Abstract
The aim of the present study was to identify basketball game performance indicators which best discriminate winners and losers in regular season and playoffs. The sample used was composed by 323 games of ACB Spanish Basketball League from the regular season (n=306) and from the playoffs (n=17). A previous cluster analysis allowed splitting the sample in balanced (equal or below 12 points), unbalanced (between 13 and 28 points) and very unbalanced games (above 28 points). A discriminant analysis was used to identify the performance indicators either in regular season and playoff games. In regular season games, the winning teams dominated in assists, defensive rebounds, successful 2 and 3-point field-goals. However, in playoff games the winning teams’ superiority was only in defensive rebounding. In practical applications, these results may help the coaches to accurately design training programs to reflect the importance of having different offensive set plays and also have specific conditioning programs to prepare for defensive rebounding.
Highlights
Preparing basketball teams to succeed in competition was a complex process that was based on players’ fitness levels and anthropometric characteristics (Sampaio et al, 2010)
The structure coefficients from the function reflected an emphasis on assists, defensive rebounds, successful 2 and 3 point field-goals (Table 1)
There were no variables emphasized by the structure coefficients (Table 1)
Summary
Preparing basketball teams to succeed in competition was a complex process that was based on players’ fitness levels and anthropometric characteristics (Sampaio et al, 2010). Coaches prepare the competition and training process using notational analysis with the scope of improving both the team’s and the players’ performances (Hughes and Franks, 2004; Ortega et al, 2009; Leite et al, 2009). Performance analysis in basketball is currently an essential tool for coaches and technical staff. This analysis method allows them to collect reliable information about their opponents, competition and, mainly, their own team. Game-related statistics are very popular among coaches, players and researchers and have been used to improve understanding of game performance in different contexts (Gómez et al, 2010; Gómez et al, 2009; Ibáñez et al, 2008; Sampaio and Janeira, 2003)
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