basketball research articles

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Exploring Game Performance in the National Basketball Association Using Player Tracking Data

Contributed equally to this work with: Jaime Sampaio, Tim McGarry

* E-mail: [email protected]

Affiliation Research Center in Sports Sciences, Health and Human Development, CIDESD, CreativeLab Research Community, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal

Affiliation Faculty of Kinesiology, University of New Brunswick, Fredericton, Canada

¶ ‡ These authors also contributed equally to this work.

Affiliation Faculty of Physical Activity Sport Sciences, University of the Basque Country, Vitoria, Spain

Affiliation Facultad de Ciencias de la Actividad Física y el Deporte, Universidad Europea de Madrid, Madrid, Spain

Affiliation Complex Systems and Sport Research Group, National Institute of Physical Education of Catalonia (INEFC), Barcelona, Spain

Affiliation Lithuanian Sports University, Kaunas, Lithuania

• Jaime Sampaio, 
• Tim McGarry, 
• Julio Calleja-González, 
• Sergio Jiménez Sáiz, 
• Xavi Schelling i del Alcázar, 
• Mindaugas Balciunas

• Published: July 14, 2015
• https://doi.org/10.1371/journal.pone.0132894
• Reader Comments

Recent player tracking technology provides new information about basketball game performance. The aim of this study was to (i) compare the game performances of all-star and non all-star basketball players from the National Basketball Association (NBA), and (ii) describe the different basketball game performance profiles based on the different game roles. Archival data were obtained from all 2013-2014 regular season games (n = 1230). The variables analyzed included the points per game, minutes played and the game actions recorded by the player tracking system. To accomplish the first aim, the performance per minute of play was analyzed using a descriptive discriminant analysis to identify which variables best predict the all-star and non all-star playing categories. The all-star players showed slower velocities in defense and performed better in elbow touches, defensive rebounds, close touches, close points and pull-up points, possibly due to optimized attention processes that are key for perceiving the required appropriate environmental information. The second aim was addressed using a k-means cluster analysis, with the aim of creating maximal different performance profile groupings. Afterwards, a descriptive discriminant analysis identified which variables best predict the different playing clusters. The results identified different playing profile of performers, particularly related to the game roles of scoring, passing, defensive and all-round game behavior. Coaching staffs may apply this information to different players, while accounting for individual differences and functional variability, to optimize practice planning and, consequently, the game performances of individuals and teams.

Citation: Sampaio J, McGarry T, Calleja-González J, Jiménez Sáiz S, Schelling i del Alcázar X, Balciunas M (2015) Exploring Game Performance in the National Basketball Association Using Player Tracking Data. PLoS ONE 10(7): e0132894. https://doi.org/10.1371/journal.pone.0132894

Editor: José César Perales, Universidad de Granada, SPAIN

Received: February 14, 2015; Accepted: June 22, 2015; Published: July 14, 2015

Copyright: © 2015 Sampaio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: Data are publicly available from the NBA website ( http://stats.nba.com ).

Funding: This study was supported by the Portuguese foundation for science and technology (PEst-OE/SAU/UI4045/2015). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The National Basketball Association (NBA) from the United States of America is the most competitive basketball league in the world, with a competition period in regular season comprising 82 games spanning approximately 24 weeks. The coaching staff must prepare and oversee the training loads on players throughout the entirety of the competition period, a complex process that places a great amount of physiological stress on the athletes [ 1 ]. This process also requires managing the significant differences in work demands introduced by position-specific game behaviors and player status (e.g., starting vs non-starting players), as well as adjusting throughout the season to several changing unpredictable constraints such as player injuries. Thus, the ongoing planning and monitoring of practice sessions and game performance is critical for optimizing the decisions on individual training loads taken by coaching staff.

While each player responds individually to the stress of practice and competition [ 2 ], there remains a clear need to use updated sports performance models to inform starting points for player preparation. One of the most common methods of monitoring sports performance is using game-related statistics to evaluate technical and tactical behavior, as well as the efficiency of players and teams throughout the season. Research reporting these variables frequently uses data from European league games but not from the NBA. This study using NBA data serves somewhat to address this imbalance. Performance variables represent duality of the performer and the environment in order to understand how players engage with others by detecting affordances [ 3 ]. For example, the assists are likely a result of affordances to the ball carrier created by open teammates. In fact, perception-action coupling indicates that information drives movement and movement drives information available for players to pick up [ 4 ]. In this sense, game-related statistics can provide insight on both perception and action of the players. In addition, they may provide a measure of co-adaptation, in the way that players function as part of a larger system (the team) co-adapting to small but important changes in each others structure and function [ 5 ].

Basketball performance depends primarily on shooting 2-point field-goals and on securing defensive rebounds [ 6 – 8 ]. In close contested games, however, fouls and free-throws exhibit increased importance for determining game outcome than for lesser contested games [ 8 , 9 ]. Other remaining game statistics such as offensive rebounds, turnovers, steals and assists are not reported consistently as discriminating performance variables for winning and losing. When contrasting the best and worst teams, the best performance variables for long term success are related to assists, steals and blocks, denoting the importance of passing skills and of defensive skills along outside and inside court positions [ 10 ]. Research from NBA data likewise reported winning game outcomes to be related to better offensive efficiency, specifically points scored in the third quarter, as well as the defensive variables of fouls and steals [ 11 ]. Thus, as expected, the results suggest that both offensive and defensive variables are important for winning games.

These descriptions are informative on a team-level basis, however, a need exists to undertake player-level analysis in order to better understand what performance variables most discriminate elite players from other players. In the NBA context, this aim can be accomplished by contrasting game performances from the awarded players that comprise the first, second and third NBA team (all-stars) with the performance statistics of the other players. The all-star players from these three teams are selected from a voting conducted by a panel of sportswriters and broadcasters [ 12 ]. The players receive five points for a first team vote, three points for a second team vote, and one point for a third team vote. At the end, and accounting for playing positions, the five players with the highest point totals make the first team, the next five make the second team, and the remaining five the third team.

One of the most recent advances in assessing basketball performance is player-tracking technology [ 13 , 14 ]. This technology uses computer vision systems designed with algorithms capable of measuring the positions of players with a sampling rate around 25 frames per second [ 15 ]. Of course, kinematic variables such as distance, velocity or acceleration may be derived from these data, and sampling frequencies might improve in future [ 16 ]. Currently, the tracking technology is being used with data obtained from notational analysis providing combined information about sports performance; for example, by analyzing the distance covered by players when the team is attacking and when the same team is defending. Research in basketball using positional-derived variables however is limited at present to small samples of young basketball players examining physical demands [ 17 ], effects of defensive pressure on movement behavior [ 18 ], and how tactical performances are affected by activity workload [ 19 ].

These new tracking data open up possibilities that advance understanding of game performance by embracing a more holistic approach to analyzing sports behavior. For example, movement patterns (kinematics) from tracking data complement variables from the physiological (e.g., work rate), technical (e.g., actions) and tactical (e.g., individual/team behavioural patterns) domains leading to a more complete description and understanding of sports behavior in its entirety. As noted, an issue to address in this study using the large amounts of tracking data at hand concerns different basketball game performance profiles for different players and teams. That is, to categorize individual player performances into like groupings for use as baseline reference for the future development and preparation of players. The aim of the present study then is twofold: (i) to compare basketball game performances from the all-star and non all-star players, and (ii) to identify and describe the different basketball game performance profiles based on different game roles in the NBA.

Regarding the first aim, it was hypothesized that all-star players will outperform the non all-stars in game statistics. Therefore, the player performances on an actions-by-minute of play basis were compared, in aim of identifying performance variables that discriminate between the two separate groups of players. It is expected that all-star players should outperform the non all-star players in their performance statistics, particularly in scoring and passing related variables, as these important variables are thought to place higher demands on anticipatory processes [ 20 – 22 ]. In the second aim it was hypothesized that player performance profiles will present similarities and dissimilarities that can be used to identify different groups of players based on playing position. This aim is accomplished by using actions-per-game, in order to identify different groups of player performances, regardless of minutes of play in the games, thereby identifying those performance variables that discriminate between different player groupings.

Finally, it is important to describe the data within these performance-based groupings according to the players (all-star vs. non all-star) and playing positions. For example, some groups might have strong presence from all-star players and other groups might comprise both all-star and non all-star players from specific positions. This information can be useful when used in planning representative tasks in practice sessions, thereby fine-tuning playing behaviors in competition by using representative tasks in training [ 23 , 24 ]. In fact, players are often divided in practice into smaller groups according to specific positions as well as their playing standard. Non-starting players, for example, lack the same amount of playing time as starting players, and this competitive playing deficit likely affects their responses to competition throughout the season [ 21 , 25 ]. It follows that a detailed description of these different performance profiles using available objective measures would serve as an appropriate performance baseline for optimizing practice planning and, ultimately, for improving game performance.

Sample and variables

Archival data were obtained from open-access official NBA records for 1230 games played during the 2013–2014 regular season (available at http://stats.nba.com , these records contained both non-tracking and tracking data). A total of 30 teams played 82 games between October 29, 2013 and April 16, 2014. The gathered database had records of game performances from 548 players. The cases of player transfer between teams were counted as two different records.

• Pull-up shots: any jump shot outside 10 feet where a player took one or more dribbles before shooting. Gathered variables include pull-up points per game (PPG) or minute (PPM), field-goal percentage (FG%) and 3-point field-goal percentage (3FG%).
• Catch and shoot: any jump shot outside of 10 feet where a player possessed the ball for two seconds or less and took no dribbles. Gathered variables include catch and shoot PPG or PPM, FG% and 3FG%.
• Close shots: any jump shot taken by a player on any touch that starts within 12 feet of the basket, excluding drives. Gathered variables include close PPG or PPM and FG%.
• Drives: any touch that starts at least 20 feet of the hoop and is dribbled within 10 feet of the hoop and excludes fast breaks. Gathered variables include drives PPG or PPM and FG%.
• Passing-variables: the total number of passes a player makes and the scoring opportunities that come from those passes, whether they lead directly to a teammate scoring a basket (assists) or free throw (free-throw assists), or if they set up an assist for another teammate (secondary assists). Gathered variables also include total assists opportunities and total points created by assists.
• Touches-variables: the number of times a player touches and possesses the ball (touches per game), where those touches occur on the court (front, close or elbow), how long the player possessed the ball (time of possession), and the number of points per touch or per half-court touch. Gathered variables also include blocks, steals and the opponent field goals made at the rim while being defended.
• Speed and distance: variables that measure the distance covered (expressed in miles) and the average speed of all movements (expressed in miles per hour) by a player while attacking or defending.
• Rebounds: the number of rebounds secured (rebounds), the times when the player was within the vicinity (3.5 feet) of a rebound (chances), the number of rebounds a player recovers compared to the number of rebounding chances available (percentage chances) as well as if the rebound was uncontested by an opponent (uncontested). These variables were gathered either for defensive and offensive rebounds.
• Free-throw percentage: the number of free-throws made divided by the number of free-throws attempted.

Video footage from the entire court was unavailable making assessment of the NBA tracking data impossible. The NBA non-tracking data (e.g., assists, steals or defensive rebounds) however was assessed for reliability as follows. Two games were selected at random and analyzed conjointly through systematic observation by two experts. The minimum Cohen’s κ value for all variables exceeded 0.91 demonstrating high inter-rater reliability [ 26 ] between the NBA non-tracking data and the two experts.

Data analysis

Variables expressed as counts per game were divided by average minutes played. Records were screened for univariate outliers (cases outside the range Mean ± 3SD) and distribution tested, together with advised assumptions for each following inferential analysis [ 27 ]. To identify which variables best predict the player category (i.e., all-star vs. non all-star), the performance per minute of play was analyzed using a descriptive discriminant analysis. Structure coefficients greater than |0.30| were interpreted as meaningful contributors for discriminating between the two groups [ 27 ]. Validation of discriminant models was conducted using the leave-one-out method of cross-validation [ 28 ]. Also, a k-means cluster analysis was performed on the entire sample with the aim of creating and describing maximal different groups of game performance profiles. The cubic clustering criterion, together with Monte Carlo simulations, was used to identify the optimal number of clusters, thereby avoiding using subjective criteria. This statistical technique requires that all cases have no missing values in any of the variables introduced in the model; there were a total of 339 cases meeting this condition (62%). Afterwards, a descriptive discriminant analysis was performed to identify which of the variables best predicts the playing clusters.

One-way independent measures ANOVA was used to compare the variables not selected in the discriminant models (i.e., points scored per game and minutes played). Tukey post-hoc homogeneous subsets were used to describe post-hoc results. Statistical significance was set at 0.05 and calculations were performed using JMP statistics software package (release 11.0, SAS Institute, Cary, NC, USA) and SPSS software (release 22.0, SPSS Inc., Chicago, IL).

Comparing all-star and non all-star players

The means and standard deviations from the variables according to the all-star vs. non all-star categories are presented in Table 1 . The most important variables for differentiating all-star and non all-star performances per minute of play were identified using discriminant analysis. The obtained function was statistically significant (p≤0.001) with a canonical correlation of 0.59 (Λ = 0.65) and reclassification of 97.2%. The structure coefficients (SC) from the function reflected emphasis on elbow touches (SC = 0.43), defensive rebounds (SC = 0.35), close touches (SC = 0.34), close points (SC = 0.33), pull-up points (SC = 0.33) and speed in defense (SC = -0.33) (see Table 1 ). There were six cases misclassified (60.0% accuracy) in the all-star group and seven cases misclassified (97.8% accuracy) in the non all-star group, therefore, the obtained mathematical model shows high accuracy in classifying the players into their original groups.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

The variables expressed as counts were divided by minutes played.

https://doi.org/10.1371/journal.pone.0132894.t001

Figs 1 and 2 present the distribution from the discriminant scores in each group of players. The all-star players presented higher mean scores when compared to non all-star players (3.04±1.45 and -0.13±0.87, respectively).

https://doi.org/10.1371/journal.pone.0132894.g001

https://doi.org/10.1371/journal.pone.0132894.g002

Describing different game performance profiles

The cubic clustering criterion (CCC) along with Monte Carlo simulations was used to identify the optimal number of clusters. The largest value (CCC = 252.6) was obtained for a model of seven clusters. Therefore, a k-means cluster analysis was performed to create and describe seven maximal different groups of performance profiles per game. The means and standard deviations from the variables according to the cluster solutions are presented in Table 2 . The discriminant analysis revealed four statistically significant functions (p≤.001), however, the first two yielded a total of 94.7% from the total variance, with canonical correlations of 0.98 and 0.88, respectively. The reclassification of the cases in the original groups was very high (96.2%).

The variables expressed as counts are averages per game.

https://doi.org/10.1371/journal.pone.0132894.t002

The structure coefficients from the functions are presented in Table 2 . The first function had stronger emphasis on total distance covered in offense (SC = 0.83) and defense (SC = 0.80), whereas the second function was emphasized by performance obtained in passing-related variables (see Table 2 ).

Table 3 presents the differences between clusters in points scored per game, minutes played and distance from each case (player) to cluster centroid. The clusters 2 and 4 had more playing minutes and points per game. The clusters 1 and 5 were the most homogeneous, as identified in smaller distances to group centroid. In addition, player distributions in the seven clusters were contrasted against player category, presence in the NBA first team, and specific court position of players. The all-star players were grouped in clusters 2, 3 and 4. The NBA first team was grouped in clusters 2 and 4.

https://doi.org/10.1371/journal.pone.0132894.t003

Fig 3 presents the territorial map from the cases and created clusters within the space from the first and second discriminant functions. Players from clusters 4 and 2 exhibited better overall performances, however, players from cluster 6 also performed well in variables related to function 2.

https://doi.org/10.1371/journal.pone.0132894.g003

This study aimed to compare game performances of all-star and non all-star basketball players and to identify and describe different basketball game performance profiles in the NBA. In general terms, key performance indicators were identified that discriminate all-star players from non all-stars and, also, the different groupings of performance profiles in competition.

As expected, all-star players outperformed non all-star players in performance statistics, particularly in defensive rebounds, close touches and close points, pull-up points and assists. (Note. These results may be confounded in that the distinction between all-star and non all-star players is determined by sportswriters and broadcasters. This said, discrimination between these prejudged player groups is reflected in some game performance variables as reported in this study.)

Noted previously, the variables obtained from the tracking systems allow use of court locations for better understanding several game statistics. Therefore, these results increase knowledge of basketball game behavior by identifying key performance variables and by reducing prior emphasis on the importance of distance covered and velocity. The reclassification obtained was very high and hence affirms accuracy of the mathematical model.

The close touches and points were identified as key variables, suggesting that all-star players performed consistently better than non all-star players within 12 feet of the basket. These court locations are highly concentrated with teammates and opponents with frequent physical contact between players. These complex actions require high anticipatory skills [ 29 ] and all-star players outperform non all-stars in producing these complex skills under extreme adverse conditions [ 20 – 22 ]. Also, related with these findings, all-star players demonstrated the ability to score pull-up points, again showing how well these players perceive environmental information and adapt their behavior accordingly [ 30 , 31 ], as they strive to reach a better position from which to score (oftentimes using one or more dribble actions before shooting, for example). Several studies from basketball [ 32 ], football [ 33 ] and futsal [ 34 ] analyzing space-time dynamics of player dyads inform how the formation of playing patterns are influenced by scoring targets (i.e., baskets and goals). This higher ability to perceive the environment requires a developed attention span [ 35 , 36 ], perhaps evidenced in the higher number of assists given that assists constitute passes to a teammate leading directly to a subsequent field goal.

The distance covered and average speeds were not discriminant variables between the all-star and non all-star players. Until the availability of recent technology, getting reliable time motion data in basketball games has been difficult to acquire and, as such, low accuracy in the measures reported and/or small sample sizes have been a concern since early times [ 37 ]. The present results however provide measures of distance and velocity from an entire NBA season that are considered reliable [ 13 , 14 ], despite the 25 Hz sampling frequency limitation [ 16 ]. Although discriminant analysis only emphasized velocity in defense, there seems to be a tendency for all-star players to cover slightly shorter distances at lower average velocities. This might be important in that it is consistent with previous observations on the enhanced attunement of players to perceive affordances [ 38 , 39 ]. Thus, all-star players may well make less mistakes when deciding when and where to run in both offense and defense, possibly taking shorter paths to reach their destinations. These fewer mistakes in a game might well result in lower distances covered by these players. In addition, these considerations might also suggest that all-star players are more efficient, having less energy demands placed on them during a game. In fact, research suggests that motor efficiency achieved through intensive training, leads to improved perception, focus, anticipation, planning and fast responses [ 40 ]. The finding of lower defensive velocities for all-star players may reinforce this observation, but might also suggest that these players might be focusing their efforts more on offensive performances, as they are more complex and depend more upon their high level expertise [ 22 , 41 ].

The results reported different performance profiles for different player groupings. There were seven different groups identified by the analysis, obtaining very high reclassifications of the cases (96.2%). These groupings, based on total distance covered in the season and performance per game, might be used in developing specific playing profiles that, taking into consideration the influence of individual differences and functional variability, may serve as baseline to facilitate optimizing practice planning and game performance.

The clusters 2, 3 and 4 performed best at discriminant variables from function 1 (78.3% of total variance) and they contained all of the all-star players. These players participated in more than 30 minutes per game and scored many points per game (from 12.8±3.4 to 17.8±6.3). As an effect of these higher playing times, the most discriminant variables of this function were the distances covered either in defense or offense. Other discriminant variables included participation in offense (touches and front court touches) and passing-related variables (passes, assists, secondary assists, assists opportunities and points created by assists). There are also unique traits from each cluster that could be used to optimize the training process. For example, due to their high playing times in game competition, players from cluster 2 are likely high conditioned players, however, they should also give the most concern for coaches when planning recovery time between games [ 42 ]. Conversely, players from cluster 4 comprised all guards or shooting guards with extremely high values from time of possession, touches per game or passing-related variables. This is key information for coaches to optimize representative task designs that enable players to perceive adequate environmental information and to subsequently act accordingly [ 25 , 30 , 43 ]. Finally, players from cluster 3 demonstrated less possession time and touches, despite the higher minutes of play, which suggests a predominant defensive role for these players. The defensive tasks are particularly related to player fitness variables as high-level defensive performances seem to require higher energy demands [ 44 ] and these kind of tasks are therefore particularly related to player fitness variables.

