Discriminant analysis of game-related statistics between basketball guards, forwards and centres in three professional leagues

Abstract

The aim of the present paper was to examine the differences in game-related statistics between basketball guards, forwards and centres playing in three professional leagues: National Basketball Association (NBA, superior level) in the USA, Associación de Clubs de Baloncesto (ACB, one of the best European leagues) in Spain and Liga de Clubes de Basquetebol (LCB, inferior level) in Portugal. We reasoned that the knowledge of these differences could allow the coaches to better establish and monitor playing patterns and increase the effectiveness of the player recruitment process. Archival data was gathered for the 2000–2001 play-off final series of the NBA (five games), ACB (three games) and LCB (four games). For players in each league, discriminant analysis was able to identify game-related statistics that maximized mean differences between playing positions (p<0.05). The interpretation of the obtained discriminant functions was based on examination of the structure coefficients greater than ∣0.30∣. In the LCB league, centres and guards were discriminated mainly in terms of defensive tasks, with emphasis on blocks (structure coefficient, SC=0.35) and defensive rebounds (SC=0.43) and a de-emphasis on unsuccessful 3-point field-goals (SC= − 0.37). In the ACB, centres and guards were discriminated by offensive tasks, with emphasis on assists (SC=0.52) and 3-point field-goals, both successful (SC=0.35) and unsuccessful (SC=0.35), and a de-emphasis on offensive rebounds (SC= − 0.44). Finally, in the NBA league guards and centres were discriminated by offensive tasks, with emphasis on offensive rebounds (SC=0.31) and a de-emphasis on assists (SC= − 0.37) and unsuccessful 3-point field-goals (SC= − 0.34). These three analyses provided high overall percentages of successful classification (86% for the LCB league, 74% for the ACB and 85% for the NBA). Generally, the players’ game-related statistics varied according to playing position, probably because of the well-known differences in the players’ anthropometric characteristics that conditioned the distance they play from the basket. Coaches can use these results to reinforce the importance of relying on different players’ contributions to team performance and evaluate players’ game performance according to their playing position. Conversely, these discriminant models could help in player recruitment and improve training programmes.