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.

The aim of the present study was to identify the game‐related statistics that discriminate between season‐long successful and unsuccessful basketball teams participating in the Spanish Basketball League (LEB1). The sample included all 145 average records per season from the 870 games played between the 2000–2001 and the 2005–2006 regular seasons. The following game‐related statistics were gathered from the official box scores of the Spanish Basketball Federation: 2‐ and 3‐point field‐goal attempts (both successful and unsuccessful), free‐throws (both successful and unsuccessful), defensive and offensive rebounds, assists, steals, turnovers, blocks (both made and received), and fouls (both committed and received). To control for season variability, all results were normalized to minutes played each season and then converted to z‐scores. The results allowed discrimination between best and worst teams’ performances through the following game‐related statistics: assists (SC=0.47), steals (SC=0.34), and blocks (SC=0.30). The function obtained correctly classified 82.4% of the cases. In conclusion, season‐long performance may be supported by players’ and teams’ passing skills and defensive preparation.

The aim of the present study was to identify the importance of basketball performance indicators in predicting the effectiveness of ball possessions in men's and women's basketball, when controlling for situational variables and game periods. The sample consisted of 7234 ball possessions, corresponding to 40 games from the Spanish professional leagues. The effects of the predictor variables on successful ball possessions according to game period were analysed using binary logistic regressions. Results from men's teams show interactions with number of passes and ending player during the first five minutes, with starting and ending zone, defensive systems, screens used and possession duration during the middle thirty minutes, and there were interactions with passes used, possession duration and players involved during the last five minutes. Results from women's teams show interactions with starting and ending zone, passes used, defensive systems and ending player during the first five minutes, and with starting and ending zone, and screens used during the middle thirty minutes. The results show no interaction with situational variables in men's basketball, while league stage was important during the middle thirty minutes and last five minutes in women's basketball, whereas match status was only important during the last five minutes.

The aim of this study was: (i) to group basketball players into similar clusters based on a combination of anthropometric characteristics and playing experience; and (ii) explore the distribution of players (included starters and non-starters) from different levels of teams within the obtained clusters. The game-related statistics from 699 regular season balanced games were analyzed using a two-step cluster model and a discriminant analysis. The clustering process allowed identifying five different player profiles: Top height and weight (HW) with low experience, TopHW-LowE; Middle HW with middle experience, MiddleHW-MiddleE; Middle HW with top experience, MiddleHW-TopE; Low HW with low experience, LowHW-LowE; Low HW with middle experience, LowHW-MiddleE. Discriminant analysis showed that TopHW-LowE group was highlighted by two-point field goals made and missed, offensive and defensive rebounds, blocks, and personal fouls; whereas the LowHW-LowE group made fewest passes and touches. The players from weaker teams were mostly distributed in LowHW-LowE group, whereas players from stronger teams were mainly grouped in LowHW-MiddleE group; and players that participated in the finals were allocated in the MiddleHW-MiddleE group. These results provide alternative references for basketball staff concerning the process of evaluating performance.

Ball screens are one of the most frequently used tactical behaviour in elite basketball games. The aim of the present study was to identify their predictors of success related to time, space, players, and tasks performed. The sample was composed of 818 ball screens corresponding to 20 close games (mean differences in score of 3.1 ± 0.8 points) randomly selected from the playoff games of the Spanish Basketball League (2008-2011). Classification tree analysis (CHAID) was used to analyse which variable or combination of variables, better predicts effectiveness during ball screens. The main results allowed identifying interactions with dribbler actions after the screen and the orientation of the screen on the ball. The results showed no interaction with game quarter and quarter minute temporal-related variables in both analyses. The present findings allow improving coaches' strategic plans that involve selecting the most appropriate offensive approach when performing ball screens.

The aim of this study was two folds: (i) to explain the relationships between game-related statistics and game outcome based on team quality and (ii) to explore the effects of opposition interaction on the players’ technical and physical performances according to playing position. Game-related statistics of all the 692 balanced games of regular season 2016–2017 in the National Basketball Association were analysed. All the analyses were conducted by decision-tree algorithm (exhaustive CHAID) and magnitude-based inferences. Our study indicated that defensive rebounds, blocked shots, and assists determined between winning and losing games for stronger teams while defensive rebounds and turnovers were the key performance indicators for weaker teams. Subsequently, in stronger vs stronger games, players from winning teams in home games ran slower than their peers from losing teams, whereas an opposite trend was found in away games. In stronger vs weaker games, players from winning teams in home games covered more distance and ran faster than their peers from losing teams. In weaker vs weaker games, effective defence played an important role in winning the games. The findings provide references for coaches and performance analysts to design training plans accounting for quality of teams and oppositions.