In addition, the worst performance variables in function 1 belong to players from cluster 1, as they exhibited lower playing times (12.6±5.0) distributed equally on playing position. In fact, the most unclear player positions (and missing values), in reference to players that can play in several different positions, were grouped in this cluster. Therefore, these results might be suggesting a profile of an all-round player that can be used in a game to serve multiple purposes, or a profile of a very specialized player (e.g. in shooting or rebounding). Together with workload compensation of reduced playing time, coaching staffs can modify the tasks to optimize the performance produced by these all-round players or specialists.

When adding the results from the second discriminant analysis function, clusters 4 and 6 emerge as active performers in the analyzed variables, such as time in possession, touches, passing, pull-up points and drives per game. These results confirm the guards profile (in cluster 4) identified previously, and also for players in cluster 6. In fact, there is an important requirement to adjust the tasks required of these players in order to fine-tune the environmental information necessary for information pick-up in game play [ 30 , 45 ]. From the same perspective, players from cluster 3, identified as defensive-related, demonstrate less activity in these variables, consistent with their roles in the game.

Conclusions

In summary, this study provided analysis of an NBA regular season using player-tracking variables and notation data. It was found that all-star players performed consistently better than non all-star players within 12 feet of the basket, possibly a result of optimized attention processes that are key for perceiving the required appropriate environmental information for action production. In addition, different groupings were identified based on playing performance, particularly in relation to the roles of scoring, passing, defensive and all-round duties. These findings can be used to optimize preparation for individual player groupings and, ultimately, improve game performances of the players and teams.

Acknowledgments

This study was part of a project registered at the Portuguese Foundation for Science (FCT, PEst-OE/SAU/UI4045/2015).

Author Contributions

Conceived and designed the experiments: JS TM. Performed the experiments: JS TM JC SJ XS MB. Analyzed the data: JS TM. Contributed reagents/materials/analysis tools: JS TM. Wrote the paper: JS TM JC SJ XS MB.

• View Article
• Google Scholar
• 3. Gibson J. The ecological approach to visual perception. Boston: Houghton Mifflin; 1979. 332 p.
• 4. Savelsbergh G, Davids K, van der Kamp J, Bennett SJ. Development of Movement Coordination in Children: Applications in the Field of Ergonomics, Health Sciences and Sport: Taylor & Francis; 2013.
• 5. Kauffman SA. The Origins of Order: Self Organization and Selection in Evolution: Oxford University Press; 1993.
• PubMed/NCBI
• 12. NBA.com. MVP Nash Highlights All-NBA First Team 2006 [April 7, 2015]. Available from: http://www.nba.com/news/AllNBA_060517.html .
• 13. Maheswaran R, Chang Y-H, Henehan A, Danesis S. Deconstructing the Rebound with Optical Tracking Data. MIT Sloan Sports Analytics Conference 2012. 2012.
• 14. Goldsberry K, Weiss E. The Dwight Effect: A New Ensemble of Interior Defense Analytics for the NBA. MIT Sloan Sports Analytics Conference 2012. 2012.
• 23. Duarte A, Davids K, Chow J, Passos P, Raab M. The development of decision making skill in sport: An ecological dynamics perspective. In: Duarte A, Hubert R, editors. Perspectives on Cognition and Action in Sport. United States of America: Nova Science Publishers, Inc., Suffolk; 2009. p. 157–69.
• 26. O'Donoghue P. Research Methods for Sports Performance Analysis. London: Routledge; 2010. 278 p.
• 27. Pedhazur E. Multiple Regression in Behavioral Research. Holt RW, editor. New York1982.
• 28. Norusis M. SPSS 13.0 Guide to Data Analysis. Upper Saddle-River, N.J.: Prentice Hall, Inc.; 2004.

ORIGINAL RESEARCH article

Long-term analysis of elite basketball players’ game-related statistics throughout their careers.

• 1 Sport Science Department, Universidad Politécnica de Madrid, Madrid, Spain
• 2 Institute of Sport Science and Innovations, Lithuanian Sports University, Kaunas, Lithuania

The aim of the present study was to analyze the changes of game-related statistics in expert players across their whole sports careers. From an initial sample including 252 professional basketball players competing in Spanish first division basketball league (ACB) in the 2017–2018 season, 22 met the inclusion criteria. The following game-related statistics were studied: average points, assist, rebounds (all normalized by minute played), 3-point field goals percentage, 2-point field goals percentage, and free throws percentage per season. Each variable was individually investigated with a customized excel spreadsheet assessing individual variations and career trends were calculated. The results showed a positive trend in most of the investigated players in assists (91% of cases) and free throw percentages (73% of cases). Similar percentages of positive and negative trends were observed for all the other game-related statistics (range: 41–59% for negative and positive, respectively). In conclusion, an increase in assist and free throw performance was shown in the investigated players across their playing career. This information is essential for basketball coaches suggesting the use of most experienced players in the final moments of the game.

Introduction

Basketball is a team sport characterized by the execution of series of skills in multiple situations occurring across the game. In particular, game-related statistics are fundamental and their level might depend on the players’ characteristics and training experience. Most of the game related statistics depends on multifactorial variables (i.e., offensive and defensive tactics) determining a complex dynamic system during games, which is difficult to control in its totality. The use of performance analysis in sport with the determination of the most important game related statistics during the game aims to improve the team performance, increasing the knowledge of the performance of each player. Specifically, game-related statistics are key tools for basketball coaches providing reliable information about teams’ performance such as those distinguishing between successful and unsuccessful teams. Previous investigations widely studied the game-related statistics mostly assessing team performance in order to determine the most valuable players and the importance of certain positions such as guards, forward and centers (e.g., Sampaio et al., 2006a ), to evaluate the impact of rule changes (e.g.; Gómez et al., 2006a ; Ibáñez et al., 2018 ), the effect of home advantage (e.g.; Carron et al., 2005 ; Pollard, 2008 ; Watkins, 2013 ), the importance of starters and bench players regarding their contribution to the game (e.g.; Sampaio et al., 2006b ), the scoring strategies differentiating between winning and losing teams in women’s basketball FIBA Eurobasket (e.g.; Conte and Lukonaitiene, 2018 ). It is important to note that in basketball several game related statistics have been used, while only some of them were deemed fundamental. Previous discriminant analyses quantitatively determined the team performance indicators (TPI), identified as a variable able to define the most important aspect of performance ( Hughes and Bartlett, 2002 ) and compare different leagues ( Sampaio and Leite, 2013 ), which most affect the game outcome ( Gomez et al., 2008 ; Ibánez et al., 2008 ). In particular, Yu et al. (2008) , established a list of the most influential TPI’s (Technical Performance Indices) such as points per game (PPG), field goals made (FGM), rebounds, assists, turnovers, blocks, fouls, and steals. Sampaio et al. (2013) included also free throws as an important technical performance indicator. The TPIs with the most impact on the outcome of a season in Spanish first division (ACB) teams were shooting percentage (both 2-point and 3-point percentage), assists and rebounds ( García et al., 2013 ; Gómez et al., 2008 ). However, to the best of our knowledge, no previous investigations assessed players’ individual game related statistics across a long period of time. Indeed, players’ experience might play a fundamental role in improving players’ game related statistics effectiveness. Therefore, studies addressing this topic are warranted.

The performance of a player across his career might play a fundamental role in distinguishing between elite and non-elite players. Indeed, acquiring playing experience, players could have a better performance due to the demand of basketball game to perform complex actions that require high anticipatory skills in difficult situations. Indeed, these high anticipatory skills can be translated into scoring and passing related variables concerning about game-related statistics ( Sampaio et al., 2015 ), and therefore they become an important variable deeming further analysis in basketball. In fact, elite players perceive better their environmental information and are capable of adapting their behavior accordingly and consequently perform better compared to other non-elite players ( Aglioti et al., 2008 ). Therefore, playing experience might be essential in increasing players’ anticipatory skills and consequently their game performance.

It has been previously showed that performance slowly decrease after reaching the peak period of the player career ( Baker et al., 2013 ). In basketball, Baker et al. (2013) , found that the typical basketball career lasts about 11 years, with the longest career studied being 23 years of playing at an elite level. However, it is not clear the performance changes across players career, and their trend (i.e., positive or negative) calling for further studies in this area. Therefore, the aim of this study was to descriptively analyze TPI changes throughout the career of expert basketball players, assessing the possible performance trend.

Materials and Methods

Participants.

From an initial sample of 252 professional basketball players competing in ACB, 22 players (9 backcourt and 13 frontcourt) were selected for this study based on the following inclusion criteria determined by a group of experts, who were identified according to Swann et al. (2015) guidelines: (a) male players, currently playing in the ACB league in the season 2017–2018; (b) to have a minimum playing experience of 10 years (including only season in which they effectively played) in the first division of any country with at least an average of 25% of number of games and minutes played per season; (c) to possess a minimum of 5 years playing experience in first division of any league amongst the top 30 countries in the FIBA Ranking (at February 28, 2018); (d) to have played at least 75% of their professional careers in any country’s first division league, consequently no years played in lower division leagues were analyzed. The aim of these criteria was to ensure the highest quality of the sample for expert players with a solid number of games and minutes played each season ( Swann et al., 2015 ).

The databases used to obtain the game related statistics of each season for the studied players were the ACB official web page 1 for any season played in the ACB league, and the RealGM website 2 , or the official ACB guide released by the Spanish Basketball Association for any season played outside Spain. These databases are normally used in studies related with basketball, and basketball statistics and are considered valid and reliable ( Gómez et al., 2018 ).

The following game-related statistics for each season were recorded and analyzed: average points, assist, rebounds, 3-point field goals percentage, 2-point field goals percentage and free throws percentage per season. The variable point, assists and rebounds were normalized by minute played with the following formula (example for points scored: mean seasonal points scored/mean seasonal minute played ∗ 40 min). All the data for these game-related statistics, for every season and every player included in this study were storage in a database and once they were used for the statistical analysis.

Statistical Analysis

All statistical analyses were performed with a customized excel spreadsheet specifically developed to monitor individual changes and trends in a rigorous quantitative way ( Hopkins, 2017 ). Recently, this excel spreadsheet has been adopted to assess individual changes in team sports ( Siahkouhian and Khodadadi, 2013 ; Loturco et al., 2017 ; Colyer et al., 2018 ; Hurst et al., 2018 ) and specifically in basketball ( Pliauga et al., 2018 ). This statistics approach could be used as a possible alternative to previously used methodologies such as the ANOVA factor ( Yu et al., 2008 ) or the Jonckheere–Terpstra test ( Leite and Sampaio, 2012 ). The individual trends across playing career for each investigated player were then quantified and the percentage of players documenting a positive, negative or steady (when the result is zero) slope were calculated using the following formula y = m⋅x+n. Figures 1 – 6 are an example of the individual points and trendlines obtained via the Hopkins spreadsheet and that were later analyzed.

Figure 1. Individual trend of one participant for average points per season normalized by minute.

Figure 2. Individual trend of one participant for average rebounds per season normalized by minute.

Figure 3. Individual trend of one participant for average assists per season normalized by minute.

Figure 4. Individual trend of one participant for 3-point percentage per season.

Figure 5. Individual trend of one participant for 2-point percentage per season.

Figure 6. Individual trend of one participant for free throw percentage per season.

The mean slope for each performance indicator and the number of cases in which the slope was positive, steady, or negative are shown in Table 1 . Results revealed that most of the players have a positive trend in assists (91% of the cases) free throws (73% of the cases), and 3-point percentage although with a lower value (59%). Conversely, there were no differences of positive and negative trends reported for the other investigated parameters ( Table 1 ).

Table 1. Mean slope and number of cases of each variable.

The aim of this study was to analyze the trends TPIs throughout the career of expert basketball players. The results revealed that assists and free throws were the two TPIs mostly showing a positive trend during players’ careers. Specifically, the 91% of the studied players have a positive tendency in assists, with a mean slope of 0.15, and 73% of them have a positive tendency in free throws, with a mean slope of 0.95. Also, 59% of the players increase their 3-point percentage, but this result might have been influenced by the fact that more frontcourt than backcourt players met our inclusion criteria.

Basketball is a sport where situations change quickly and continuously as a result of the combination of factors such as the position of opponents in the field and their tactical behavior, the position of the ball and the timing of the offensive movements ( Altavilla and Raiola, 2014 ). Therefore, players are required to decide an appropriate response with a proper timing and executing it in a correct spacing. Often, players are subject to defensive pressure and the more skilled and experienced players might be able to anticipate events and perform unhurried actions as a result of their improved ability to “read the game” ( Sampaio et al., 2004 ). In this context, executing a successful pass (i.e., assist) assume a fundamental importance in basketball. Indeed, when analyzing the mechanism of this technical action, the assist requires a combination of good decision making in court, coordination, anticipation, timing, and a good execution ( Melnick, 2001 ; Gómez et al., 2006b ). Previous research demonstrated that assists and free throw percentage are two of the most factors to win a game ( Dias, 2007 ; Gómez and Lorenzo, 2007 ; Sampaio et al., 2015 ). Moreover, Sampaio et al. (2004) , suggested that assists are indicators of players’ maturity and experience, increasing in number as the player gets a better ability to read the game due to the years of playing experience. The results of our investigation highlighted supporting results, since most of the investigated players increased their assist performance across their playing career. This information seems essential for basketball coaches, who can rely on the performance of more experienced basketball players characterized by a better tactical awareness in order to execute successful passes and increase the scoring possibilities during the game. Indeed, Melnick (2001) showed a positive correlation between number of assists of a team and a better win-loss record through a season.

Free throws have also been demonstrated to be performance indicators differentiating between winning and losing teams in particular in close games ( Ibánez et al., 2008 ; Conte et al., 2018 ; Gómez et al., 2018 ). Therefore, it was expected that players increasing their experience and possibly assuming a leadership and fundamental role in their team would increase their free throw performance during their career. Accordingly, our results demonstrated an increased trend across players’ career for free throws and therefore possibly increasing their teams’ possibility to be successful. In this sense, experience accumulated in games and practices is the most crucial factor for developing expertise in one aspect ( Gómez et al., 2018 ). An increase in the percentages of free throws can be associated with the fact that players have already mastered the shooting during their years of training. Interestingly, a previous investigation showed that free throws shooting trajectories are more efficient and possess a lower variability in more experienced players compared to less experienced players ( Button et al., 2003 ). The practical application of our result is that coaches should favor the participation of most experienced players in last minutes of close games, when usually there are higher number of fouls generating free throws opportunities.

Other variables such as points, rebounds, and 2-point percentage did not show any trend increase across the players’ career. A possible reason for this finding is that these variables might be more influenced by physical factors (i.e., strength, power, and fitness), which showed a decrease during the lifespan ( Horton et al., 2008 ). Even though experienced players compensate this decrease in their physical abilities with a better understanding of the games’ tactical aspects, better timing and spacing and better decision-making abilities, it seems not enough to show a positive trend according to the results of our investigation.

Although this investigation provides basketball coaches with useful information, some limitations should be mentioned. Firstly, the results might have been influenced by some confounding factors such as injuries across the season, the playing status (i.e., starting vs. bench players), economical aspects such as players’ contracts and players and/or coaching staffs changing teams during investigated period. Therefore, future studies are warranted in order to overcome these limitations possibly controlling these factors. However, to the best of our knowledge, this investigation provides the first evidence about the individual trend in players’ performance across their playing career and notably increase the knowledge in this field. Moreover, further studies should be designed in order to assess players’ individual season-by-season changes across their playing career.

The results of this investigation suggest that as the players acquire years of experience in first division elite teams, their assists per game and free throw percentage increase. Conversely, other game-related statistics such as points, rebounds, 3-point percentage, and 2-point percentage showed both positive and negative trends in the investigated players resulting in a high between players variability. Finally, further research is required in this field using an individualized approach to increase the knowledge about players’ performance across their playing career.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• ^ http://www.acb.com
• ^ https://basketball.realgm.com

Aglioti, S., Cesari, P., Romani, M., and Urgesi, C. (2008). Action anticipation and motor resonance in elite basketball players. Nat. Neurosci. 11, 1109–1116. doi: 10.1038/nn.2182

PubMed Abstract | CrossRef Full Text | Google Scholar

Altavilla, G., and Raiola, G. (2014). Global vision to understand the game situations in modern basketball. J. Phys. Educ. Sport 14, 493–496.

Google Scholar

Baker, J., Koz, D., Kungl, A. M., Fraser-Thomas, J., and Schorer, J. (2013). Staying at the top: Playing position and performance affect career length in professional sport. High Ability Stud. 24, 63–76. doi: 10.1080/13598139.2012.738325

CrossRef Full Text | Google Scholar

Button, C., Macleod, M., Sanders, R., and Coleman, S. (2003). Examining movement variability in the basketball free-throw action at different skill levels. Res. Q. Exerc. Sport 74, 257–269. doi: 10.1080/02701367.2003.10609090

Carron, A. V., Loughhead, T. M., and Bray, S. R. (2005). The home advantage in sport competitions: Courneya and Carron’s (1992) conceptual framework a decade later. J. Sports Sci. 23, 395–407. doi: 10.1080/02640410400021542

Colyer, S. L., Stokes, K. A., Bilzon, J., Holdcroft, D., and Salo, A. I. T. (2018). The effect of altering loading distance on skeleton start performance: is higher pre-load velocity always beneficial? J. Sports Sci. 36, 1930–1936. doi: 10.1080/02640414.2018.1426352

Conte, D., and Lukonaitiene, I. (2018). Scoring Strategies Differentiating between Winning and Losing Teams during FIBA EuroBasket Women 2017. Sports 6:50. doi: 10.3390/sports6020050

Conte, D., Tessitore, A., Gjullin, A., Mackinnon, D., Lupo, C., and Favero, T. (2018). Investigating the game-related statistics and tactical profile in NCAA division I men’s basketball games. Biol. Sport 35, 137–143. doi: 10.5114/biolsport.2018.71602

Dias, J. M. (2007). The stats value for winning in the world basketball championship for men 2006. Fit. Perform. 6, 57–61. doi: 10.3900/fpj.6.1.57.e

García, J., Ibáñez, S. J., Martínez, R., Leite, N., and Sampaio, J. (2013). Identifying Basketball Performance Indicators in Regular Season and Playoff Games. J. Hum. Kinet. 36, 163–170. doi: 10.2478/hukin-2013-0016

Gómez, M. A., Avugos, S., Oñoro, M. A., Lorenzo, A., and Bar-Eli, M. (2018). Shaq is not alone: free-throws in the final moments of a basketball game. J. Hum. Kinet. 62, 135–144. doi: 10.1515/hukin-2017-0165

Gómez, M. A., and Lorenzo, A. (2007). Análisis discriminante de las estadísticas de juego entre bases, aleros y pívots en baloncesto masculino. Apunts 87, 86–92.

Gómez, M. A., Lorenzo, A., Barakat, R., Ortega, E., and Palao, J. M. (2008). Differencesin game-related statistics of basketball performance between men’s winning and losing teams according to game location. Percept. Mot. Skills 106, 98–107. doi: 10.2466/pms.106.1.43-50

Gómez, M. A., Lorenzo, A., Sampaio, J., and Ibáñez, S. J. (2006a). Differences in game-related statistics between winning and losing teams in women’s basketball. J. Hum. Mov. Stud. 51, 357–369.

Gómez, M. A., Tsamourtzis, E., and Lorenzo, A. (2006b). Defensive systems in basketball ball possessions. Int. J. Perform. Anal. Sport 6, 98–107. doi: 10.1080/02640414.2013.792942

Gomez, M. A., Lorenzo, A., Sampaio, J., Ibanez, S. J., and Ortega, E. (2008). Game related statistics that discriminated winning and losing teams from the Spanish men’s professional basketball teams. Coll. Antropol. 32, 451–456.

Hopkins, W. G. (2017). A Spreadsheet for Monitoring an Individual’s Changes and Trend. Sportscience 21, 5–9.

