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, Associacio ´n 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 Á2001 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 B/0.05). The interpretation of the obtained discriminant functions was based on examination of the structure coefficients greater than j0.30j. In the LCB league, centres and guards were discriminated mainly in terms of defensive tasks, with emphasis on blocks (structure coefficient, SC 0/0.35) and defensive rebounds (SC0/0.43) and a deemphasis on unsuccessful 3-point field-goals (SC 0/(/0.37). In the ACB, centres and guards were discriminated by offensive tasks, with emphasis on assists (SC0/0.52) and 3-point field-goals, both successful (SC0/0.35) and unsuccessful (SC0/ 0.35), and a de-emphasis on offensive rebounds (SC0/(/0.44). Finally, in the NBA league guards and centres were discriminated by offensive tasks, with emphasis on offensive rebounds (SC 0/0.31) and a de-emphasis on assists (SC0/ (/0.37) and unsuccessful 3-point field-goals (SC 0/ (/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' gamerelated 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 fieldgoal 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 ^-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 the present study was to identify game-related statistics that differentiate winning and losing teams according to game location. The sample included 306 games of the 2004-2005 regular season of the Spanish professional men's league (ACB League). The independent variables were game location (home or away) and game result (win or loss). The game-related statistics registered were free throws (successful and unsuccessful), 2- and 3-point field goals (successful and unsuccessful), offensive and defensive rebounds, blocks, assists, fouls, steals, and turnovers. Descriptive and inferential analyses were done (one-way analysis of variance and discriminate analysis). The multivariate analysis showed that winning teams differ from losing teams in defensive rebounds (SC = .42) and in assists (SC = .38). Similarly, winning teams differ from losing teams when they play at home in defensive rebounds (SC = .40) and in assists (SC = .41). On the other hand, winning teams differ from losing teams when they play away in defensive rebounds (SC = .44), assists (SC = .30), successful 2-point field goals (SC = .31), and unsuccessful 3-point field goals (SC = -.35). Defensive rebounds and assists were the only game-related statistics common to all three analyses.

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.

The aim of this study was to (i) identify technical and physical performances of basketballers according to playing position in strong and weak teams, and (ii) describe variability in game-togame performance according to game outcome, location, quality of teams and opposition. Performance-related variables of all the 699 matches of regular season 2015-2016 in the National Basketball Association were analysed. All the comparisons were performed using magnitude-based inferences. As could be expected, results showed that technical and physical performances differed between players of strong and weak teams. In technical aspect, forwards and centres from strong teams made more three-point field goals, but fewer two-point field goals, than their counterparts from weak teams. Interestingly, forwards and guards from strong teams covered shorter distances and lower speeds than their peers from weak teams. In addition, the threepoint field goals made and attempted presented high variability. Game location generally had no significant impact on the variability of players' performance. Guards exhibited relatively lower variability in technical and physical variables in comparison with players from other positions. Exploring the difference and variability of technical and physical performances of basketballers allows fine-tuning of practice and game plans in order to build up optimal winning strategies.

The aim of the present study was to identify differences in defensive strategies used during basketball games, to compare the defensive strategies used by home and away basketball teams, and to analyze the effectiveness of home and away ball possessions when playing against each defensive strategy. The sample was composed of 10 games of the Spanish men's 2005-2006 regular basketball season (N = 1,785 ball possessions). The analyzed variables were the number of types of defenses used, points per possession, foul percentage, and turnover percentage according to the type of defensive strategy and game location. The game location main effect was significant in points per possession, with home teams having lower values than away teams. The defensive strategy main effect was significant in number of types of defenses used, with man-to-man as the most frequently utilized defense, and foul percentages with higher values in zone defenses. There was a statistically significant interaction in turnover percentages, with significantly lower values for man-to-man defense and home games. Overall, it is suggested that team performance for the studied variables changed according to the factors and, thus, it may be beneficial to change defensive (and offensive) strategies according to game location.

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.