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The purpose of the present study was to analyse men's basketball competitions, trying to identify which game-related statistics allow to discriminate winning and losing teams. The sample used corresponded to 306 games from the 2004-2005 Regular Season of the Spanish Men's Professional League. The game-related statistics gathered were: 2 and 3 points field-goals (both successful and unsuccessful), free-throws (both successful and unsuccessful), offensive and defensive rebounds, blocks, assists, fouls, turnovers and steals. The data were analysed in two groups: balanced games (final score differences equal or below 12 points) and unbalanced games (final score differences above 12 points). Discriminant analysis allowed to conclude the following: (i) in balanced games, the variable that best differentiate both groups were the defensive rebounds; (ii) in unbalanced games, the variables that discriminate between both groups were the successful 2 points field-goals, the defensive rebounds and the assists; and (iii) in all games, the statistical analysis identified two variables that discriminate winning and losing teams (defensive rebounds and assists). The defensive rebounds were the only game-related statistic that discriminates both groups in all performed analysis. Coaches and players should be aware of these different profiles in order to increase knowledge about game cognitive and motor solicitation and, therefore, to enhance specificity at the time of practice and game planning.
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... The match analysis of physical demands and game-related statistics has been shown to explain differences in match outcome and identify key performance indicators for success [1][2][3][4][5][6][7]. Knowledge of these key performance indicators can guide training prescription and strategy to improve the overall success of the team. ...
... Knowledge of these key performance indicators can guide training prescription and strategy to improve the overall success of the team. For example, analysis of team game-related statistics in elite men's and women's basketball shows that field goal percentage, offensive and defensive rebounds, assists, steals, blocks and fouls against are greater, while turnovers and fouls committed are lower, in wins compared to losses [1][2][3][4]. On-court match demands can be influenced by various situational and contextual factors, such as playing position [8], playing period [8] and match outcome [6,7,9]. When exploring physical match demands between wins and losses, findings differ between studies. ...
... This finding suggests that an inability to maintain possession through offensive rebounds might contribute to streaks occurring. It has been identified that more defensive rebounds are associated with more overall match success when comparing wins and losses [1][2][3][4], and fewer offensive rebounds are associated with less overall match success [1,2]. The findings of the present study support the notion that maximizing opportunities to regain possession through defensive rebounds and maintain possession through offensive rebounds is important for success. ...
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... Quantitative analysis of game-related statistical parameters has been widely used as a measure of individual and team basketball performance efficiency on various levels of basketball competition [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Basketball coaches and sport scientists commonly rely on these performance parameters to develop offensive and defensive team strategies, plan off-and on-court training regimens, and identify areas for basketball skill-related improvements [1,2,11,13]. ...
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... when games were close (≤ 5 points). This result may be because when games are close, other GRS have an increased importance 23,24 . For example, in close games, winning might be explained by rebounding efficiency or lack of turnovers 23 . ...
... This result may be because when games are close, other GRS have an increased importance 23,24 . For example, in close games, winning might be explained by rebounding efficiency or lack of turnovers 23 . The ability of BEi to predict better in close games reinforces the relevance of using a different scheme to weight each GRS when calculating the summary of a team's performance. ...
... However, and based on previous evidence (Wunderlich et al., 2021), referee bias appears to be the most viable explanation for teams being sanctioned more in the presence of spectators. There is evidence that quality of offensive plays allowed a better process of perception, decision-making, execution, and best performance by players (Gomez et al., 2008). In fact, cognitive factors including past experience, motivation, and development largely contribute to this process (Wu et al., 2013). ...
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... In terms of better team communication, which could be enabled by home court advantage and familiarity (and which could be easily disturbed by the loud noise produced by the spectators within the arena), team assists could be an indication of such variable. As a vital statistical indicator, the assist was identified by Gomez et al. (2008) and Melnik (2001) as one of the discriminating factors between teams and between winning or losing a game. A similar point was raised by Dogan et al. (2016) who included assists as an important game-related statistical category that has a great impact on the team's success. ...
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