Horton, S., Baker, J., and Schorer, J. (2008). Expertise and aging: maintaining skills through the lifespan. Euro. Rev. Aging Phys. Act. 5, 89–96. doi: 10.1007/s11556-008-0034-5

Hughes, M. D., and Bartlett, R. M. (2002). The use of performance indicators in performance analysis. J. Sports Sci. 20, 739–754. doi: 10.1080/026404102320675602

Hurst, C., Batterham, A. M., Weston, K. L., and Weston, M. (2018). Short and long-term reliability of leg extensor power measurement in middle-aged and older adults. J. Sports Sci. 36, 970–977. doi: 10.1080/02640414.2017.1346820

Ibáñez, S. J., García-Rubio, J., Gómez, M. A., and González-Espinosa, S. (2018). The Impact of Rule Modifications on Elite Basketball Teams’ Performance. J. Hum. Kinet. 64, 181–193. doi: 10.1515/hukin-2017-0193

Ibánez, S. J., Sampaio, J., Feu, S., Lorenzo, A., Gómez, M. A., and Ortega, E. (2008). Basketball game related statistics that discriminate between team’s season-long success. Euro. J. Sport Sci. 8, 369–372. doi: 10.1080/17461390802261470

Leite, N. M. C., and Sampaio, J. E. (2012). Long-Term Athletic Development across Different Age Groups and Gender from Portuguese Basketball Players. Int. J. Sports Sci. Coach. 7, 285–300. doi: 10.1260/1747-9541.7.2.285

Loturco, I., Pereira, L. A., Kobal, R., Kitamura, K., Cal Abad, C. C., Marques, G., et al. (2017). Validity and Usability of a New System for Measuring and Monitoring Variations in Vertical Jump Performance. J. Strength Cond. Res. 31, 2579–2585. doi: 10.1519/JSC.0000000000002086

Melnick, M. J. (2001). Relationship between team assists and win-loss record in the National Basketball Association. Percept. Mot. Skills 92, 595–602. doi: 10.2466/pms.2001.92.2.595

Pliauga, V., Lukonaitiene, I., Kamandulis, S., Skurvydas, A., Sakalauskas, R., Scanlan, A., et al. (2018). The effect of block and traditional periodization training models on jump and sprint performance in collegiate basketball players. Biol. Sport 35, 373–382. doi: 10.5114/biolsport.2018.78058

Pollard, R. (2008). Home Advantage in football: a current review of an unsolved puzzle. Open Sports Sci. J. 1, 12–14. doi: 10.2174/1875399X00801010012

Sampaio, J., Ibáñez, S., and Lorenzo, A. (2013). “Basketball,” in Routledge Handbook of Sport Performance Analysis , eds T. McGarry, P. O’Donoghue, and J. Sampaio (London: Routledge), 357–366.

Sampaio, J., Ibáñez, S. J., and Feu, S. (2004). Discriminative power of basketball game-related statistics by level of competition and sex. Percept. Mot. Skills 99, 1231–1238. doi: 10.2466/pms.99.3f.1231-1238

Sampaio, J., Ibáñez, S., Lorenzo, A., and Gomez, M. (2006a). Discriminative game related statistics between basketball starters and nonstarters when related to team quality and game outcome. Percept. Mot. Skills 103, 486–494.

PubMed Abstract | Google Scholar

Sampaio, J., Janeira, M., Ibáñez, S., and Lorenzo, A. (2006b). Discriminant analysis of game-related statistics between basketball guards, forwards and centres in three professional leagues. Euro. J. Sport Sci. 6, 173–178. doi: 10.1080/17461390600676200

Sampaio, J., and Leite, N. (2013). “Performance Indicators in Game Sports,” in Routledge Handbook of Sports Performance Analysis (1st ed.) , eds T. McGarry, P. O’Donoghue, and J. Sampaio (New York, NY: Routledge Handbooks).

Sampaio, J., McGarry, T., Calleja-González, J., Jiménez Sáiz, S., Schelling i del Alcázar, X., and Balciunas, M. (2015). Exploring Game Performance in the National Basketball Association Using Player Tracking Data. PLoS One 10:e0132894. doi: 10.1371/journal.pone.0132894

Siahkouhian, M., and Khodadadi, D. (2013). Narita target heart rate equation underestimates the predicted adequate exercise level in sedentary young boys. Asian J. Sports Med. 4, 175–180. doi: 10.5812/asjsm.34255

Swann, C., Moran, A., and Piggott, D. (2015). Defining elite athletes: issues in the study of expert performance in sport psychology. Psychol. Sport Exerc. 16, 3–14. doi: 10.1016/J.PSYCHSPORT.2014.07.004

Watkins, P. (2013). Revisiting the Home Court Advantage in College Basketball. Int. J. Sport Soc. 3, 33–42. doi: 10.18848/2152-7857/CGP/v03i01/53892

Yu, K., Su, Z., and Zhuang, R. (2008). An Exploratory Study of Long-Term Performance Evaluation for Elite Basketball Players. Int. J. Sports Sci. Eng. 2, 195–203.

Keywords : evolution, statistics, tendencies, career, team sports, professional

Citation: Lorenzo J, Lorenzo A, Conte D and Giménez M (2019) Long-Term Analysis of Elite Basketball Players’ Game-Related Statistics Throughout Their Careers. Front. Psychol. 10:421. doi: 10.3389/fpsyg.2019.00421

Received: 30 November 2018; Accepted: 12 February 2019; Published: 27 February 2019.

Reviewed by:

Copyright © 2019 Lorenzo, Lorenzo, Conte and Giménez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jorge Lorenzo, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Share full article

Advertisement

Supported by

At N.Y.U., Explaining an Unraveling World Through Basketball

A professor thought he had created a class that could explore society’s fissures through a single sport. Then the pandemic struck, and basketball became more relevant than ever.

By Kevin Armstrong

Shortly after New York University honored David Hollander, an assistant dean, with the college’s highest faculty honor in March 2019, he turned his attention on fine-tuning the syllabus for a new class that he had wanted to teach for 15 years.

Hollander, 55, viewed the world as broken. Having identified deep fissures within politics, culture and commerce, he explained to his bosses that his ideal course would explore all these elements — through the prism of a game.

“I want to elevate the study of basketball to the same plane as a science or history course,” he said.

Hollander named the course “How Basketball Can Save the World,” and posited that the sport, through its easy accessibility and global reach, offered distinct antidotes for modern issues. In all, he compiled 13 principles — a tribute to James Naismith’s 13 original basketball rules — for his proposed philosophy. Hollander’s tenets included, for example, applying basketball’s values of cooperation and balance to real-world issues like the evolving global economy and systemic racism.

Hollander, a quirky academic who wears black Chuck Taylors in the classroom, believed he had captured the zeitgeist of 2020, but he did not envision himself lecturing about his thoughts over Zoom amid a pandemic. When the N.B.A. suspended its season on March 11, Hollander watched as society followed the league’s lead after it shuttered to protect players, coaches and fans.

College learning went remote, and New York City removed rims from the basketball courts at public playgrounds to enforce social distancing. As scores of N.Y.U. students largely uprooted from Greenwich Village for their hometowns, whether in New Jersey or China, Hollander doubled down on his belief that basketball could be instrumental in reshaping the way the world worked.

“Here we are in this moment, and, my God, the world needs saving,” he said. “Every piece of life is being disrupted, reconfigured. Nobody knows what this will look like on the other side.”

When he looked at the future of work, he saw the value basketball places on players being “positionless” and its relevance in a gig economy. When he thought of urban planning, he saw the need for better spacing, highlighting basketball’s ability to thrive in city, suburban and rural settings. Above all, he saw basketball as a solution to isolation because of its low barrier to entry.

“Whatever remedy you have to save the world, it must be accessible or it cannot work,” Hollander said. “It cannot be elitist. James Naismith did not want basketball to be a country club sport or a hyper-commercialized sport. He didn’t even want coaches. He wanted it to be a sanctuary for the outsider.”

In 2020, Hollander saw parallels to the chaotic era when Naismith invented basketball in 1891 amid the Gilded Age. He did not foresee the pandemic, which forced him to adjust quickly.

He had his class watch the director Dan Klores’s short film on Magic Johnson and his impact on the public’s understanding of the AIDS epidemic in the 1990s from “Basketball: A Love Story.” He spoke about race and culture, agency and ownership, showing the students “ High Flying Bird ,” a movie about an agent negotiating his way through a professional basketball lockout. He cued up songs like Kurtis Blow’s “Basketball” to demonstrate the sport’s cultural impact.

The guest list included Big East Commissioner Val Ackerman and the N.B.A. deputy commissioner Mark Tatum. The former Nike marketing executive Mark Thomashow helped guide an exploration of culture and commerce, as well as the grift and graft of the sport’s grass-roots scene. Walt Frazier, a member of the Naismith Memorial Basketball Hall of Fame and a Knicks broadcaster, appeared via Zoom, welcoming the opportunity to engage with others while Manhattan was locked down.

“I haven’t gone out in five weeks!” Frazier said when he appeared. “I had to spiff myself up for class.”

One guest was fresh off serving 90 days in federal prison: Emanuel Richardson, the former University of Arizona assistant coach who pleaded guilty to conspiracy to commit bribery during a prospect’s recruitment. After a discussion about the merits of amateurism and the role of Black assistant coaches in college recruiting, one student asked him about his relationship with basketball after incarceration.

“Did I fall out of love with it? Yes,” Richardson said. “I hated basketball, but it wasn’t basketball. It was people. Because basketball is still pure. Basketball is still one of the sweetest joys.”

Hollander conceded that soccer is the world’s most popular sport, but he asserted that basketball was its most influential. In 2004, he read Franklin Foer’s “ How Soccer Explains the World: An Unlikely Theory of Globalization ,” and agreed with its examination of a new consciousness through the perspective of a global sport.

He believed basketball players had showed their ability to reach a wide audience when drawing attention to social causes. LeBron James and others wore “I Can’t Breathe” T-shirts when Eric Garner was killed by police in Staten Island. Maya Moore, the W.N.B.A.’s top player, left the league last year to work on behalf of Jonathan Irons, a wrongfully convicted man who walked free from a Missouri prison in July after Moore and his team argued for his release. Atlanta Dream players in August wore shirts supporting the candidate opposing Senator Kelly Loeffler, Republican of Georgia, a co-owner of the Dream who had spoken disparagingly of the Black Lives Matter movement.

“Basketball in its highest form is a balance of self-interest and self-expression in service of the collective,” Hollander said. “It does not surprise me that it has been a leader in so many areas of social impact and social change.”

But while the N.B.A. returned to play in a bubble, the professional form of the game was far from utopia. Though the N.B.A. had shown some progressiveness when the Nets signed Jason Collins , the first openly gay athlete in major American sports, and Commissioner Adam Silver dismissed the Clippers owner Donald Sterling for racist remarks, the delicacy of its business partnership with China came into view last October.

After Daryl Morey, the general manager of the Houston Rockets, posted support for pro-democracy protesters in Hong Kong on Twitter, Chinese sponsors cut ties with the league, and China Central Television, the state-run television broadcaster, refused to air games. A few months later, ESPN reported that coaches at NBA China academies complained of player abuse.

Hollander maintained that all levels of the game should abide by the principles of equality and equity in order to maintain Naismith’s ideals.

The conversations are continuing, though still not exactly how Hollander originally planned. Many N.Y.U. students were back in the city by late August. Others stayed home, like Alessandro Gherardi, a student in Hollander’s class, who was in Bologna, Italy, when he signed in via Zoom for a scheduled talk.

It was an hourlong discussion with Nets wing Kevin Durant. After questions about his recovery from his Achilles’ tendon injury (he’s doing fine, he said), Gherardi asked Durant about how he used basketball to better understand the world and what “epiphanies, opportunities” he experienced because of the sport since growing up in Prince George’s County, Maryland.

Durant considered the way basketball had shaped his life experience as he traveled from game to game as a youth.

“I started to crave more and more of those experiences and I realized they were tied to the game,” he said. “I understood it was a way for me to learn more, see more and have more friends. Once I played the game for that small reason, the world started to open up for me. I’m just craving more and more experiences, more and more perspectives.”

Inside the World of Sports

Dive deeper into the people, issues and trends shaping professional, collegiate and amateur athletics..

What We Saw at Augusta: Golf enthusiasts regard a trip to the Masters as the stuff of dreams. Here are photos from this year’s tournament .

A Dizzying 3 Weeks: At times, Shohei Ohtani, baseball’s biggest star, seemed in danger of being tainted by a gambling scandal , before his longtime interpreter was charged with fraud.

A Soccer Team With Free Matches: When Paris F.C. made its tickets free, it began an experiment into the connection between fans and teams , and posed a question about the value of big crowds to televised sports.

Minor League Baseball’s Real Estate: The fight over a new stadium for the Eugene Emeralds  highlights a wider challenge for cheaper alternatives to big-league live sports.

New York’s Favorite Soccer Team: Some people splurge on vacations, fancy shoes and motorcycles. A group of dozens of friends, neighbors and co-workers decided to try something better (or maybe worse): They bought a middling soccer team in Denmark .

Here Comes Padel: The sport is played with a racket on a court with a net, but watch out for those bouncing shots from the back wall. Reporters take a look at the padel scene in New York City .

• Systematic Review
• Open access
• Published: 22 February 2022

Body Fat of Basketball Players: A Systematic Review and Meta-Analysis

• Pierpaolo Sansone 1 ,
• Bojan Makivic 2 ,
• Robert Csapo 3 ,
• Patria Hume 4 ,
• Alejandro Martínez-Rodríguez 5 &
• Pascal Bauer   ORCID: orcid.org/0000-0003-1867-2422 3  

Sports Medicine - Open volume  8 , Article number:  26 ( 2022 ) Cite this article

10k Accesses

16 Citations

21 Altmetric

Metrics details

This study aimed to provide reference values for body fat (BF) of basketball players considering sex, measurement method, and competitive level.

A systematic literature research was conducted using five electronic databases (PubMed, Web of Science, SPORTDiscus, CINAHL, Scopus). BF values were extracted, with analyses conducted using random-effects models and data reported as percentages with 95% confidence intervals (CI).

After screening, 80 articles representing 4335 basketball players were selected. Pooled mean BF was 13.1% (95% CI 12.4–13.8%) for male players and 20.7% (95% CI 19.9–21.5%) for female players. Pooled mean BF was 21.4% (95% CI 18.4–24.3%) measured by dual-energy X-ray absorptiometry (DXA), 15.2% (95% CI 12.8–17.6%) via bioelectrical impedance analysis (BIA), 12.4% (95% CI 10.6–14.2%) via skinfolds and 20.0% (95% CI 13.4–26.6%) via air displacement plethysmography. Pooled mean BF across competitive levels were 13.5% (95% CI 11.6–15.3%) for international, 15.7% (95% CI 14.2–17.2%) for national and 15.1% (95% CI 13.5–16.7%) for regional-level players. As the meta-regression revealed significant effects of sex, measurement method and competitive level on BF, the meta-analysis was adjusted for these moderators. The final model revealed significant differences in BF between male and female players ( p  < 0.001). BF measured by DXA was significantly higher than that measured by BIA or skinfolds ( p  < 0.001). International-level players had significantly lower BF than national and regional-level players ( p  < 0.05).

Conclusions

Despite the limitations of published data, this meta-analysis provides reference values for BF of basketball players. Sex, measurement method and competitive level influence BF values, and therefore must be taken into account when interpreting results.

This systematic review and meta-analysis found that body fat of basketball players differs according to players’ sex, competitive level as well as by the measurement method implemented

Female basketball players have higher body fat than male counterparts. International-level players have lower body fat than national and regional-level players. Across measurement methods, body fat values obtained by DXA are higher than those obtained via BIA and skinfolds.

Future studies reporting the body fat of basketball players should specify the reliability of measurement, clearly report the hydration and feeding status prior to measurement, specify the competitive level of the sample by reporting the country and/or region and name of the league in which players competed at the time of the study, and report body fat of players in distinct categories (i.e. sex, competitive level, playing position) for better interpretation of data.

Basketball is one of the most practiced team sports worldwide [ 1 ] and has been an Olympic discipline since 1936. The game is characterised by a highly intermittent profile as well as intense neuromuscular actions such as accelerations, decelerations, changes of direction, jumps, lateral sliding and static efforts [ 2 , 3 , 4 ]. In basketball, the anthropometric profile of players is a strong performance-limiting factor. Between the mid to late twentieth century, major increases in the average height of players [ 5 , 6 ] were reported in the U.S. National Basketball Association (NBA), demonstrating that in selection processes more importance was given to the screening of the players’ physical profile.

In many sports, including basketball, body composition is an important feature that is regularly assessed by practictioners [ 6 ]. The high locomotion demands of basketball [ 3 ] impose considerable physical loads on the players’ bodies [ 7 ]; therefore, a more favourable body composition profile (e.g. less fat mass) might be beneficial for the athlete. In fact, the relative proportion of body fat (BF) has been shown to be negatively associated with performance of explosive actions such as changes of direction [ 8 ] and vertical jumps [ 9 ]. Noticeably, these actions are frequent in basketball (e.g. jumps: ~ 1 ± 0.1 per minute; changes of activity every 1–3 s) [ 2 , 3 ]. Higher BF has also been shown to increase risk of overuse injuries (e.g. patellar tendinopathy) in basketball and volleyball players [ 10 , 11 ]. Considering also the high training [ 12 , 13 ] and competition [ 12 ] loads imposed during the basketball season, it appears therefore relevant for basketball practitioners to control players’ BF, in order to optimize their performance and guarantee their health.

With regard to body composition assessments in basketball players, the player’s sex must be taken into consideration. Females possess greater BF content compared to their male peers [ 2 , 14 ], mainly for evolutionary benefits (e.g. pregnancy) and hormonal differences (higher estrogen) [ 15 ]. While this notion is widely known, no study has systematically assessed previous data of BF of male and female basketball players, and thus no precise reference values are available to practitioners yet. This is of foremost importance considering that, to be selected at high levels, basketball players are commonly screened for anthropometric characteristics (including BF) [ 14 , 16 ] and physical capacities which can be influenced by BF (e.g. jumps, changes of direction) [ 8 , 9 ].

BF is usually quantified by laboratory (e.g. dual-energy X-ray absorptiometry [DXA], air displacement plethysmography [ADP]) and field methods (e.g. skinfold measurement, bioelectrical impedance analysis [BIA]) all of which have their own advantages and disadvantages [ 17 ]. However, it is important to note that each method makes its own assumption when estimating BF, which may yield discrepant results in the same group of athletes.

Furthermore, it is reasonable to expect that BF levels would discriminate players of different competitive levels, since the physiological demands are known to be greater in higher compared to lower leagues [ 2 , 3 ]. Differences in anthropometric and physiological characteristics, such as body height, aerobic capacity and muscle power have been previously reported, with all parameters favouring players in higher leagues [ 18 , 19 , 20 ]. However, in terms of differences in BF the available body of evidence is less clear. For instance, two previous studies [ 18 , 20 ] reported lower BF content in higher-level players compared to lower levels, two studies found no differences [ 19 , 21 ], and one study [ 22 ] reported higher BF values in national compared to regional-level players.

Reference values for BF in basketball players are needed by researchers, coaches, and practitioners alike when evaluating players. This information should distinguish between female and male players, help interpretation of values obtained by different measurement techniques, and aid in selection processes [ 16 ] and training design [ 23 ]. Therefore, the aim of this study was to provide reference values for BF of basketball players considering sex, measurement method, and competitive level.

Study Design and Searches

A systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement guidelines [ 24 ]. A literature search was performed using electronic databases PubMed, Web of Science, SPORTDiscus, CINAHL and Scopus (Fig.  1 ). The search was limited to peer-reviewed studies from all languages published between January 2010 to June 2020 and was updated November 2021. The following body composition related search terms were combined with the term “basketball” to source pertinent peer-reviewed articles: “body composition” OR “body fat*” OR “fat mass” OR “lean body mass” OR “fat free mass” OR “muscle mass” OR skinfold* OR anthropometr* OR “multi-component model” OR “bioelectrical impedance” OR bioimpedance OR “magnetic resonance imaging” OR “computed tomography” OR “dual-energy X-ray absorptiometry” OR “dual X-ray absorptiometry” OR densitometry OR “underwater weighing” OR “air displacement plethysmography” OR hydrometry OR ultrasound OR “3D photonic scanning”. The literature search and study selection were independently conducted by three researchers (PS, PB and BM) and disagreements were resolved by discussion until consensus was achieved.

