has multiple purposes and these are (i) to divide EuroLeague players into clusters according to their statistics and positions, (ii) to examine the clusters according to the players' characteristics and their continents of origin, and (iii) to compare statistics of European and non-European EuroLeague basketball players according to their positions. Dataset is based on the 2020-2021 EuroLeague season. Similarities and differences between players are explained by using the t-test/Mann-Whitney U test, effect sizes, Cluster Analysis (CA), and Multi-Dimensional Scaling (MDS). Six different MDS maps have been introduced to separate guards, forwards, and centers in terms of performance indicators (including both raw and per 40 min statistics). Moreover, the vast majority of MDS maps revealed for all positions are visualized with six clusters. MDS Results show that players playing in similar positions and exhibiting similar performances in the EuroLeague are on the same maps. Also, the results prove that European and non-European basketball players have different playing styles and certain clusters are dominated by either European or non-European players. The information to be obtained from this study may benefit on basketball players, coaches, and managers regarding various issues (player development plan and player transfer policy).
Keywords: EuroLeague; basketball; multidimensional scaling (MDS); cluster analysis (CA); sports analytics; basketball statistics; European/Non-European; sports performance; game statistics; player comparison; GAME-RELATED STATISTICS; NBA; PREDICTION; INSIGHTS; Engineering; Sport Sciences