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 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.

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-to-game 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 three-point 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 the game-related statistics which discriminate between winning, drawing and losing teams in Chinese Soccer Association Super League. The sample included 1056 balance games from the 2012–2017 Chinese Soccer Association Super League. Physical and technical game-related statistics were gathered. A one-way analysis of variance and discriminant analysis of data was done. The results showed that winning teams were significantly higher for the following game statistics: shots, shots on target, 50–50 challenge won, offsides, sprinting distance, sprinting effort, sprinting distance in ball possession and high-speed-running distance in ball possession. Losing teams had significantly higher averages in the variable crosses, passes, forward passes, sprinting distance out of ball possession and high-speed-running distance out of ball possession. Discriminant analysis concluded the following: the variables that discriminate between winning, drawing and losing teams were the shots on target, sprinting distance in ball possession, quality of opposition, passes and forward passes. Coaches and players should be aware of these different profiles in order to design and evaluate practices and competitions for their teams.

Background: The aim of this study is to describe the peak match demands and compare them with average demands in basketball players, from an external load point of view, using different time windows. Another objective is to determine whether there are differences between positions and to provide an approach for practical applications. Methods: During this observational study, each player wore a micro technology device. We collected data from 12 male basketball players (mean ± SD: age 17.56 ± 0.67 years, height 196.17 ± 6.71 cm, body mass 90.83 ± 11.16 kg) during eight games. We analyzed intervals for different time windows using rolling averages (ROLL) to determine the peak match demands for Player Load. A separate one-way analysis of variance (ANOVA) was used to identify statistically significant differences between playing positions across different intense periods. Results: Separate one-way ANOVAs revealed statistically significant differences between 1 min, 5 min, 10 min, and full game periods for Player Load, F (3,168) = 231.80, η_p² = 0.76, large, p < 0.001. It is worth noting that guards produced a statistically significantly higher Player Load in 5 min (p < 0.01, η_p² = −0.69, moderate), 10 min (p < 0.001, η_p² = −0.90, moderate), and full game (p < 0.001, η_p² = −0.96, moderate) periods than forwards. Conclusions: The main finding is that there are significant differences between the most intense moments of a game and the average demands. This means that understanding game demands using averages drastically underestimates the peak demands of the game. This approach helps coaches and fitness coaches to prepare athletes for the most demanding periods of the game and present potential practical applications that could be implemented during training and rehabilitation sessions.

The FIBA Basketball World Cup is one of the most prominent sporting competitions for men’s basketball, with coaches interested in key performance indicators (KPIs) that give a better understanding of basketball competitions. The aims of the study were to (1) examine the relationship between match KPIs and outcome in elite men’s basketball; and (2) identify the most suitable analysis (multiple linear regression (MLR) vs. quantile regression (QR)) to model this relationship during the men’s basketball tournament. A total of 184 performance records from 92 games were selected and analyzed via MLR and QR, using 10th, 25th, 50th, 75th and 90th quantiles. Several offensive (Paint Score, Mid-Range Score, Three-Point Score, Offensive Rebounds and Turnovers) and defensive (Defensive Rebounds, Steals and Personal Fouls) KPIs were associated with match outcome. The QR model identified additional KPIs that influenced match outcome than the MLR model, with these being Mid-Range Score at the 10th quantile and Offensive Rebounds at the 90th quantile. In terms of contextual variables, the quality of opponent had no impact on match outcome across the entire range of quantiles. Our results highlight QR modelling as a potentially superior tool for performance analysts and coaches to design and monitor technical–tactical plans during match-play. Our study has identified the KPIs contributing to match success at the 2019 FIBA Basketball World Cup with QR modelling assisting with a more detailed performance analysis, to support coaches with the optimization of training and match-play styles.