Flowchart of study screening and selection. BB basketball. a Data for each database represent results of Jun 2020/Nov 2021 searches

Study Inclusion and Exclusion Criteria

After database screening and removal of duplicates, the remaining studies were carefully examined by screening the (1) titles, (2) abstracts and (3) full texts. The following inclusion criteria were applied: (1) participants were healthy basketball players older than 18 years; (2) players were competing at regional, national or international competitions; (3) the full-text of the article was published in a peer-reviewed journal in English, Spanish, Portuguese or German language; and (4) outcome measures included and described at least one method of estimating relative BF.

Studies were considered ineligible for this review if (1) the mean age of the sample was lower than 18 years; (2) some or all basketball players were injured (e.g. rehabilitation study); (3) the full-text of the article was not written in English, Spanish, Portuguese or German language; (4) the term basketball player was used referring to athletes from other sports or recreational basketball players, who did not engage competitively, trained less than at least twice per week and/or had less than a minimum of one year of basketball experience; (5) the BF value was not stated, or not independently reported by sex or measurement method, or the study contained duplicate data (e.g. same sample of another study already included in the search results); (6) the article full-text was not available. Case studies, reviews, conference communications, opinion articles, presentations, theses, book chapters or posters were not included. To complement the literature research, the reference lists of the included studies were also screened. The literature review and selection processes are summarized in Fig.  1 .

Data Extraction Strategy

Studies were independently read by three researchers (PS, PB, and BM) for the extraction of the following variables: (1) descriptive information including authors, year of publication and type of study; (2) participant information including sample size, sex, age, body height, body mass and general sample description. Players were assigned to one of three competitive levels: regional, national and international. Players from third national leagues or lower, university athletes or regional teams without further description were considered regional-level, whereas the national level represents players from first or second national leagues, including the National Collegiate Athletic Association (NCAA) divisions 1 and 2. If the study clearly mentioned that players competed at the international level (i.e., members of a national team, club teams competing in international championships) or were playing in the NBA, they were categorised as international level. (3) Measurement information including the technique and equipment and equations used were extracted. The measurement techniques included in the study were: skinfold measurement; BIA; DXA; and ADP. Beside BF as the main variable of interest, lean compartment mass, including absolute (kg) or relative (%) muscle mass, fat free mass, or lean body mass were extracted and reported. For studies reporting multiple assessments (e.g. baseline, post-intervention, follow-up) of the same body composition indicator, the pre-intervention data or initial value were considered. Additional information regarding the ethical approval of studies, preparation for measurements (e.g. clothes, food intake, hydration) and reliability of results was also extracted. If pertinent data were absent, the authors were contacted, and the necessary information was requested via e-mail. In case of no response or unavailability of data, the article was excluded according to ineligibility criteria 5 (no data). Coding was cross-checked between authors and disagreements were settled by discussion until consensus was achieved.

Data Synthesis and Presentation, Potential Effect Modifiers and Reasons for Heterogeneity

Statistical analysis was performed using R version 4.0.3, RStudio version 1.4.1103, and the package Metafor (version 3.0-2) [ 25 ]. The outcome variable was BF, and moderator variables were: sex (male, female); method of body composition assessment (ADP, BIA, DXA, and skinfold); and competitive level (international, national and regional) with random effect being the study itself. The pooled mean estimates, and their corresponding 95% confidence intervals (CI) were reported for each performed analysis. The variance of the sample mean BF for each study was calculated (SD 2 /sample size) and studies were weighted by the inverse of the variance in the meta-analysis models. The random-effects model takes into consideration the residual heterogeneity of studies and it is assessed through Cochran's test of heterogeneity (QE). In addition, I 2 statistics were calculated to determine the degree of statistical heterogeneity, with > 75% considered as high statistical heterogeneity. Test statistics for residual heterogeneity by removing a single study were calculated to check for single study influence on residual heterogeneity. Sensitivity analysis was implemented to investigate the influence of the removal of a single study on the pooled estimate. Publication bias was visually inspected by examining the asymmetry of the funnel plots containing pseudo confidence interval regions (white (90%), light grey (95%) and dark grey (99%) areas). Forest plots were used to present pooled means with 95% CI of arbitrarily defined groups (e.g., male international players measured with DXA).

Each moderator variable was first considered independently (e.g. in a separate model including only one moderator). As the analysis demonstrated the statistically significant difference between groups in all single moderator variables (e.g., between females and males, between international and national/regional, and between measurement methods), we subsequently used the moderator sex in combination with another moderator (measurement method or competitive level). Finally, we combined all three moderators in one model. Hence, the model equation for the final model was

where \(\hat{\theta }_{k}\) is the observed mean BF in study \(k\) , \(\beta_{0}\) is the mean BF in the arbitrarily chosen reference group of male international players measured with DXA. Further regression coefficients \(\beta_{1}\) to \(\beta_{6}\) represent the change in mean BF due to female sex, measurement with BIA, skinfold or ADP, and national or regional competitive level. \(\varepsilon_{k}\) is a residual term with mean 0 and variance corresponding to the sampling variance of \(\hat{\theta }_{k}\) within the study-specific population of study \(k\) . \(\zeta_{k}\) is an additional random effect with mean 0 and variance corresponding to the heterogeneity between studies.

Post-hoc Bonferroni correction was applied for p -values when performing all pairwise comparisons between the four methods of body composition assessment or the three competitive levels.

The search of the five databases resulted in a total of 2563 publications. After removal of duplicates, the titles and abstracts of 1305 studies were read. Following the application of the predetermined inclusion/exclusion criteria to both titles and abstracts, a total of 326 studies remained. Following further inspection of the full texts, 80 studies [ 2 , 8 , 9 , 16 , 18 , 19 , 20 , 22 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ] were included into the meta-analysis (see Fig.  1 ).

A detailed summary of each of the included studies (authors and years of publication, populations, methods and outcomes) can be found in Tables 1 , 2 , 3 and 4 . Across studies, 4335 basketball players were included (3467 male, 868 female) with a mean age ranging from 19.0 [ 22 ] to 28.9 [ 74 ] years. Mean body mass and body height ranged from 75.0 [ 28 ] kg to 105.6 [ 69 ] kg and 179.4 [ 48 ] cm to 203.0 [ 70 ] cm for males and 63.8 [ 67 ] kg to 81.1 [ 34 ] kg and 164.0 [ 67 ] cm to 185.8 [ 42 ] cm for females. Mean sample size was 55 players per study and ranged from 7 [ 74 ] to 1160 [ 16 ]. There were 652 players categorized as “regional level”, 2142 as “national level” and 1518 as “international”, with one study presenting a mixed sample of regional and national level players [ 91 ]. For the assessment of BF, 39 studies used skinfold measurements, 23 BIA, 15 DXA and 3 studies used ADP.

Our results revealed that male players had significantly lower BF values compared to their female counterparts (pooled mean for males = 13.2%; 95% CI 12.4–14.0% vs. pooled mean for females = 20.4%; 95% CI 19.4–21.3%; p  < 0.001). BF measured by DXA (pooled mean = 21.6%; 95% CI 18.5–24.7%) was significantly higher than BF measured by BIA (pooled mean = 14.7%; 95% CI 12.2–17.3%; p  < 0.001) and skinfolds (pooled mean = 12.3%; 95% CI 10.4–14.2%; p  < 0.001). Furthermore, BF measured by skinfolds was significantly lower than BF measured by ADP (pooled mean = 20.0%; 95% CI 13.3–26.6%, p  = 0.02). Pooled mean BF values across competitive levels were 13.2% for international level players (95% CI 11.3–15.1%), 15.6% for national level players (95% CI 14.0–17.1%) and 15.0% for regional level players (95% CI 13.3–16.6%), with a significant difference found between international and national level players ( p  < 0.001) as well as international and regional level players ( p  = 0.02).

A random-effects meta-regression model was used to examine the effects of sex, measurement method and competitive level on BF. Our model combining all variables revealed that BF differences between male and female players stayed significant ( p  < 0.001) after correcting for competitive level and measurement method. Similarly, the differences between BF as measured by DXA and BIA as well as by DXA and skinfold remained significant ( p  < 0.001) after accounting for sex and competitive level. By contrast, the differences between BF measured by ADP and skinfolds were no longer significant after adjusting for sex and competitive level. Differences between international players and national players ( p  = 0.02) as well as differences between international and regional players ( p  = 0.02) remained significant after adjusting for sex and measurement method. However, sensitivity analysis suggested that the analysis of the influence of competitive level was not completely robust. Exclusion of one study [ 18 ] changed the statistical significance. By contrast, the stability of our findings on measurement method and sex were confirmed by the sensitivity and cumulative meta-analyses. The forest plot of the analysis is presented in Fig.  2 . The results of meta-analysis according to subgroups adjusted for sex and measurement method are shown in Table 5 .

Relative body fat of basketball players: forest plot showing pooled mean estimates and 95% confidence intervals of included studies. ADP air displacement plethysmography; BIA bioelectrical impedance analysis; CI confidence interval; DXA dual-energy X-ray absorptiometry; F female; M male; 1, 2, 3 single study included multiple times in the forest plot as it included data from multiple samples (e.g. male and females; international and regional); * marking different studies from same authors and published in the same year

We found no indication of a publication bias, with most points falling symmetrically within the funnel plot (see Fig.  3 ). Heterogeneity in our dataset was estimated by Cochran's test of heterogeneity (QE = 2621, p  < 0.0001) and I 2 statistics ( I 2  > 75%). The Cochran's test of heterogeneity revealed highly stable outcomes in our case when we ran a sensitivity analysis for p -values by removing single studies step-by-step (i.e., no changes in p -values).

Funnel plot of the model including all moderator variables

This is the first systematic review and meta-analysis to examine body fat in basketball players as well as the respective influences of sex, measurement method and competitive level. The main findings of this meta-analysis were: (1) male basketball players have greater BF compared to their female counterparts; (2) considerable differences exist between BF as assessed with different methods, with greater BF values reported from DXA analysis compared to BIA and skinfold estimates; and (3) BF is lower in international level players compared to lower level (i.e. national and regional) players. In general, the BF data obtained by our meta-analysis (see Table 5 ) are in a healthy, athletic-level range. Aside from this general outcome, as all the factors investigated significantly influenced BF, it is essential to discuss and interpret results in consideration of the player’s sex, competitive level and the measurement method implemented.

Given the increasing popularity of women’s basketball and the general need for more high-quality sports science research focusing on female athletes [ 98 ], the present study made a particular effort to evaluate the effect of sex on BF of basketball players by including sex as a potential factor into the meta-regression. As initially expected, BF values were greater in female basketball players than in males. These results were confirmed even when considering the moderating effects of measurement method and competitive levels. While a previous direct comparison across male and female basketball players has shown similar results [ 2 ], our study compiled all previous relevant research on body composition of basketball players. Females carry greater BF than males due to biological differences [ 15 ] which have to be taken into account by practitioners working with female basketball players, from both performance (e.g. speed, power training) and health (e.g. manipulating training loads to reduce risk of injury) perspectives. Despite the increasing number of publications focusing on female basketball players in recent years, the body of evidence available on women is still much smaller than that available for men (3467 male players included versus 868 female players). Considering the already comparatively low number of female athletes included into this meta-analysis, it should be noted that only 8 of the 44 studies involving female athletes estimated BF content through measurements of skinfold thickness. Hence, the respective reference values reported here must be interpreted carefully. While skinfold assessment has some limitations [ 99 ], it is also the least expensive method and most frequently used by practitioners [ 99 ]. For these reasons, further research into the anthropometry of female basketball players is warranted to obtain more robust reference data.

Interestingly, considerable differences were found between BF values registered with different measurement methods. BF as measured by DXA was significantly higher compared to BF measured by BIA or skinfolds. Thus, our meta-analysis confirms the results of a single original study, in which BF values measured by different methods were compared in the same sample [ 30 ]. Furthermore, it has been observed by various studies that athletes` BF measured by skinfold or BIA is significantly underestimated when directly compared to BF measured by DXA [ 100 , 101 ]. Given these differences, it is recommended to compare BF values only to reference values derived with the same measurement method (see Table 1 , 2 , 3 , 4 , 5 ). Additionally, results can also be affected by measurement preparation as well as the type of measurement equipment and the computational procedures used for the estimation of BF content [ 17 , 102 ]. As an example, Golja et al. [ 102 ] observed that BF estimates of young, healthy subjects ranged from 6 to 29% across several skinfold regression equations. Similarly, large variability between measurement devices and equations have been found for BIA and DXA derived values of body composition [ 17 , 103 ]. This carries important implications for practitioners assessing BF levels in athletic cohorts and comparing their results to data reported in the literature. If possible, data should be compared to values obtained with the same measurement equipment and computational procedure. Equally, it is imperative that future studies clearly state both measurement devices and computational procedures. Another important point to consider is measurement methodology standardization. Even though it is well known that factors such as hydration status, food intake, physical activity and temperature can influence all body composition measurement methods [ 17 , 103 , 104 ] about half of the studies included in this review did not provide adequate details regarding measurement methodology standardization. Another secondary finding that might help future research planning is that only about one third of the studies included in this review reported measures of reliability (e.g. coefficient of variation, intraclass correlation coefficient, etc.) for their body fat assessments. However, this is important to ensure that data are sound, and results are accurate.

Regarding competitive levels, we found BF levels to be significantly lower in international-level players compared to national or regional players. However, it should be noted that the sensitivity analysis of the data showed that findings were influenced by single studies, which means caution is needed in their interpretation. While we expected to find lower BF values in higher competitive levels, differences between groups were generally small and could be only observed when comparing the international to lower competitive levels. While lower BF is advantageous for neuromuscular actions such as jumps and changes of directions [ 8 , 9 ], the game of basketball is also characterised by static efforts. These actions refer to all those situations in which players are stationary and fight to obtain and maintain advantageous position on the court (e.g.to rebound, in picking and low-post situations) [ 3 , 105 ]. In these specific scenarios, a greater body mass might be advantageous for the player, making him/her less prone to be pushed away from his/her position by an opponent. Since previous studies have shown that higher level players have a greater body mass than lower-level players [ 19 , 20 , 22 ], it is possible that lean compartment mass, rather than BF, is more sensitive in discriminating between basketball players of different competitive levels. While we extracted lean compartment mass from all included studies (see Tables 1 , 2 , 3 , 4 ), inconsistencies in terminologies and calculation methods used impeded their joint evaluation by meta-analysis. Future studies should address these inconsistencies and clearly state how lean compartment mass was calculated. Nevertheless, our results evidenced that BF content was lower in higher competitive levels in basketball, an expected finding which might be explained by several factors related to competing at higher levels, such as more rigorous anthropometric profiling and selection processes, controlled diet, as well as higher physical, physiological and energetic demands of training and competition.

This study had some limitations. Firstly, most studies did not report reliability measures of the body composition methods implemented, which casts doubt on the reproducibility of included data. Similarly, few studies reported essential information such as hydration and feeding status—factors known to influence body composition measurements [ 17 , 104 ]. Another limitation regarded the categorisation of competitive level, which could also have influenced our results. We categorised players as international, national or regional, but this classification may improperly reflect the players’ actual competitive or skill level (e.g., the competitive level in a regional league in the U.S. might actually be higher than that in a national league of a country where basketball is less popular). Lastly, since only 19 out of 80 included studies reported BF values by playing position, it was not possible to account for playing position in the present meta-analysis. Players of different positions typically feature significantly different anthropometric characteristics and performance profiles [ 3 , 20 ], so there is a clear need for future studies to report BF data by playing position.

This study also aimed at critically discussing the shortcomings of research published to date, and to identify promising future research directions. We recommend future studies assessing BF of basketball players to (1) clearly describe computational procedures and measurement devices used to estimate BF (2) specify the reliability of the measurement instruments, (3) clearly control and report the hydration and feeding status prior to measurement, (4) specify the competitive level of the sample by reporting the country and/or region and name of the league in which players competed at the time of the study, and (5) report BF of players in distinct categories (i.e. sex, competitive level, playing position) for better interpretation of data. Additionally, it would be interesting to review the influence of sex, measurement method and competitive level on lean compartment mass values, such as fat free mass, lean body mass and muscle mass. However, inconsistencies in terminology could be an important barrier to the successful (quantitative) comparison of studies investigating lean compartment mass of basketball players.

This meta-analysis summarised and evaluated the available body of evidence on BF of basketball players. The results showed that female basketball players have greater BF than male counterparts. Results for the same basketball players varied depending on the measurement method used; therefore, it is imperative for practitioners assessing BF to compare their players’ BF only with the values obtained in this study for the same measurement method. International-level players appeared to have lower BF than national or regional level players, suggesting that body composition variables can discriminate competitive levels in basketball.

Availability of data and materials

Data will be made available upon reasonable request.

Abbreviations

Air displacement plethysmography

Bioelectrical impedance analysis

95% Confidence interval

Dual-energy X-ray absorptiometry

National Basketball Association

National Collegiate Athletic Association

Cochran's test of heterogeneity

Standard deviation

Hulteen RM, Smith JJ, Morgan PJ, Barnett LM, Hallal PC, Colyvas K, et al. Global participation in sport and leisure-time physical activities: a systematic review and meta-analysis. Prev Med. 2017;95:14–25. https://doi.org/10.1016/j.ypmed.2016.11.027 .

Article   PubMed   Google Scholar  

Scanlan AT, Dascombe BJ, Kidcaff AP, Peucker JL, Dalbo VJ. Gender-specific activity demands experienced during semiprofessional basketball game play. Int J Sports Physiol Perform. 2015;10(5):618–25. https://doi.org/10.1123/ijspp.2014-0407 .

Stojanovic E, Stojiljkovic N, Scanlan AT, Dalbo VJ, Berkelmans DM, Milanovic Z. The activity demands and physiological responses encountered during basketball match-play: a systematic review. Sport Med. 2018;48(1):111–35. https://doi.org/10.1007/s40279-017-0794-z .

Article   Google Scholar  

Sansone P, Tessitore A, Paulauskas H, Lukonaitiene I, Tschan H, Pliauga V, et al. Physical and physiological demands and hormonal responses in basketball small-sided games with different tactical tasks and training regimes. J Sci Med Sport. 2019;22(5):602–6. https://doi.org/10.1016/j.jsams.2018.11.017 .

Article   CAS   PubMed   Google Scholar  

Sedeaud A, Marc A, Schipman J, Schaal K, Danial M, Guillaume M, Berthelot G. Toussaint JF secular trend: morphology and performance. J Sports Sci. 2014;32(12):1146–54. https://doi.org/10.1080/02640414.2014.889841 .

Drinkwater EJ, Pyne DB, McKenna MJ. Design and interpretation of anthropometric and fitness testing of basketball players. Sport Med. 2008;38(7):565–78. https://doi.org/10.2165/00007256-200838070-00004 .

Vanrenterghem J, Nedergaard NJ, Robinson MA, Drust B. Training load monitoring in team sports: a novel framework separating physiological and biomechanical load-adaptation pathways. Sport Med. 2017;47(11):2135–42. https://doi.org/10.1007/s40279-017-0714-2 .

Spiteri T, Newton RU, Binetti M, Hart NH, Sheppard JM, Nimphius S. Mechanical Determinants of faster change of direction and agility performance in female basketball athletes. J Strength Cond Res. 2015;29(8):2205–14. https://doi.org/10.1519/JSC.0000000000000876 .

Ribeiro BG, Mota HR, Sampaio-Jorge F, Morales AP, Leite TC. Correlation between body composition and the performance of vertical jumps in basketball players. J Exerc Physiol Online. 2015;18(5):69–78.

Google Scholar  

Visnes H, Bahr R. Training volume and body composition as risk factors for developing jumper’s knee among young elite volleyball players. Scand J Med Sci Sport. 2013;23(5):607–13. https://doi.org/10.1111/j.1600-0838.2011.01430.x .

Article   CAS   Google Scholar  

Sprague AL, Smith AH, Knox P, Pohlig RT, Grävare SK. Modifiable risk factors for patellar tendinopathy in athletes: a systematic review and meta-analysis. Br J Sports Med. 2018;52(24):1575–85. https://doi.org/10.1136/bjsports-2017-099000 .

Paulauskas H, Kreivyte R, Scanlan AT, Moreira A, Siupsinskas L, Conte D. Monitoring workload in elite female basketball players during the in-season phase: weekly fluctuations and effect of playing time. Int J Sport Physiol Perform. 2019;14(7):941–8. https://doi.org/10.1123/ijspp.2018-0741 .

