Basketball can be described as a moderate-to-long duration exercise including repeated bouts of high-intensity activity interspersed with periods of low to moderate active recovery or passive rest. A match is characterized by repeated explosive activities, such as sprints, jumps, shuffles and rapid changes in direction. In top-level modern basketball, players are frequently required to play consecutive matches with limited time to recover. To ensure adequate recovery after any basketball activity (i.e., match or training), it is necessary to know the type of fatigue induced and, if possible, its underlying mechanisms. Despite limited scientific evidence to support their effectiveness in facilitating optimal recovery, certain recovery strategies are commonly utilized in basketball. It is particularly important to optimize recovery because players spend a much greater proportion of their time recovering than they do in training. Therefore, the main aim of this report is to facilitate useful information that may lead to practical application, based on the scientific evidence and applied knowledge specifically in basketball.

The study of elite basketball players’ anthropometric characteristics alongside those of body composition contributes significantly to their profiling as professional athletes and plays an important role in the selection process, as these characteristics can have a significant impact on performance. In the current study, 110 professional basketball players from a series of Spanish professional Leagues (ACB, LEB and EBA) and youth level National Teams (U20 and U18) had their anthropometric profiles measured and compared to determine differences between them. Furthermore, all 110 players were divided into three different categories according to their playing position: guards, forwards and centres. The results obtained show no significant differences between players in different competitions in weight, height and the sum of skinfolds. Nonetheless, there were several differences related to body fat content (13.03% in ACB players and 10.52% in the lower categories and National Teams). There were also several differences found between the different playing positions amongst all playing levels in body mass (79.56 ± 2.41, 91.04 ± 1.51 and 104.56 ± 1.73 kg), height (182.28 ± 0.96, 195.65 ± 1.00 and 204.08 ± 0.67 cm), skinfold distribution and perimeters. However, there were no significant differences in body fat content between the different playing positions. The conclusions obtained from this study provide a better understanding to basketball specialists regarding the selection process of players at the elite level, especially on the transition from youth elite programs to men’s elite leagues.

This study aimed to identify the game-related statistics that discriminated between Euroleague basketball players and European basketball players playing in the NBA, when competing in the same event (EuroBasket 2015). There was a total of 78 matches played by 24 teams in two groups of analysis: NBA, participants in the European Championship who played in the NBA season of 2014-2015 (n = 26); Euroleague, participants in the European Championship who played in the Euroleague season of 2014-2015 (n = 82). The players’ performance variables were normalized to the time they spent on the court. To identify which variables best discriminated between the NBA and the Euroleague performance profiles, a descriptive discriminant analysis was conducted. Structure coefficients (SC) from the matrix greater than |0.30| were interpreted as meaningful contributors to discriminating between the groups. The results revealed a significant function (p = 0.008, canonical correlation of 0.51, Λ = 0.74, reclassification = 84.2%) and substantial performance differences in game-related statistics much related to the influence of body size (body height and mass), such as two-point field goals made (SC = 0.42) and missed (SC = 0.40), free-throws made (SC = 0.55), defensive rebounds (SC = 0.62), blocks (SC = 0.48) and suffered fouls (SC = 0.34). No differences were found at the level of game-related statistics indirectly related to perception, such as assists, turnovers or steals. Also, the greater body size in NBA players was likely related to higher variability in performance, thus, being an important topic for coaches and recruiters to analyse.

The importance of umpire during competition makes it essential to identify in detail the referee’s performance. The present study aimed to carry out a bibliographic review that presents an analysis of the factors that influence a referee’s performance, from the physical/physiological, psychological and decisional points of view. The design was centred on a systematic data review according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The databases used for the review have been WOS and SCOPUS. The inclusion criteria were: (i) Studies in English and Spanish; (ii) Investigations on the physiological, psychological and technical profile of basketball referees; and (iii) Relevant data on the performance of basketball referees during competition. After the review process, 51 articles were selected, classified into three groups of variables: “Decision-Making” (16), “Psychological Demands” (15) and “Physiological/Physical Demands” (21). The results show the importance and influence of the basketball referee during competition and the need to work on their continuous training according to the requirements of the game. Specifically, the referee must work to improve in three principal aspects: decision-making (knowledge of the rules, their application during the competition, the mechanics of movements or the control of the game in situations of conflict or stress); physical/physiological condition (HR, Distance Covered, Speed, PlayerLoad or METs) and stress situations (referee errors, protests, abuses or game adjusted). All of the above should be related with the experience and competence that all referees should acquire to be able to develop their functions as successfully as possible, using the different tools and methods detailed in the studies analysed to help them to improve their vision and their role in the competition.

Vaquera, A, Suárez-Iglesias, D, Guiu, X, Barroso, R, Thomas, G, and Renfree, A. Physiological responses to and athlete and coach perceptions of exertion during small-sided basketball games. J Strength Cond Res 32(10): 2949–2953, 2018—This study describes heart rate (HR) responses during different small-sided games (SSGs) in junior basketball players and identifies the level of agreement between athlete and coach perceptions of internal training load calculated using the in-task rating of perceived exertion (RPE) method. Over a 6-week period, 12 male junior basketball players, who played in the Spanish national under-18 League, played 7 games of one-a-side (1v1), 6 games of 2-a-side (2v2), 8 games of 5-a-side (5v5), and 5 games of superiority (3v2) situations. During 1v1, 2v2, 5v5, and 3v2, peak HRs were 90.27 ± 3.37%, 92.68 ± 3.29%, 92.01 ± 3.48%, and 88.74 ± 5.77% of HRmax, respectively. These differences were statistically significant between 1v1 and 2v2 (p < 0.01), 1v1 and 5v5 (p ≤ 0.05), 2v2 and 3v2 (p < 0.001), and 5v5 and 3v2 (p < 0.001). Mean HR was 79.5 ± 4.4%, 83.1 ± 4.2%, 91.2 ± 4.7%, and 78.5 ± 7.5% of HRmax during 1v1, 2v2, 5v5, and 3v2, respectively, and differences were observed between 1v1 and 2v2 (p < 0.001), 2v2 and 3v2 (p < 0.001), and 5v5 and 3v2 (p ≤ 0.05). There were differences in athletes and coaches in-task RPE in all SSGs (all p < 0.0001 apart from 5 × 5 p = 0.0019). The 2v2 format elicited a higher mean in-task RPE in comparison with all other SSGs (p < 0.001), possibly because 2v2 imposes a greater cognitive load.

The aim of the present study was to identify and describe players’ performances in NBA games using individual and team-based game variables. The sample was composed by 535 balanced games (score differences below or equal to eight points) from the regular season (n=502) and the playoffs (n=33). A total of 472 players were analysed. The individual-based variables were: minutes on court, effective field-goal percentage, free-throws/field-goals ratio, offensive rebound percentage, turnover percentage and playing position. The team-based variables were: team points minus opponent’s points (on and off court), NET score (player’s on values minus his/her off values), maximum negative and positive point difference, team’s winning percentage, game pace, defensive and offensive ratings. A two-step cluster analysis was performed to identify the player’s profiles during regular season and playoff games. The results identified five performance profiles during regular season games and four performance profiles during playoff games. The profiles identified were mainly characterized by the game quarter and the negative NET indicator (players’ performance on court minus their performance off court) in regular season games and the positive NET indicator during playoff games and second and third game-quarters. Coaching staffs can fine-tune these profiles to develop more team-specific models and, conversely, use the results to monitor and rebuild team formation under the constrained dynamics of the game and competition stages.