Sansone P, Gasperi L, Tessitore A, Gomez MA. Training load, recovery and game performance in semi-professional male basketball: Influence of individual characteristics and contextual factors. Biol Sport. 2021;38(2):207–17. https://doi.org/10.5114/BIOLSPORT.2020.98451 .

Ziv G, Lidor R. Physical attributes, physiological characteristics, on-court performances and nutritional strategies of female and male basketball players. Sport Med. 2009;39(7):547–68. https://doi.org/10.2165/00007256-200939070-00003 .

Karastergiou K, Smith SR, Greenberg AS, Fried SK. Sex differences in human adipose tissues—the biology of pear shape. Biol Sex Differ. 2012;3(1):13. https://doi.org/10.1186/2042-6410-3-13 .

Article   PubMed   PubMed Central   Google Scholar  

Cui Y, Liu F, Bao D, Liu H, Zhang S, Gómez MÁ. Key anthropometric and physical determinants for different playing positions during national basketball association draft combine test. Front Psychol. 2019;10:2359. https://doi.org/10.3389/fpsyg.2019.02359 .

Kasper AM, Langan-Evans C, Hudson JF, Brownlee TE, Harper LD, Naughton RJ, et al. Come back skinfolds, all is forgiven: a narrative review of the efficacy of common body composition methods in applied sports practice. Nutrients. 2021. https://doi.org/10.3390/nu13041075 .

Pehar M, Sekulic D, Sisic N, Spasic M, Uljevic O, Krolo A, et al. Evaluation of different jumping tests in defining position-specific and performance-level differences in high level basketball players. Biol Sport. 2017;34(3):263–72. https://doi.org/10.5114/biolsport.2017.67122 .

Ferioli D, Rampinini E, Bosio A, La Torre A, Azzolini M, Coutts AJ. The physical profile of adult male basketball players: differences between competitive levels and playing positions. J Sports Sci. 2018;36(22):2567–74. https://doi.org/10.1080/02640414.2018.1469241 .

Ben Abdelkrim N, Chaouachi A, Chamari K, Chtara M, Castagna C. Positional role and competitive-level differences in elite-level men’s basketball players. J Strength Cond Res. 2010;24(5):1346–55.

Sallet P, Perrier D, Ferret JM, Vitelli V, Baverel G. Physiological differences in professional basketball players as a function of playing position and level of play. J Sports Med Phys Fitness. 2005;45(3):291–4.

CAS   PubMed   Google Scholar  

Vaquera A, Santiago S, Gerardo VJ, Carlos MJ, Vicente GT. Anthropometric characteristics of spanish professional basketball players. J Hum Kinet. 2015;46(1):99–106. https://doi.org/10.1515/hukin-2015-0038 .

Sansone P, Tschan H, Foster C, Tessitore A. Monitoring training load and perceived recovery in female basketball: implications for training design. J Strength Cond Res. 2020;34(10):2929–36. https://doi.org/10.1519/JSC.0000000000002971 .

Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097.

Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1–48. https://doi.org/10.18637/jss.v036.i03 .

Atalaǧ O, Gotshalk LA, Queen L, Wottlin S. More stress for the eastward travelling student athlete: a preliminary analysis. Isokinet Exerc Sci. 2019;27(4):275–82.

Dobrosielski DA, Leppert KM, Knuth ND, Wilder JN, Kovacs L, Lisman PJ. Body composition values of division 1 female athletes derived from dual-energy X-ray absorptiometry. J Strength Cond Res. 2019;Publish Ah:1–8.

Imeri B, Dureha DK. The study of body composition variables as predictor factors on back spines bone mineral density of basketball players. HealthMED. 2012;6(7):2308–13.

Nepocatych S, Balilionis G, O’Neal EK. Analysis of dietary intake and body composition of female athletes over a competitive season. Montenegrin J Sport Sci Med. 2017;6(2):57–65.

Ploudre A, Arabas JL, Jorn L, Mayhew JL. Comparison of techniques for tracking body composition changes across a season in college women basketball players. Int J Exerc Sci. 2018;11(4):425–38.

PubMed   PubMed Central   Google Scholar  

Raymond-Pope CJ, Solfest AL, Carbuhn A, Stanforth PR, Oliver J, Bach CW, et al. Total and regional body composition of NCAA Division I collegiate basketball athletes. Int J Sports Med. 2020;41(4):242–7. https://doi.org/10.1055/a-1073-7941 .

Rockwell MS, Kostelnik SB, McMillan RP, Lancaster M, Larson-Meyer DE, Hulver MW. An association between bioavailable 25-hydroxyvitamin d and bone mineral density in a diverse cohort of collegiate athletes. Med Sci Sports Exerc. 2021. https://doi.org/10.1249/MSS.0000000000002807 .

Sanfilippo J, Krueger D, Heiderscheit B, Binkley N. Dual-energy X-ray absorptiometry body composition in NCAA Division I athletes: exploration of mass distribution. Sports Health. 2019;11(5):453–60. https://doi.org/10.1177/1941738119861572 .

Sekel NM, Gallo S, Fields J, Jagim AR, Wagner T, Jones MT. The effects of cholecalciferol supplementation on vitamin d status among a diverse population of collegiate basketball athletes: a quasi-experimental trial. Nutrients. 2020;12(2):1–13. https://doi.org/10.3390/nu12020370 .

Stanforth D, Lu T, Stults-Kolehmainen MA, Crim BN, Stanforth PR. Bone mineral content and density among female NCAA Division I athletes across the competitive season and over a multi-year time frame. J Strength Cond Res. 2014;30(10):2828–38.

Zanders BR, Currier BS, Harty PS. Changes in energy expenditure, dietary intake, and energy availability across an entire collegiate women’s basketball season. J Strength Cond Res. 2021;35(3):804–10.

Gantois P, Aidar FJ, Peixoto Dantas M, Medeiros da Silva L, Pinheiros Paes P, Santana EE, et al. Aerobic fitness is associated with improved repeated sprints ability of basketball players after six weeks of training during preseason. Rev Bras Cineantropometria e Desempenho Hum. 2018;20(1):114–24. https://doi.org/10.5007/1980-0037.2018v20n1p114 .

Taylor LW, Wilborn C, Roberts MD, White A, Dugan K. Eight weeks of pre-and postexercise whey protein supplementation increases lean body mass and improves performance in division III collegiate female basketball players. Appl Physiol Nutr Metab. 2015;41(3):249–54. https://doi.org/10.1139/apnm-2015-0463 .

Sousa S, Hogera Rodrigues WR, de Aguiar Cintra Filho D. Relationship between body composition and performance in vertical jump on different sports. Rev Bras Prescrição e Fisiol do Exerc. 2017;11(71):944–51.

Ljubojevic M, Bojanic D, Krivokapic D, Nokic A. Differences in anthropometric characteristics and body composition between two elite female basketball clubs—champions of slovenia and champions of Montenegro. Sport Mont. 2020;18(3):45–9. https://doi.org/10.26773/smj.201018 .

Ljubojevic M, Bojanic D, Bjelica D, Vasiljevic I, Vukotic M. Differences in anthropometric characteristics between two elite female basketball national teams—participants at Eurobasket 2019 in Latvia and Serbia. Int J Morphol. 2020;38(4):857–62.

Mala L, Maly T, Zahalka F, Bunc V, Kaplan A, Jebavy R, et al. Body composition of elite female players in five different sports games. J Hum Kinet. 2015;45(1):207–15. https://doi.org/10.1515/hukin-2015-0021 .

Vukašinović-Vesić M, Andjelković M, Stojmenović T, Dikić N, Kostić M, Ćurčić D. Sweat rate and fluid intake in young elite basketball players on the FIBA Europe U20 Championship. Vojnosanit Pregl. 2015;72(12):1063–8. https://doi.org/10.2298/VSP140408073V .

Delextrat A, Trochym E, Calleja-González J. Effect of a typical in-season week on strength jump and sprint performances in national-level female basketball players. J Sport Med Phys Fit. 2012;52:128–36. https://doi.org/10.13140/RG.2.1.5129.4169 .

Delextrat A, Calleja-González J, Hippocrate A, Clarke ND. Effects of sports massage and intermittent cold-water immersion on recovery from matches by basketball players. J Sports Sci. 2013;31(1):11–9. https://doi.org/10.1080/02640414.2012.719241 .

Delextrat A, Baliqi F, Clarke N. Repeated sprint ability and stride kinematics are altered following an official match in national-level basketball players. J Sport Med Phys Fit. 2013;147(2):135–40. https://doi.org/10.13140/RG.2.1.5129.4169 .

Delextrat A, Hippocrate A, Leddington-Wright S, Clarke ND. Including stretches to a massage routine improves recovery from official matches in basketball players. J Strength Cond Res. 2014;28(3):716–27.

Delgado-Floody P, Caamaño-Navarrete F, Carter-Thuillier B, Gallardo-Fuentes F, Ramirez-Campillo R, Cresp Barría M, et al. Comparison of body composition and physical performance between college and professional basketball players. Arch Med del Deport. 2017;34(6):332–6.

Dzedzej A, Ignatiuk W, Jaworska J, Grzywacz T, Lipińska P, Antosiewicz J, et al. The effect of the competitive season in professional basketball on inflammation and iron metabolism. Biol Sport. 2016;33(3):223–9. https://doi.org/10.5604/20831862.1201811 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Gryko K, Kopiczko A, Mikołajec K, Stasny P, Musalek M. Anthropometric variables and somatotype of young and professional male basketball players. Sports. 2018;6(1):9. https://doi.org/10.3390/sports6010009 .

Article   PubMed Central   Google Scholar  

Koklu Y, Utku A, Fatma UK, Kocak U, Emre EA. The relationship among body composition, maximal oxygen uptake, sprint ability and T-drill agility tests in first division basketball players. Ovidius Univ Ann Ser Phys Educ Sport Mov Health. 2010;10(2):633–5.

Kukrić A, Petrović B, Dobraš R, Sekulic Z, Vuckovic I. Application of the theoretical model in normalization of vertical jump test results with respect to the body mass. Sportlogia. 2017;13(1):9–17. https://doi.org/10.5550/sgia.171301.en.kpdsv .

Michalczyk M, Zajac A, Mikolajec K, Zydek G, Langfort J. No modification in blood lipoprotein concentration but changes in body composition after 4 weeks of low carbohydrate diet (LCD) followed by 7 days of carbohydrate loading in basketball players. J Hum Kinet. 2018;65(1):125–37.

Michalczyk MM, Chycki J, Zajac A, Maszczyk A, Zydek G, Langfort J. Anaerobic performance after a low-carbohydrate diet (LCD) followed by 7 days of carbohydrate loading in male basketball players. Nutrients. 2019. https://doi.org/10.3390/nu11040778 .

Ramos-Campo DJ, Sánchez FM, García PE, Rubio Arias JA, Bores C, Clemente Suarez VJ, et al. Body composition features in different playing position of professional team indoor players: basketball handball and futsal. Int J Morphol. 2014;32(4):1316–24. https://doi.org/10.4067/S0717-95022014000400032 .

Stauffer K, Nagle E, Goss F, Robertson R. Assessment of anaerobic power in female division I collegiate basketball players. J Exerc Physiol Online. 2010;13(1):1–9.

Czuba M, Zając A, Maszczyk A, Roczniok R, Poprzęcki S, Garbaciak W, et al. The effects of high intensity interval training in normobaric hypoxia on aerobic capacity in basketball players. J Hum Kinet. 2013;39:103–14.

Delextrat A, Mackessy S, Arceo-Rendon L, Scanlan A, Ramsbottom R, Calleja-Gonzalez J. Effects of three-day serial sodium bicarbonate loading on performance and physiological parameters during a simulated basketball test in female university players. Int J Sport Nutr Exerc Metab. 2018;28(5):547–52. https://doi.org/10.1123/ijsnem.2017-0353 .

Kutseryb T, Hrynkiv M, Vovkanych L, Muzyka F. Influence of basketball training on the features of women’s physique. J Phys Educ Sport. 2019;19(4):2384–9. https://doi.org/10.7752/jpes.2019.04361 .

Omorczyk J, Ambrozy T, Puszczalowska-Lizis E, Nowak M, Markowksi A. Effects of 6-week basketball training using the modified circuit weight method. Balt J Health Phys Act. 2017;2017(4):44–54. https://doi.org/10.29359/bjhpa.09.4.04 .

Salgueiro DFS, Barroso R, Barbosa AC, Telles T, Andries JO. Anthropometric parameters of cadets among different military sports. Int J Morphol. 2015;33(3):831–4. https://doi.org/10.4067/s0717-95022015000300004 .

Albaladejo M, Vaquero-Cristóbal R, Esparza-Ros F. Effect of preseason training on anthropometric and derived variables in professional basketball players. Retos. 2019;2041(36):474–9. https://doi.org/10.47197/retos.v36i36.68535 .

Calleja-González J, Jukíc I, Ostojic SM, Milanovic L, Zubillaga A, Terrados N. Fitness profile in elite international senior male basketball players. Differences between Croatian and Japan team. Arch Med del Deport. 2010;27(137):181–90.

Doeven SH, Brink MS, Frencken WGP, Lemmink KAPM. Impaired player-coach perceptions of exertion and recovery during match congestion. Int J Sports Physiol Perform. 2017;12(9):1151–6. https://doi.org/10.1123/ijspp.2016-0363 .

Gonzalez AM, Hoffman JR, Rogowski JP, Burgos W, Manalo E, Weise K, et al. Performance changes in NBA basketball players vary in starters vs nonstarters over a competitive season. J Strength Cond Res. 2013;27(3):611–5.

Jurić I, Labor S, Plavec D, Labor M. Inspiratory muscle strength affects anaerobic endurance in professional athletes. Arh Hig Rada Toksikol. 2019;70(1):42–8. https://doi.org/10.2478/aiht-2019-70-3182 .

Mtsweni LB, West SJ, Taliep MS. Anthropometric and physical fitness characteristics of female basketball players in South Africa. South Afr J Res Sport Phys Educ Recreat. 2017;39(3):93–103.

Ponce-González JG, Olmedillas H, Calleja-González J, Guerra B, Sanchis-Moysi J. Physical fitness, adiposity and testosterone concentrations are associated to playing position in professional basketballers. Nutr Hosp. 2015;31(6):2624–32. https://doi.org/10.3305/nh.2015.31.6.8977 .

Tsoufi A, Maraki MI, Dimitrakopoulos L, et al. The effect of professional dietary counseling: elite basketball players eat healthier during competition days. J Sports Med Phys Fitness. 2017;57(10):1305–10. https://doi.org/10.23736/S0022-4707.16.06469-0 .

Zhao J, Fan B, Wu Z, Xu M, Luo Y. Serum zinc is associated with plasma leptin and Cu–Zn SOD in elite male basketball athletes. J Trace Elem Med Biol. 2015;30:49–53. https://doi.org/10.1016/j.jtemb.2014.10.005 .

Boone J, Bourgois J. Morphological and physiological profile of elite basketball players in Belgium. Int J Sport Physiol Perform. 2013;8(6):630–8. https://doi.org/10.1123/ijspp.8.6.630 .

Brini S, Ouerghi N, Bouassida A. Small sided games vs repeated sprint training effects on agility in fasting basketball players. Rev Bras Med Esporte. 2020. https://doi.org/10.1590/1517-869220202603213948 .

Chatzinikolaou A, Draganidis D, Avloniti A, Karipidis A, Jamurtas AZ, Skevaki CL, et al. The microcycle of inflammation and performance changes after a basketball match. J Sports Sci. 2014;32(9):870–82. https://doi.org/10.1080/02640414.2013.865251 .

Daniel J, Montagner PC, Padovani CR, Beneli LM, Borin JP. Analysis of direct and indirect participation in basketball game actions according to their intensity. Sport TK-Revista Euroam Ciencias del Deport. 2017;6(1):193. https://doi.org/10.6018/293661 .

Daniel JF, Montagner PC, Padovani CR, Borin JP. Techniques and tactics in basketball according to the intensity in official matches. Rev Bras Med do Esporte. 2017;23(4):300–3. https://doi.org/10.1590/1517-869220172304167577 .

Dragonea P, Zacharakis E, Kounalakis S, Kostopoulos N, Bolatoglou T, Apostolidis N. Determination of the exercise intensity corresponding with maximal lactate steady state in high-level basketball players. Res Sport Med. 2019;27(1):112–20. https://doi.org/10.1080/15438627.2018.1511559 .

Gomes JH, Mendes RR, De Almeida MB, Zanetti MC, Leite GDS, Júnior AJF. Relationship between physical fitness and gamerelated statistics in elite professional basketball players: regular season vs. playoffs. Motriz Rev Educ Fis. 2017. https://doi.org/10.1590/S1980-6574201700020004 .

Kariyawasam A, Ariyasinghe A, Rajaratnam A, Subasinghe P. Comparative study on skill and health related physical fitness characteristics between national basketball and football players in Sri Lanka. BMC Res Notes. 2019;12(1):397. https://doi.org/10.1186/s13104-019-4434-6 .

Korkmaz C, Karahan M. A comparative study on the physical fitness and performance of male basketball players in different divisions. Phys Educ Sport Sci J. 2012;6(1):16–23.

Masanovic B, Popovic S, Bjelica D. Comparative study of anthropometric measurement and body composition between basketball players from different competitive levels: elite and sub-elite. Pedagog Psychol Med Biol Probl Phys Train Sport. 2019;23(4):176–81. https://doi.org/10.15561/18189172.2019.0403 .

Peña J, Moreno-Doutres D, Coma J, Buscà B. Anthropometric and fitness profile of high-level basketball, handball and volleyball players. Rev Andaluza Med del Deport. 2018;11(1):30–5. https://doi.org/10.36104/amc.2018.1400 .

Pireva A. Anthropometric and body composition differences among elite Kosovo basketball, handball and soccer players. Int J Morphol. 2019;37(3):1067–72. https://doi.org/10.4067/S0717-95022019000301067 .

Pluncevic J, Marosevic-Markovski J, Todorovski M, Lekovska T, Manchevska S. Comparison of cardio-physiological and anthropometrical parameters between basketball and football players. Res Phys Ed Sport Health. 2020;9(2):51–6. https://doi.org/10.46733/PESH20920051pg .

Puente C, Abián-Vicén J, Salinero JJ, Lara B, Areces F, Del Coso J. Caffeine improves basketball performance in experienced basketball players. Nutrients. 2017;9(9):1–13. https://doi.org/10.3390/nu9091033 .

Sekulic D, Pehar M, Krolo A, Spasic M, Uljevic O, Calleja-González J, et al. Evaluation of basketball-specific agility: applicability of preplanned and nonplanned agility performances for differentiating playing positions and playing levels. J Strength Cond Res. 2017;31(8):2278–88. https://doi.org/10.1519/JSC.0000000000001646 .

Thigpen LK, Green JM, O’Neal EK. Hydration profile and sweat loss perception of male and female division ii basketball players during practice. J Strength Cond Res. 2014;28(12):3425–31.

Watson AD, Zabriskie HA, Witherbee KE, Sulavik A, Gieske BT, Kerksick CM. Determining a resting metabolic rate prediction equation for collegiate female athletes. J Strength Cond Res. 2019;33(9):2426–32. https://doi.org/10.1519/JSC.0000000000002856 .

De Oliveira GT, Gantois P, Faro HKC, Gantois P, Faro HK, Nascimiento PHD. Vertical jump and handgrip strength in basketball athletes by playing position and performance. J Phys Educ Sport. 2018;18(1):132–7. https://doi.org/10.7752/jpes.2018.01017 .

Freitas TT, Calleja-González J, Carlos-Vivas J, Marín-Cascales E, Alcaraz PE. Short-term optimal load training vs a modified complex training in semi-professional basketball players. J Sports Sci. 2019;37(4):434–42. https://doi.org/10.1080/02640414.2018.1504618 .

Gomez JG, Verdoy PJ. Characterization of college football athletes and basketball: anthropometry and body composition. E-balonmano.com. J Sport Sci. 2011;7(1):39–51. https://doi.org/10.47189/rcct.v18i19.205 .

El MC. perfil esteroideo en jugadores de baloncesto de ambos sexos y su relación con parámetros físicos, genéticos y nutricionales. Kronos A J Interdiscip Synth. 2016;15(1):2.

Pacheco Gabaldón RP, González Peris M, Romeu FM. Nutri-K study: evaluation of potassium intake and sport in young adults. Nutr Clin y Diet Hosp. 2018;38(3):161–7. https://doi.org/10.12873/383romeu .

Scanlan AT, Dascombe BJ, Reaburn PRJ. The construct and longitudinal validity of the basketball exercise simulation test. J Strength Cond Res. 2012;26(2):523–30.

Scanlan A, Humphries B, Tucker PS, Dalbo V. The influence of physical and cognitive factors on reactive agility performance in men basketball players. J Sports Sci. 2013;32(4):367–74. https://doi.org/10.1080/02640414.2013.825730 .

Fields J, Merrigan J, White J, Jones M. Seasonal and longitudinal changes in body composition by sport-position in NCAA division I basketball athletes. Sports. 2018;6(3):85. https://doi.org/10.3390/sports6030085 .

Fields JB, Metoyer CJ, Casey JC, Esco MR, Jagim AR, Jones MT. Comparison of body composition variables across a large sample of national collegiate athletic association women athletes from 6 competitive sports. J Strength Cond Res. 2018;32(9):2452–7. https://doi.org/10.1519/JSC.0000000000002234 .

Ladwig E, Shim A, Yom J, Cross P, Beebe J. Preseason and postseason body composition does not change relative to playing time in division I female basketball players. Int J Exerc Sci. 2013;6(3):208–16.

Elliott-Sale KJ, Minahan CL, de Jonge XAKJ, Ackerman KE, Sipilä S, Constantini NW, et al. Methodological considerations for studies in sport and exercise science with women as participants: a working guide for standards of practice for research on women. Sport Med. 2021;51(5):843–61. https://doi.org/10.1007/s40279-021-01435-8 .

Meyer NL, Sundgot-Borgen J, Lohman TG, Ackland TR, Stewart AD, Maughan RJ, et al. Body composition for health and performance: a survey of body composition assessment practice carried out by the ad hoc research working group on body composition, health and performance under the auspices of the IOC medical commission. Br J Sports Med. 2013;47(16):1044–53. https://doi.org/10.1136/bjsports-2013-092561 .

McLester CN, Nickerson BS, Klisczczewicz BM, McLester JR. Reliability and agreement of various inbody body composition analyzers as compared to dual-energy X-ray absorptiometry in healthy men and women. J Clin Densitom. 2020. https://doi.org/10.1016/j.jocd.2018.10.008 .

Suarez-Arrones L, Petri C, Maldonado RA, Torreno N, Munguia-Izquierdo D, Di Salvo V, et al. Body fat assessment in elite soccer players: cross-validation of different field methods. Sci Med Footb. 2018. https://doi.org/10.1080/24733938.2018.1445871 .

Golja P, Robic Pikel T, Zdesar Kotnik K, Flezar M, Selak S, Kapus J, et al. Direct comparison of (Anthropometric) methods for the assessment of body composition. Ann Nutr Metab. 2020. https://doi.org/10.1159/000508514 .

Moon JR. Body composition in athletes and sports nutrition: an examination of the bioimpedance analysis technique. Eur J Clin Nutr. 2013. https://doi.org/10.1038/ejcn.2012.165 .

Ackland TR, Lohman TG, Sundgot-Borgen J, Maughan RJ, Meyer NL, Stewart AD, et al. Current status of body composition assessment in sport: review and position statement on behalf of the ad hoc research working group on body composition health and performance, under the auspices of the I.O.C. Medical Commission. Sports Med. 2012;42(3):227–49. https://doi.org/10.2165/11597140-000000000-00000 .

Sansone P, Ceravolo A, Tessitore A. External, internal, perceived training loads and their relationships in youth basketball players across different positions. Int J Sport Physiol Perform. 2021. https://doi.org/10.1123/ijspp.2020-0962 .

Download references

Acknowledgements

We would like to thank all the authors who gently provided us with the original data from their articles and answered our queries, and Dr. Robin Ristl for his precious assistance. This article was supported by the Open Access Publishing Fund of the University of Vienna.

This article was supported by the Open Access Publishing Fund of the University of Vienna.

Author information

Authors and affiliations.

Faculty of Sport Sciences, UCAM - Catholic University of Murcia, Murcia, Spain

Pierpaolo Sansone

University of Applied Sciences Wiener Neustadt, Wiener Neustadt, Austria

Bojan Makivic

Centre for Sports Science and University Sports, University of Vienna, Vienna, Austria

Robert Csapo & Pascal Bauer

Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand

Patria Hume

Department of Analytical Chemistry, Nutrition and Food Sciences, Faculty of Sciences, University of Alicante, Alicante, Spain

Alejandro Martínez-Rodríguez

You can also search for this author in PubMed   Google Scholar

Contributions

PS wrote the manuscript. PS, PB and BM performed the systematic review search. All authors contributed to conception of the systematic review. PS, PB and BM devised the search parameters for the systematic review. All authors contributed to the interpretation of the results. All authors reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Pascal Bauer .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors, Pierpaolo Sansone, Bojan Makivic, Robert Csapo, Patria Hume, Alejandro Martínez-Rodríguez and Pascal Bauer, declare that they have no competing interests with the content of this article.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Sansone, P., Makivic, B., Csapo, R. et al. Body Fat of Basketball Players: A Systematic Review and Meta-Analysis. Sports Med - Open 8 , 26 (2022). https://doi.org/10.1186/s40798-022-00418-x

Download citation

Received : 13 September 2021

Accepted : 06 February 2022

Published : 22 February 2022

DOI : https://doi.org/10.1186/s40798-022-00418-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Team sports
• Physique assessment
• Reference values
• Playing level
• Anthropometry

Validity of 3-D Markerless Motion Capture System for Assessing Basketball Dunk Kinetics – A Case Study

Authors: Dimitrije Cabarkapa 1 , Andrew C. Fry 1 and Eric M. Mosier 2

• Jayhawk Athletic Performance Laboratory, University of Kansas, Lawrence, KS
• Northwest Missouri State University, Maryville, MO

Corresponding Author: Dimitrije Cabarkapa, MS, CSCS, NSCA-CPT, USAW 1301 Sunnyside Avenue, Lawrence, KS 66045 University of Kansas E-mail: [email protected] Phone: +1 (785) 551-3882

Basketball is one of the most popular international sports, but the current sport science literature does not directly address on-court performance such as force and power during a game. This case study examined the accuracy of a three-dimensional markerless motion capture system (3-D MCS) for determining the biomechanical characteristics of the basketball dunk. A former collegiate (NCAA Division-I) basketball player (age=26 yrs, height=2.08 m, weight=111.4 kg) performed 30 maximum effort dunks utilizing a two-hands, no-step, two-leg jumping approach. A uni-axial force plate (FP) positioned under a regulation basket sampled data at 1000 Hz. Additionally, a 3-D MCS composed of eight cameras placed 3.7 m high surrounding the recording area collected data at 50 Hz, from which ground reaction forces were derived using inverse dynamics. The dunks were analyzed by both systems for peak force and peak power. Peak force (X±SD) was similar (p<0.05) for both systems (FP= 2963.9±92.1 N, 3-D MCS= 3353.2±255.9 N), as was peak power (FP= 5943±323, 3-D MCS= 5931±700 W). Bland-Altman plots with 95% confidence intervals for both force and power indicated all measurements made with the 3-D MCS accurately assessed peak force and peak power during a basketball dunk as performed in the current study. These data provide strength and conditioning professionals with a better understanding of the magnitude of forces and powers that athletes experience during a basketball game, as well as validate use of a novel technology to monitor athletes’ progress and optimize overall athletic performance.

NCAA Realignment: Impact upon University ‘Olympic’ Sports

Authors: Stephen W. Litvin, Crystal Lindner and Jillian Wilkie

Corresponding Author: Stephen W. Litvin, DBA Professor, School of Business College of Charleston 66 George Street Charleston, South Carolina 29424 [email protected] 843-953-7317

Stephen Litvin is a professor in the School of Business of the College of Charleston.  Crystal Lindner and Jillian Wilkie are students at the College of Charleston and Research Assistants within the School’s Office of Tourism Analysis.

NCAA Realignment: Impact upon University ‘Olympic’ Sports

Conference realignment has in recent years led to a “case of intercollegiate musical chairs” (2, p. 254). This research paper looks at the issue from a new perspective.  While past research has almost exclusively focused on football, this research considers the impact that affiliation change has upon universities’ non-football sports.  The findings suggest the move has been challenging for these teams.

Special Edition: Refuting IOC’s Plan to End Modern Pentathlon Competition

The recent decision of the International Olympic Committee (IOC) to drop the modern pentathlon from the Olympic Games has prompted Dr. Thomas P. Rosandich, president of the United States Sports Academy, and the editors of The Sport Journal to publish a special edition bringing attention to this grave matter. We join the call that has gone out from various quarters to retain the modern pentathlon. It is a vital component of the Olympic Games and an important historic tradition. The special edition features the opinions of several IOC members, reproduced from four sources.

The first source is an abridged version of a letter from Klaus Schormann, president of the Union Internationale de Pentathlon Moderne (UIPM), to IOC President Jacques Rogge:

Monaco, 5 November 2002

According to our discussion during our last meeting in Lausanne [Switzerland], the UIPM is sending a summary of its arguments and response to the Program Commission report which it feels appropriate to be considered for the sport of modern pentathlon to remain in the Olympic program. These arguments, which cover a larger spectrum than those developed by the Program Commission, should be given to the IOC executive board prior to their last meeting in November, and to the IOC members in case the matter would be voted during the session in Mexico.

I. Answer to the arguments of the Program Commission

— Lack of global participation by nations and individual athletes Ninety-four nations from five continents are now affiliated with the UIPM (more are coming, as they are in establishment procedure), while the Olympic Charter requires 75 nations in four continents. The sport meets the criteria of the Olympic Charter. We want to remind that Pierre de Coubertin founded the sport in 1912 from scratch, on the model of the ancient pentathlon, the symbolic and complete sport of the Ancient Games, which means that this sport has never stopped growing since its creation.

—Significant expense of practicing the sport, with resulting difficulties in major development Modern pentathlon is not more significantly expensive than most of the other Olympic sports or than those willing to enter the Olympic program. The change of its format to the one-day in 1992 and the new shooting system (air pistols instead of guns) have reduced the costs for organizing and training. Facilities already used by other sports are also for modern pentathlon, inside and outside of Olympic Games, for competing, training, and studying. The new compactness of venues in many cities gives new possibilities for modern pentathlon. The reduction of the costs for sport equipment (including horse riding) brings new possibilities. It is to be noted that pentathletes do not need to have a horse of their own, are not charged for that in competitions, and that the use of local horses does not require any guarantee.

—High operational complexity Experience with organization of UIPM events on all continents and in the previous Olympic Games shows that all organizers were able easily to offer facilities for the five disciplines of modern pentathlon (shooting, fencing, swimming, riding, running) within walking distance. It is to be noted that no specific venue is required for the modern pentathlon, and that UIPM has developed a policy of polyvalent international technical officials. Modern pentathlon helps to a more efficient use of venues used at Games time. The official report of the XXVII Olympiad made by SOCOG makes a clear statement on this.

— Relatively low broadcast and press coverage The relatively low broadcast stated by the Program Commission does not fit the statistics established by the UIPM, which can easily be checked. . . . All major UIPM events on all five continents were covered by international TV during the last seven years. Due to its TV coverage, the UIPM has developed a successful marketing program . . . which is in very good standing in comparison with other Olympic sports.

II. Arguments which should be taken into consideration by the IOC to keep modern pentathlon in the Olympic program

— Modern pentathlon is the only sport that has ever been created in its entirety by Pierre de Coubertin and the IOC, as the Ancient sports were created by the Ancient Greeks, and therefore [has] a symbolic value within the Olympic Games. It was especially designed on the model of the ancient pentathlon in order to show all possible skills developed, through five sport events, in one single athlete, and not for a massive number of participants. It is important for the sake of the Olympic tradition.

—Modern pentathlon, from the skills it develops, has an educational value. [It is] a complete sport: On the physical side, swimming, running are the basic disciplines; on the mental side, shooting requires stress control and a precise technique; on the intellectual side, fencing requires adaptability and intelligence; riding an unknown horse requires a mix of adaptability, self-control, and courage.

—Modern pentathlon has an entertainment function at the Olympic Games. Since the Atlanta Olympic Games and the introduction of the one-day format, the interest of spectators at Games time has grown dramatically, which can be easily shown by statistics on the number of spectators at the Sydney Games (full venue and 15,000 spectators per session) and by an independent survey published in the Olympic Review.

— An Olympic sport with reasonable number of athletes and with a high representation of NOCs. Only 32 women and 32 men, a total of 64 athletes (in fact around 0.5% of the total athletes number), competing for only two days (six medals), which means that modern pentathlon, as one of the 28 sports of the Olympic program, has a very limited impact on the overall number of athletes in the Games. Remarks: The average number of athletes for the other sports is (10500 – 64) /27 = 386/ At the same time, modern pentathlon gives to many NOCs the possibility to take part in the Olympic Games. In Sydney 48 pentathletes competed while 24 NOCs were represented. This means 50% of the quota was dedicated to NOCs’ representation, which is the highest value of all Olympic sports.

— A drug-free sport. Since the one-day format has been created and due to the permanent efforts of the UIPM, modern pentathlon has become a drug-free sport. The one-day format has discouraged prohibited behaviors, as there is no interest in using drugs for shooting when fencing comes right after it. Anabolic substances are not useful in a sport that does not place the success of the winner only on his physical skills, but in his overall physical and intellectual harmony.

—UIPM, a flexible organization. In addition to the changes in the modern pentathlon’s format, the UIPM has created an ad hoc commission looking at the optimal evolution of the sport for the future. The purpose is to keep to symbolic construction of modern pentathlon in placing its complete skills first, but looking, at the same time, at its events in order to fit with the evolution of sport practice in general. This commission already collaborates with the International Pierre de Coubertin Committee and intends to do the same with the other international federations and the IOC.

—Modern pentathlon is a symbolic sport for the Olympic Movement. Modern pentathlon is a true representation of the Olympic Movement. The five Olympic rings are reflected in modern pentathlon’s five events and participation from all five continents. It is a true sport of the Olympic Games, created by the founder of the Modern Games, Pierre de Coubertin, and reflecting the ideals embodied by the Olympic Movement. It has to remain an indefatigable part of it.

The concept and the philosophy of the pentathlon are 2,710 years old, as described by Aristotle: “The most perfect sportsmen are the pentathletes, because in their bodies strength and speed are combined in beautiful harmony.” Created by the Greeks and renovated by the founder of the [Modern] Games, it shows the symbolic complete athlete in his body, will, and mind as stated and described in Fundamental Principle 2 of the Olympic Charter. Let’s keep this part of the soul of the Olympics, let’s keep it on the field of play, let’s see it on the stadium, and not only in the Olympic Museum in the future!

The 28 Sports of the Olympic Program, Participating NOCs, and Disqualification Quotas

The second source reproduced in this special edition is HSH Prince Albert Monaco’s address to the IOC in Switzerland on behalf of the cause of the modern pentathlon:

HSH Prince Albert reaffirms Modern Pentathlon as soul of Olympic Movement, to be maintained for the sake of olympic tradition & values

I’m here not only because I am the honorary president of the UIPM, nor because Monaco is host to the headquarters of the UIPM. I’m here above all as an IOC member who is fearful that some very important part of the values and the philosophy of the Olympic Movement handed down to us by Baron Pierre de Coubertin might be lost forever if modem pentathlon should disappear from the program. The cultural dimension of this sport, its ancient roots and the educational value of its different components, are an important legacy for the IOC, for the Olympic Movement. This dimension is more important than the sport itself; the consequences of its demise larger than any one of us in this room.

Some people will argue that tradition and values are not the only elements that should guide us. If you look around you, watch TV, or read a newspaper article, you will find quite a few people saying the opposite: that a society has lost points of reference, that values have diminished. Why not continue to provide our youth with the kind of values and symbol that this sport possesses, and that they obviously are looking for? Why challenge a sport that celebrates and showcases the versatile, complete athlete? According to the latest figures from the Sydney Olympic Games, more people than ever seem interested in watching athletes test their abilities in combined events.

Is it right to deny the development of a sport that is growing in popularity and has sustained youth programs? There is a quotation from a young Cuban athlete in your brochure, “I want to compete in modem pentathlon at the Beijing Olympic Games.” Is it right to deny Jose Fernandez and his friends the opportunity to realize his dreams in an existing Olympic sport?

Having said all this, we are not stifled in tradition, we are not dinosaurs, we are willing to be open to change, if it is for the better.

The American philosopher and author Tom Wolfe once wrote, in his book The Search for Excellence,   “We must learn to accept change, as much as we hated to in the past.” I’m sure he meant changes in our society, changes in behavior, changes in economics, etc., not changes in our values.

The values of education and culture, and understanding through sport, are everlasting and something we in the Olympic Movement should hold sacred.

The third source reproduced in the special edition is a further communication written by Klaus Schormann, UIPM president:

I am just back in my home after a lot of traveling. . . . In Busan during the Asian Games (modern pentathlon was included, with the whole competition-program: individual women/men and relay women/men and team-medal. I could speak with a lot of IOC members, NOC presidents, and media people. As you can see [Table 2], my schedule for the next weeks is very busy; therefore, I think we should meet in Colorado Springs at the GAISF meeting (20 to 24.11.2002). I send you some documents about the “IOC Program Commission” and our actions now, for your information. UIPM needs from all institutions of international-sport-scene support: Public statements . . . for modern pentathlon are needed.

UIPM President Klaus Schormann’s Schedule, September to December 2002

The fourth source reproduced in the special edition is an abridged version of a UIPM press release dated 8 October 2002:

UIPM Delegation Visits IOC Regarding the Olympic Program; HSH Prince Albert Reaffirms Modern Pentathlon as the Soul of the Olympic Movement, to be Maintained for the Sake of Olympic Tradition and Values; International Pierre De Coubertin Committee and DeCoubertin’s Family Call for Pentathlon’s Respect and Promotion

On 4 October, a UIPM delegation composed of President Klaus Schormann, Honorary President HSH Prince Albert of Monaco, First Vice President Juan Antonio Samaranch, and Secretary General Joel Bouzou was welcomed at the IOC headquarters by IOC President Jacques Rogge, accompanied by Sport Director Gilbert Felli and his new assistant, Olivier Lenglet.

The purpose of the meeting was to answer to the Program Commission’s recommendation to the IOC executive board and to present additional arguments to be considered by the IOC executive board before their final decision during their meeting in Mexico City, 26 and 27 November.

After the opening by IOC President Rogge, UIPM President Klaus Schormann referred to the letter sent to the IOC that answered the points raised by the technical report of the Program Commission. [As Schormann noted,] “We now have more than 95 countries in the five continents. . . . De Coubertin started the sport from scratch in 1912, and the media coverage of our events has dramatically increased since the adoption of the one-day format. Our sport is only using existing venues during the Games and therefore is not expensive, as stated in the report. Equally, compact venues in modern cities allow more and more pentathletes to practice the sport and combine it with studies.

President Schormann also mentioned the surveys made during the last Olympic Games by an independent observer, Prof. Dr. Mfiller from the research group of the Gutenberg University in Mainz, and by SOCOG, which both support the UIPM counter-arguments. Dr Rogge confirmed that he took into account the point made by President Schormann concerning the flexibility of UIPM in terms of the sports evolution.

UIPM Secretary General Bouzou recalled that modem pentathlon does not need any specific venue for the Games; that most modem cities have multisport complexes adapted to the organization of modem pentathlon; that nine modem pentathlon major competitions are seen on international TV in the five continents; that, as stated by SOCOG (in a post-Games report), “[T]he quality of competition and sports presentation, combined with the most comprehensive television coverage ever of modem pentathlon in Olympic Games history, ensured first-class viewing for live spectators and global television audiences.” He also acknowledged the fact that modem pentathlon is not, and will never be, practiced by millions of athletes throughout the world. However, it was never designed for this by the founder of the Games, Pierre de Coubertin, but to be used as a living symbol of all values within a single sport. This was the reason why exceptional personalities like General Patton or Chevalier Raoul Mollet chose this sport in their respective athletic times.

UIPM Vice President Samaranch reminded that 15,000 spectators attended each of the two days of modem pentathlon at the Sydney Olympic Games, in sold-out venues, and that there are only 64 athletes competing in modem pentathlon, which represents only 0.5% of the overall number, and, therefore, that taking the sport out of the program would not affect the reality in terms of cost.

IOC President Rogge, following the presentation of all the arguments, informed the UIPM delegation that he would ensure they would all be duly reported on to the IOC executive board.

Professor Dr. Norbert Muller, president of the International Pierre de Coubertin Committee, wrote a letter to the IOC president saying that he had been “informed with great regrets about the proposal of the program commission,” adding that, “this sport represents the real legacy of Pierre de Coubertin, which he elaborated personally when he wanted to showcase the Perfect Olympic Man or Woman.” [Muller] transmitted an appeal from the committee, saying, “[T]he personal legacy of Pierre de Coubertin should be respected and modem pentathlon permanently included.”

Mr. Geoffroy de Navacelle de Coubertin, the great-nephew of Pierre de Coubertin, also wrote to the IOC president, saying, “Let me tell you my astonishment and my emotion. I have always decided not to interfere with the IOC business. I am simply concerned in making sure that the achievements and the philosophy of Pierre de Coubertin will be respected. This sport is the most symbolic one in showing the perfect athlete. Should you not promote and support it in order to make it grow, instead of only promoting ‘specialists’ which media like so much?” De Coubertin had contacted Schormann . . . in order to create a permanent Pierre de Coubertin Commission within UIPM, that he would lead, the role of which will be to promote the philosophy of the founder “on the ground,” particularly through modem pentathlon events, in close cooperation with the International Pierre de Coubertin Committee, throughout the entire world. The Pierre de Coubertin Commission was established 1 October 2002, comprising the following members: de Coubertin, Schormann, Muller, Bouzou, and modern pentathlon Olympic champions Dr. Stephanie Cook [of Great Britain] and Janus Peciak [of Poland].

Author’s Note:

Correspondence regarding this articLEwhould go to:

Union Internationale de Pentathlon Moderne (UIPM) Tel. +377,9777 8555 Fax.+377 9777 8550 E-mail: [email protected] For more on Pentathlon, visit the website: http://www.pentathlon.org 08.10.2002/ JB

A Review of Service Quality in Corporate and Recreational Sport/Fitness Programs

This article is a review of the literature related to the study of service quality in corporate and recreational sport and fitness programs. It considers earlier discussions of conceptualization and operationalization aspects of consumers’ perceptions of service quality. It reviews several models used by researchers in the past, as well as more recent approaches to understanding the constructs of service and service quality and the various means used to measure them.

Quality of service has been studied within the discipline of business management for years, because the market is increasingly competitive and marketing management has transferred its focus from internal performance (such as production) to external interests like customer satisfaction and customers’ perceptions of service quality (Gronroos, 1992). However, the concept of service quality has only recently—over the last two decades—gained attention from sport and recreation providers and those who study them (Yong, 2000). The service-quality framework known as SERVQUAL comprises a traditional disconfirmatory model and was the first measurement tool to operationalize service quality. Although it made a contribution to the field of service quality and was very popular among service-quality researchers in many areas, SERVQUAL proved insufficient due to conceptual weaknesses in the disconfirmatory paradigm and to its empirical inappropriateness.

Later service-quality frameworks included a greater number of dimensions than SERVQUAL offered. Most recent models, such as Brady’s (1997) hierarchical multidimensional model, have synthesized prior approaches and suggest the complexity of service-quality perception as a construct. Because of this complexity, despite numerous efforts in both business management and the sport/fitness field, the study of service quality is still in a state of confusion. No consensus has been reached on its conceptualization or its operationalization of consumers’ perceptions of service quality.

Service and Service Quality

Service quality has long been studied by researchers in the field of business management. However, they have reached no consensus concerning how the service quality construct is best conceptualized or operationalized. In presenting the literature that reflects this lack of consensus, it is first necessary to focus on the definitions and characteristics of service and service quality. The concept of service comes from business literature. Many scholars have offered various definitions of service. For example, Ramaswamy (1996) described service as “the business transactions that take place between a donor (service provider) and receiver (customer) in order to produce an outcome that satisfies the customer”(p. 3). Zeithaml and Bitner (1996) defined service as “deeds, processes, and performances” (p. 5). According to Gronroos (1990),

A service is an activity or series of activities of more or less intangible nature that normally, but not necessarily, take place in interactions between the customer and service employees and /or systems of the service provider, which are provided as solutions to customer problems. (p. 27)

Some researchers have viewed service from within a system-thinking paradigm (Lakhe & Mohanty, 1995), defining service as

a production system where various inputs are processed, transformed and value added to produce some outputs which have utility to the service seekers, not merely in an economic sense but from supporting the life of the human system in general, even maybe for the sake of pleasure. (p. 140)

Yong (2000) reviewed definitions of service and noted the following features of service that are important to an understanding of the concept. First, service is a performance. It happens through interaction between consumers and service providers (Deighton, 1992; Gronroos, 1990; Ramaswamy, 1996; Sasser, Olsen, & Wyckoff, 1978; Zeithaml & Bitner, 1996). Second, factors such as physical resources and environments play an important mediating role in the process of service production and consumption (American Marketing Association, 1960; Collier, 1994; Gronnroos, 1990). Third, service is a requirement in terms of providing certain functions to consumers, for example problem solving (Gronroos, 1990; Ramaswamy, 1996). From these points Yong (2000) concluded that “a service, combined with goods products, is experienced and evaluated by customers who have particular goals and motivations for consumers for consuming the service.” (p. 43)

Among researchers generally, there is no consensus about the characteristics of service. According to Yong (2000), their various conceptualizations fall into two groups. First, there are those researchers who view the concept from the perspective of service itself. They pay attention to the discrepancy between marketing strategies for service and goods, in an approach that differentiates service (intangibles) from goods (tangibles). The suggestion is that distinct marketing strategies are appropriate for the two concepts. Parasuraman, Zeithaml, and Berry (1985) as well as Zeithaml and Bitner (1996) identified the following features of service that distinguish it from goods: Service is intangible, heterogeneous, simultaneous, simultaneous in production and consumption, and perishable.

Pointing out the unique features of service advances understanding of the concept, but it has drawn criticism, for example because the identified features are not universal across service sectors. As Wright (1995) noted, first, a service industry depends more on tangible equipment to satisfy customers’ demands, while some customers do not care whether or not goods are tangible. Second, some service businesses are well standardized; an example is franchise industries (Wright, 1995). In addition, some customers value equality and fairness in the service provided. Third, many services are not simultaneously produced and consumed (Wright, 1995). Fourth, highly technological and equipment-based services could be standardized. Critics other than Wright (Wyckham, Fitzroy, & Mandry, 1975) have argued that the four-point approach to service ignores the role of customers.

The second group of researchers conceptualizing service comprises those who view service from the perspective of service customers. These researchers focus on the utility and total value that a service provides for a consumer. This approach points out that service combines tangible and intangible aspects in order to satisfy customers during business transactions (Gronroos. 1990; Ramaswamy, 1996). The approach implies that because consumers evaluate service quality in terms of their own experiences, customers’ subjective perceptions have great impact upon service businesses’ success or failure (Shostack, 1997).

Conceptualization and Operationalization of Service Quality

Although researchers have studied the concept of service for several decades, there is no consensus on how to conceptualize service quality (Cronin & Taylor, 1992; Rust & Oliver, 1994), in part because different researchers have focused on different aspects of service quality. Reeves and Bednar (1994) noted that “there is no universal, parsimonious, or all-encompassing definition or model of quality” (p. 436). The most common definition of service quality, nevertheless, is the traditional notion, in which quality is viewed as the customer’s perception of service excellence. That is to say, quality is defined by the customer’s impression of the service provided (Berry, Parasuraman, & Zeithaml, 1988; Parasuraman, Zeithaml, & Berry, 1985). This definition assumes that customers form a perception of service quality according to the service performance they experience and in light of prior experiences of service performance. It is therefore the customer’s perception that categorizes service quality. Many researchers accept this approach. For example, Bitner and Hubbert (1994) defined quality as “the consumer’s overall impression of the relative inferiority/superiority of the organization and its services” (p. 77). But their definition of service quality differs from that of the traditional approach, which locates service quality perception within the contrast between consumer expectation and actual service performance (Gronroos, 1984; Lewis & Booms, 1983; Parasuraman, Zeithaml, & Berry, 1985; Parasuraman, Zeithaml, & Berry, 1990).

Parasuraman, Zeithaml, and Berry (1985) viewed quality as “the degree and direction of discrepancy between customers’ service perception and expectations.” According to this approach, services are different from goods because they are intangible and heterogeneous and are simultaneously produced and consumed. Additionally, according to the disconfirmation paradigm, service quality is a comparison between consumers’ expectations and their perceptions of service actually received. Based on the traditional definition of service quality, Parasuraman, Zeithaml, and Berry (1985) developed their gap model of perceived service quality. The model incorporates five gaps: (a) the gap between management’s perceptions of consumer expectations and expected service, (b) the gap between management’s perceptions of consumers’ expectations and the translation of those perceptions into service-quality specification, (c) the gap between translation of perceptions of service-quality specification and service delivery, (d) the gap between service delivery and external communications to consumers, and (e) the gap between the level of service consumers expect and actual service performance. This disconfirmation paradigm conceptualizes the perception of service quality as a difference between expected level of service and actual service performance. The developers of the gap model proposed 10 second-order dimensions consumers in a broad variety of service sectors use to assess service quality. The 10 are tangibles, reliability, responsiveness, competence, courtesy, credibility, security, access, communication, and understanding (Parasuraman et al., 1985).

Using these 10 dimensions, Parasuraman et al. (1988) made the first effort to operationalize the concept of service quality. They developed an instrument to assess service quality that empirically relied on the difference in scores between expectations and perceived performance. Their instrument consisted of 22 items, divided along the 10 second-order dimensions, with a seven-point answer scale accompanying each statement to test the strength of relations. The 22 items were used to represent 5 dimensions, ultimately: reliability, responsiveness, tangibles, assurance, and empathy. Yong (2000) described the five as follows:

Reliability refers to the ability to perform the promised service dependently and accurately. Responsiveness reflects the willingness to help a customer and provide prompt service. Tangible refers to the appearance of the physical facilities, equipment, personnel and communication material. Empathy refers to caring, individualized attention the firm provides its customer. (p. 66)

In their seminal study, Parasuraman and colleagues used SERVQUAL to measure service quality as the gap between expectation and perception in several venues: an appliance repair and maintenance firm, retail banks, a long-distance telephone provider, a securities broker, and credit card companies (Parasuraman et al., 1985). The study provided a comprehensive conceptualization of service quality, and it marked the first time, in service-quality research, that an instrument for measuring perceived service quality was used. It became very well known among service-quality researchers.

However, numerous researchers challenged the usefulness of the SERVQUAL scale as a measure of service quality (e.g., Babakus & Boller, 1992; Brown, Churchill, & Peter, 1993; Carmen, 1990; Cronin & Taylor, 1992; Dabholkar, Thorpe, & Rentz, 1996). Carmen (1990) selected four service settings that were quite different from those in the original test and found that in some situations, SERVQUAL must be customized (items added or edited), despite its introduction as a generic instrument measuring service quality in any sector. In addition, Carmen suggested that SERVQUAL’s five dimensions are insufficient to meet service-quality measurement needs, and that measurement of expectation using SERVQUAL is problematic. Finn and Lamb (1991) argued that “the SERVQUAL measurement model is not appropriate in a retail setting” (p. 487). Furthermore, they argued, “retailers and consumer researchers should not treat SERVQUAL as an ‘off the shelf’ measure of perceived quality. Much refinement is needed for specific companies and industries” (p. 489). According to Brown, Churchill, & Peter (1993) SERVQUAL’s use of difference between scores causes a number of problems in such areas as reliability, discriminate validity, spurious correlations, and variance restriction. Finally, Cronin and Taylor (1992) argued that the disconfirmation paradigm applied by SERVQUAL was inappropriate for measuring perceived service quality. The paradigm measures customer satisfaction, not service quality, and Cronin and Taylor’s study employing solely the performance scale SERVPERF showed SERVPERF to outperform SERVQUAL.

SERVQUAL’s shortcomings result from the weakness of the traditional disconfirmatory definition of service quality which it incorporates. Yong (2000) notes several problems in this traditional definition of service quality. First, customers’ needs are not always easy to identify, and incorrectly identified needs result in measuring conformance to a specification that is improper. Schneider and Bowen (1995) pointed out that

[C]ustomers bring a complex and multidimensional set of expectations to the service encounter. Customers come with expectations for more than a smile and handshake. Their expectations include conformance to at least ten service quality attributes (i.e., Parasuraman et al.’s 10 dimensions—reliability, responsiveness, competence, access, courtesy, communication, credibility, security, understanding, and tangible).” (p. 29)

Second, the traditional definition fails to provide a way to measure customers’ expectations, and expectations determine the level of service quality. Because customer expectations may fluctuate greatly over time (Reeves & Bednar, 1994), a definition of quality based on expectation cannot be parsimonious. It is invalid, empirically speaking, to use the disparity of scores for expectation and scores for perceived service quality to measure service quality.

Oliver (1997) is another researcher who pointed out the traditional model’s difficulty distinguishing service quality from satisfaction. While perception of quality may come from external mediation rather than experience of service, consumers must experience satisfaction in person. In addition, judgments and standards of quality are based on ideals or perceptions of excellence, while judgments concerning satisfaction involve predictive expectations, needs, product category norms, and even expectations of service quality. Moreover, while judgments concerning quality are mainly cognitive, satisfaction is an affective experience (Bitner & Hubbert, 1994; Oliver, 1994). Service quality is influenced by a very few variables (e.g, external cues like price, reputation, and various communication sources); satisfaction, in contrast, is vulnerable to cognitive and affective processes (e.g., equity, attribution, and emotion). Quality is primarily long-term, while satisfaction is primarily short-term.

Discussing various analyses in terms of their definitions of service quality, Yong (2000) pointed out that service quality should not be defined using a disconfirmation paradigm (i.e., by comparing expectation and perceived quality). Indeed, since service quality may not necessarily involve customer experience and consumption, the disconfirmation paradigm does not clarify service quality (Yong, 2000). Furthermore, it is easier to measure service quality if judgment occurs primarily at the attribute-based cognitive level. Yong (2000) stated as well that customer perception of quality to date has been the main focus of service-quality research; consumers’ overall impressions determine service quality. Yong (2000) argues that what constitutes service changes from one service sector to another, so each sector’s consumers may perceive service quality differently, and that service quality is multidimensional or multifaceted. Finally, according to Yong (2000), service quality must be clearly differentiated from customer satisfaction.

Several researchers have approached service quality from perspectives quite different from that of Parasuraman et al. (1988). On the one hand, some scholars argue for multidimensional models of service quality. At first, Gronroos (1984) used a two-dimensional model to study service quality. Its first dimension was technical quality, meaning the outcome of service performance. Its second dimension was functional quality, meaning subjective perceptions of how service is delivered. Functional quality reflects consumers’ perceptions of their interactions with service providers. Gronroos’s model compares the two dimensions of service performance to customer expectation, and eventually each customer has an individual perception of service quality. McDougall and Levesque (1994) later added to Gronroos’s model a third dimension, physical environment, proposing their three-factor model of service quality. This later model consists of service outcome, service process (Gronroos, 1984), and physical environment. McDougall and Levesque (1994) tested the model with confirmatory factor analysis, using the dimensions of the SERVQUAL scale (which provided empirical support for the three-factor model). The three components from the above models, together with Rust and Oliver’s (1994) service product, represent one important aspect of services. All of them contribute to consumers’ perception of service quality (Yong, 2000).

On the other hand, Dabholkar, Thorpe, and Tentz (1996) proposed a hierarchical model of service quality that describes service quality as a level, multidimensional construct. That construct includes (a) overall consumer perception of service quality; (b) a dimension level that consists of physical aspects, reliability, personal interaction, problem solving, and policy; and (c) a subdimension level that recognizes the multifaceted nature of the service-quality dimensions. Dabholkar and colleagues found that quality of service is directly influenced by perceptions of performance levels. In addition, customers’ personal characteristics are important in assessing value, but not in assessing quality.

The two lines of thought on the modeling of service quality were combined by Brady (1997). He developed a hierarchical and multidimensional model of perceived service quality by combining Dabholkar, Thorpe, and Tentz’s (1996) hierarchical model and McDougall and Levesque’s (1994) three-factor model (Brady, 1997). Brady’s model incorporates three dimensions, interaction quality, outcome quality, and physical environment quality. Each dimension consists of three subdimensions. The interaction quality dimension comprises attitude, behavior, and expertise subdimensions. The outcome quality dimension comprises waiting time, tangibles, and valence. Finally, the physical environment quality dimension comprises ambient conditions, design, and social factors. Brady’s hierarchical and multidimensional approach is believed to explain the complexity of human perceptions better than earlier conceptualizations in the literature did (Dabholkar, Thorpe, & Rentz, 1996; Brady, 1997). Furthermore, empirical testing of Brady’s model shows the model to be psychometrically sound.

In a study of service quality in recreational sport, Yong (2000) further developed Brady’s (1997) model, proposing that perception of service quality occurs in four dimensions. The first is program quality: the range of activity programs, operating time, and secondary services. The second is interaction quality, or outcome quality. The third is environment quality. Yong tested his model with a two-step approach of structural equation modeling, and he supported multidimensional conceptualization of service-quality perception.

Perception of service quality is quite a controversial topic; to date no consensus has been reached on how to conceptualize or operationalize this construct. In its summarization of the existing literature about service quality, this article explored the concepts of service, service quality, consumer perception of service quality, and the conceptualization and operationalization of the service-quality concept. It covered several models of service quality, the earliest one of which was SERVQUAL. An application of the traditional disconfirmatory model, SERVQUAL represents the first effort to operationalize service quality. Although it made a great contribution to the field and was very popular among service-quality researchers in many areas, SERVQUAL is now thought to be insufficient because of conceptual weaknesses inherent in the disconfirmatory paradigm and also because of its empirical inappropriateness. Service-quality researchers working after SERVQUAL’s introduction proposed models containing additional dimensions. Brady developed a hierarchical and multidimensional model of perceived service quality by combining the ideas of earlier researchers. The relatively recent approaches like Brady’s (1997) utilize ideas seen in earlier models, yet more fully represent the complexity of the concept of service-quality perception.

American Marketing Association (1960). Dynamic Marketing for a Changing World. Chicago, IL: AMA.

Babakus, E., & Boller, G. W. (1992). An empirical assessment of the SERVQUAL scale. Journal of Business Reaearch, 24, 253-268.

Berry, L. L., Parasuraman, A., & Zeithaml, V. A. (1988). The service-quality puzzle. Business Horizon, 31(5), 35-43.

Bitner, M. J., & Hubbert, A. R. (1994). Encounter Satisfaction versus Overall Satisfaction versus Quality: The Customer’s Voice. In Rust, R.T., & Oliver, R.L. (Eds.). Service Quality: New Directions in Theory and Practice. Thousand Oaks, CA: Sage, 72-94.

Brady, M. K. (1997). Re-Conceptualizing Perceived Service Quality: Hierarchical Model. Unpublished doctoral dissertation, The Florida State University.

Brown, T. J., Churchill, G. A. & Peter, J. P. (1993). Improving the measurement of service quality. Journal of Retailing, 69(1), 127-139.

Carmen, J. M. (1990). Consumer perceptions of service quality: An assessment of the SERVQUAL dimensions. Journal of Retailing, 66(1), 33-55.

Collier. D. A. (1994). The Service /Quality Solution: Using Service Management to Gain Competitive Advantage. Milwaukee, WI: ASQC Quality Press.

Cronin, J. J., & Taylor, S. A. (1992). Measuring service quality: A reexamination and extension. Journal of Marketing, 56(July). 55-68.

Dabholkar, P., Thorpe, D. I., & Rentz, J. Q. (1996). A measure of service quality for retail stores. Journal of the Academy of Marketing Science. 24(Winter). 3-16.

Deighton, J. (1992). The consumption of performance. Journal of Consumer Research, 19(December), 362-372.

Gronroos, C. (1984). A service quality model and its marketing implications. European Journal of Marketing, 18, 36-44.

Gronroos, C. (1990). Service Management and Marketing: Managing the Moment of Truth in Service Competition. Lexington, MASS: Lexington Books.

Gronroos, C. (1992). Service Management: A Management Focus for Service Competition. IN Lovelock, C.H. Managing Services: Marketing, Operations, and Human Resources (Eds.). Englewood Cliffs, NJ: Prentice Hall, 9-16.

Lakhe, R. R., & Mohanty, R. P. (1995). Understanding TQM in service system. International Journal of Quality & Reliability Management,12(9), 139-153.

Lewis. R. C., & Booms, B. H. (1983). The Marketing Aspects of Service Quality. In Berry, L., Shostack, G., & Upah, G. (Eds.). Emerging Perspectives on Service Marketing. Chicago, IL: American Marketing, 99-107.

McDougall, G. H. G., & Levesque, T. J. (1994). A revised view of service quality dimensions: An empirical investigation. Journal of Professional Service Marketing, 11(1), 189-209.

Oliver, R. L. (1994). Conceptual Issues in the Structural Analysis of Consumption Emotion. Satisfaction, and Quality: Evidence in a Service Setting. In Allen, C.T., & John, D.R. (Eds.). Advances in Consumer Research, Vol. 21. Association for Consumer Research. Provo. UT, 16-22.

Oliver, R. L. (1997). Satisfaction: A Behavioral Perspective on the Consumer. New York, NY: McGraw-Hill.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research, Journal of Marketing, 49(Fall), 41-50.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1990). Moving Forward in Service Quality Research: Measuring Different Customer Expectation Levels, Comparing Alternative Scales, and Examining the Performance-Behavioral Intentions Link. MSI Report # 94-114.

Ramaswamy, R. (1996). Design and Management of Service Processes: Keeping Customers for Life. Reading, MA: Addison-Wesley Publishing Co.

Reeves, C. A., & Bednar, D. A. (1994). Defining quality: Alternatives and implications. Academy of Management Review, 19, 419-445.

Rust, R. T., & Oliver, R. L. (1994). Service quality: Insights and managerial implications from the frontier. In Rust, R.T. & Oliver, R. L. (Eds.). Service Quality: New Directions in Theory and Practice. Thousand Oaks, CA: Sage Publications.

Sasser. W. E., Olsen, R. P., & Wyckoff, D. D. (1978). Management of Service Operations: Text and Cases. Boston, MA: Allyn & Bacon Schneider, B., & Bowen, D. E. (1995). Winning the Service Game. Boston, MA: Harvard Business School Press.

Shostack, G. L. (1977). Breaking free from product marketing. Journal of Marketing,41(April), 73-80.

Wright. L. K. (1995). Avoiding Service Marketing Myopia. In Glynn, W. J. & Barnes, J. G. (Eds.). Understanding Service Management: Integrating Marketing, Organizational Behavior, Operations and Human Resource Management. West Sussex, England: John Wiley & Sons Ltd., 33-56

Wyckham, R. G., Fitzroy, P. T., & Mandry, G. D. (1975). Marketing of services: An evaluation of theory. European Journal of Marketing, 9(1), 59-67.

Yong, J. K. (2000). A multidimensional and hierarchical model of service quality in the participant sport industry. Unpublished doctoral dissertation, The Ohio State University.

Zeithaml, V. A., & Binter, M.J. (1996). Service Marketing. New York. NY: The McGraw-Hill Companies, INC.

ADR Fundamentals

©2002 Adam Epstein, J.D./M.B.A. Removed due to copyright until advised otherwise.

THE PLIGHT OF THE PRE-PENSION PLAYERS Find out how the league's pioneer players are getting the short end of the stick. For more information on the Pre-1965 NBA Players Association, or to order copies of Vintage NBA from the Pre-1965 NBA Players Association [for only $20, including shipping], write to: Bill Tosheff XNBA.org The Pre-1965 NBA Players Association 1455 2nd Ave. Suite 1402 San Diego, CA 92101 619-899-2504 & 619-234-3500

BASKETBALL LOGO INDEX PAGE Major leagues [NBA, ABA, ABL, etc.], minor leagues [CBA, USBL, etc.], women's leagues [WBL, WNBA, ABL, etc.]

HISTORICAL BASKETBALL STATISTICAL DATABASE Online statistical database for the BAA/NBA, NBL and ABA [Courtesy of Bob Chaikin]

NBDL National Basketball Developmental League 2001-02 to Present NBL National Basket Ball League 1898-99 to 1903-04 NBL National Basketball League 1926-27 NBL National Basketball League1929-30 NBL National Basketball League1932-33 NBL National Basketball League1937-38 to 1948-49 NBL National Basketball League [Canada] 1993 to 1994 NPBL National Professional Basketball League 1950-51 NRL National Rookie League 2001 to Present NEBA New England Basketball Association 1904-05 NEBL New England Basketball League 1903-04 NEL New England League 1946-47 NYSL New York State League 1911-12 to 1916-17, 1919-20 to 1922-23 NYSPL New York State Professional League 1946-47 to 1948-49 NABL North American Basketball League 1964-65 to 1967-68 PCPBL Pacific Coast Professional Basketball League 1946-47 to 1947-48 PSL Pennsylvania State League 1914-15 to 1917-18, 1919-20 to 1920-21 PBL Philadelphia Basket Ball League 1902-03 to 1908-09 PBLA Professional Basketball League of America 1947-48 SBL Southern Basketball League 1947-48 to 1948-49 SBL Southwest Basketball League 1997-98 to Present ABA/EBA/UBA Atlantic Basketball Association/Eastern Basketball Alliance/United Basketball Alliance 1993-94 to Present USBL United States Basketball League 1985 to Present WBA Western Basketball Association 1974-75 WBA Western Basketball Association 1978-79 WMBL Western Massachusetts Basket Ball League 1903-04 WPBL Western Pennsylvania Basket Ball League 1903-04 WPBL Western Pennsylvania Basket Ball League 1912-13 WPBL Western Pennsylvania Basket Ball League 1914-15 WNBA Women's National Basketball Association 1997 to Present Women's Professional Basketball 1936 to Present  · AllAmericanRedheads.com [John Molina]  · EdmontonGrads.com [John Molina]  · History of Women's Basketball [John Molina]  · Machine Gun Moll - WBL Legend [John Molina]  · Women's Basketball League [John Molina] WBL Women's Professional Basketball League 1978-79 to 1980-80 WBL World Basketball League 1988 to 1992

PRO BASKETBALL LISTS Team Abbreviations 100 Greatest Players of the 20th Century 10 Greatest Teams of the 20th Century ABA/NBA Exhibition Games Al Hoffman's Adjusted Stats - Introduction  · 1946-1967 · 1967-1976 · 1976-1988 · 1988-2000 · 1967-1976 · 1976-1988 · 1988-2000 · 1967-1976 · 1976-1988 · 1988-2000 All-Star Game Participants All-Time Greats Bill Spivey's Professional Career Highlights History of Basket Bowl A fictional account of the ABA/NBA championship game Deceased Players European League Champions Hall of Fame Inductees Left-Handed Players Most Teams in a Career and a Season NBA/ABA Attendance History BAA/NBA/ABA Finals/Championship Series Participants Most Games With One Franchise Entire Career With One Franchise Relatives in the NBL/BAA/NBA/ABA Wilt Chamberlain Career Retrospective

BARNSTORMING TEAMS AND PROFESSIONAL TOURNAMENTS Harlem Globetrotters All-Time Roster Harlem Globetrotters - Minneapolis Lakers Box Scores Jackie Robinson and the Los Angeles Red Devils 1941 Rosenblum Tournament World Professional Basketball Tournament World Series of Basketball

AMATEUR BASKETBALL LISTS Amateur Athletic Union/National Industrial Basketball League History Olympic Games NCAA All-American NCAA Annual Awards NCAA Tournament NCAA Yearly Final Polls BAA/NBA/ABA DRAFT HISTORY BAA/NBA

• 1946-47 to 1959-60
• 1960-61 to 1969-70
• 1970-71 to 1979-80
• 1980-81 to 1989-90
• 1990-91 to 2000-01
• 2001-02 to 2010-11

Arch Manning stars in Texas Longhorns' spring game

Longhorns fans get their first look at Arch Manning, who starred with 355 yards and three touchdowns in Texas' Orange-White spring game. (2:54)

AUSTIN, Texas -- In his most extensive action since arriving at Texas , Arch Manning put on a show in the Orange-White game Saturday, and he didn't take long to do it.

Manning threw a 75-yard touchdown on his first pass attempt, started 10-for-10 and finished the first half 11-for-13 for 189 yards with two touchdowns. His first incompletion occurred with 12 seconds left in the second quarter, and his second on a throw off the hands of Isaiah Bond in the end zone.

Texas didn't provide statistics, but according to ESPN Stats & Information, Manning finished with 355 yards and three touchdowns with one interception while completing 19 of 26 attempts. At least four of those incompletions were catchable passes. His pocket presence and confidence was an important showing considering Quinn Ewers missed five games in the past two years, backup Maalik Murphy transferred to Duke , and Texas returns just 16% of its receiving production from last season after losing its top five pass-catchers.

Sarkisian said the plan all along was to limit Ewers to one or two series, because he is entrenched as a third-year starter after throwing for 3,479 yards and 22 touchdowns with six interceptions as Texas made the College Football Playoff. On Saturday, Ewers' first drive ended with defensive end Colton Vasek tipping a pass that was grabbed by defensive tackle Alfred Collins , who ran it back for a touchdown. Ewers said after the game that he knew his time would be short.

"I know what Quinn's about," Sarkisian said. "Quinn's had a great spring."

After Manning made an appearance in just two games last season, against Texas Tech in a blowout win and in the final series of the Big 12 title game, he got a chance to take the majority of his team's snaps in this game for the first time.

"I wanted Arch to be able to just go play football. He hadn't really played in a year," Sarkisian said. "When he keeps his eyes up and steps up in the pocket, he can deliver those balls down the field the way we like to play. It was good to see, and it's good to see some of the guys around him play with him the way that they did.

"We're very fortunate at the quarterback position to have a third-year starter to have the backup that we have."

Sarkisian told ESPN's Chris Low this week that Manning has been patient, despite his famous last name and the proliferation of quarterback transfers around the country.

"The majority of guys like Arch have always been the best their whole life," Sarkisian said. "Then they get to college and it's like, 'Wait, I'm not the starter?' No, but we're going to develop you in a way that when you do become the starter, you're going to play great. You're not going to have to go through some of these growing pains that some of these other guys go through with their freshman and sophomore year. We're going to keep training you in a way that when your number does get called, you're going to play really good football."

He did that on Saturday, but Sarkisian also praised the performance of true freshman quarterback Trey Owens, a four-star recruit who helped offset Manning's performance on the other team in a game Sarkisian called "the most exciting spring game I've ever been a part of."

"Not to take a shot of those that put stars on quarterbacks, but I trust our evaluation, and we could probably recruit any quarterback in the country," Sarkisian said. "But Trey Owens is really talented and can make a lot of throws, so I'm very encouraged with where we're at, at the quarterback position."

The Longhorns were excited to see the performance of freshman Ryan Wingo, a 6-foot-2, 200-pound wide receiver from St. Louis who was No. 33 in the 2024 ESPN 300. Wingo caught two touchdowns. Sophomore DeAndre Moore caught Manning's 75-yarder to open the game. Bond, the Alabama transfer who led the Crimson Tide with 48 catches last season, worked his way into the offense.

Sarkisian possibly foreshadowed some transfer portal priorities when he said the Longhorns still need more "big humans" along the defensive line after losing NFL draft prospects T'Vondre Sweat , the Outland Trophy winner, and Byron Murphy II . But otherwise, he feels really good about the Longhorns' outlook.

"I think we're a very talented football team," Sarkisian said. "What excited me today is that playmakers made plays and that's something that you try to recruit to. ... I think we're very good. And I think that we have a chance to do some really good things."

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

The NBA and Youth Basketball: Recommendations for Promoting a Healthy and Positive Experience

John p. difiori.

1 Primary Care Sports Medicine, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021 USA

Arne Güllich

2 Department of Sports Science, University of Kaiserslautern, Kaiserslautern, Germany

Joel S. Brenner

3 Department of Pediatrics, Children’s Hospital of The King’s Daughters Eastern Virginia Medical School, Norfolk, VA USA

Jean Côté

4 School of Kinesiology and Health Studies, Queens University, Kingston, ON Canada

Brian Hainline

5 NCAA, Indianapolis, IN USA

Edward Ryan, III

6 USA Basketball, Colorado Springs, CO USA

Robert M. Malina

7 Department of Kinesiology and Health Education, University of Texas, Austin, TX USA

Participation in sports offers both short-term and long-term physical and psychosocial benefits for children and adolescents. However, an overemphasis on competitive success in youth sports may limit the benefits of participation, and could increase the risk of injury, burnout, and disengagement from physical activity. The National Basketball Association and USA Basketball recently assembled a group of leading experts to share their applied research and practices to address these issues. This review includes the group’s analysis of the existing body of research regarding youth sports participation and the related health, performance, and psychosocial outcomes. Based upon this, age-specific recommendations for basketball participation are provided that aim to promote a healthy and positive experience for youth basketball players.

Participation in youth sports such as basketball offers many potential benefits for children and adolescents. Youth sport participation provides an avenue to develop peer relationships, self-esteem, and leadership qualities [ 1 ]. It may also lay the foundation for an active and healthier adult lifestyle [ 2 – 4 ]. Basketball has one of the highest rates of youth sport participation and is well suited to offer young athletes opportunities to obtain these benefits.

However, an overemphasis on competitive success in youth sports may impede children from realizing the benefits of participation, and may ultimately limit their ability to reach their athletic potential. Such a highly-competitive approach may be driven by desires for children to gain placement on elite travel teams, secure high school roster spots, obtain collegiate scholarships, and eventually earn professional contracts. This focus on early results rather than playing sport for enjoyment and the long-term physical and psycho-social benefits has led to several well-recognized issues:

• Pressure to begin high-intensity training in childhood.
• Single-sport specialization that occurs prior to adolescence.
• Frequent and multiple competitive event scheduling.
• Increased risk for injury, burnout, and disengagement from health-promoting physical activity both in the short term and the long term.

The idea that single-sport training at young ages increases the prospect of future sport success has been popularized in the media, but there are few scientific data to support this approach. Yet, there is a fear among parents, coaches, and young athletes that not specializing in one sport early will place the child at a competitive disadvantage. In fact, research indicates that early sport specialization is not a pre-requisite and may even be detrimental to long-term achievement and elite performance [ 5 – 14 ]. There is also a concern that excessive focus on sport-specific intensive training and competition at a young age may impede an athlete’s ability to develop transferable athletic skills, and possibly increase the risk of burnout and overuse injury, rather than optimize participation and foster interest in a variety of sports [ 15 – 20 ]. Regarding the relationship between injury and early single-sport specialization, the data at this time are limited and do not provide consistent evidence [ 21 – 26 ].

Aim and Procedure

In 2016, the Jr. NBA partnered with USA Basketball to address issues facing youth basketball in the USA. As part of this initiative, a multidisciplinary team of clinicians and researchers with expertise in athlete development and youth sports was assembled.

This group assessed the existing research related to youth sport participation, focusing on the sport of basketball. A series of seven meetings were held from May 2016 to July 2017 to review these data. From this, the recommendations for best practice in youth basketball were developed. Each recommendation was classified using the Strength of Recommendation Taxonomy system (SORT, Table  1 ) [ 27 ].

Table 1

Strength of recommendation taxonomy (SORT)

Basketball Participation

Basketball has high levels of participation for girls and boys across all age ranges, including recreational play and organized competition. Among US youth 6–14 years of age, 14.4 million play basketball, representing 39% of this age group [ 28 ]. Furthermore, basketball is the most popular team sport for those 12–17 years of age, with over 11 million participants. At the high school level, approximately 430,000 girls and 550,000 boys play interscholastic basketball [ 29 ]. Importantly, the top reason for playing basketball, cited by 74% of children and adolescents, is to have fun [ 30 ]. Basketball is a sport that can be modified so that it can be played informally in groupings of one, two, or three players on a side (i.e., one-on-one, two-on-two, or three-on-three). In fact, 50% of children and adolescents cite that one of the reasons they started to play basketball was because it can be played with any number of people [ 30 ]. Such recreational play is also a reason that the game can be enjoyed into adulthood. In addition, wheelchair basketball is a team sport for individuals with chronic conditions resulting in lower-limb disability such as spinal cord injury, cerebral palsy, musculoskeletal conditions, spina bifida, amputation, and poliomyelitis, and a reduced ability to play running basketball in the same manner as able-bodied players [ 31 , 32 ].

Basketball Promotes Healthy Youth

In addition to the psychosocial benefits described above, youth sports can provide participants with other health benefits, including those involving the cardiorespiratory, musculoskeletal, and metabolic systems [ 33 – 39 ]. While physical activity is essential for healthy childhood growth and development, children in the USA and globally are not sufficiently active [ 40 ]. Recent research has shown that the development of fundamental movement skills (FMS) in children is linked to lower levels of overweight, and higher levels of physical activity, cardiorespiratory fitness, and self-esteem [ 41 ]. Mastery of FMS such as the sprint run, vertical jump, and overarm throw, has been shown to be low [ 41 , 42 ]. However, the implementation of FMS programs in schools is effective in improving FMS competencies [ 42 ].

Basketball promotes speed, agility, strength, power, endurance, flexibility, and motor coordination. As a result, basketball is uniquely oriented to improve FMS, and has been shown to be beneficial in promoting general health. In one study, basketball, along with soccer and track, provided middle school children the highest level of physical activity, regardless of the way schools offered the sport [ 43 ]. This is important in light of public health concerns related to obesity and diabetes among youth, while paradoxically, participation in school-sponsored physical education programs is low [ 44 ]. Specifically, the study suggested that basketball can effectively increase physical activity and reduce the long-term negative health consequences of an inactive lifestyle, while being an efficient option in the face of limited school resources.

Basketball can also have a positive effect on bone mineral density (BMD) for boys and girls [ 45 – 47 ]. A prospective study of teenage girls compared basketball players to age-matched controls and found that those who played basketball had significant increases in BMD [ 48 ]. This is important since maximizing BMD at these ages provides the basis for long-term bone health throughout adulthood [ 49 ].

There is also evidence that health benefits obtained via youth sports activity can extend into adulthood [ 50 – 54 ]. For example, physical activity during adolescence predicts lower cardiometabolic risk in adulthood [ 55 ]. In addition, youth sport participation appears to be associated with better mental health in later life [ 56 ]. Importantly, because basketball can be modified to allow participation in various small-sided formats, it is a sport that is conducive for participation well into adulthood, thus yielding health benefits over a wide age range.

Injuries in Youth Sports: How Does Basketball Compare?

Among youth sports, basketball has a relatively low injury rate. A decade-long surveillance study of US high school sports found that basketball consistently had lower injury rates than football, wrestling, and boys’ and girls’ soccer [ 57 ]. With respect to overuse injuries, basketball has a relatively low injury rate at the high school level [ 58 ]. In fact, in a study of high school sports, basketball had the lowest overuse injury rate in boys and the second lowest rate in girls [ 58 ]. In addition, among female middle school athletes, basketball had a lower injury rate than both soccer and volleyball [ 59 ].

Injury Risk Factors and Injury Prevention in Youth Basketball

Risk factors.

Several risk factors for injury in youth sport have been identified, though data specific to basketball are limited. Prior injury, low energy availability, and training volume have been shown to be important risk factors. Previous sport-related injury is perhaps the most-established predictor of subsequent injury [ 60 – 62 ]. Low energy availability, a relative deficit in energy needs, may increase the risk of bone stress injuries in both boys and girls [ 63 , 64 ]. Bone stress injuries that are a result of low energy availability highlight the dangers of excessive training and competition, especially when combined with inadequate provision for re-fueling and recovery [ 63 – 65 ]. A weekly training time of > 16 h per week among 14- to 18-year-old youth has been correlated with injury risk [ 66 – 68 ]. As in most sports, the injury rate in basketball is greater in competition than practice [ 69 , 70 ]. In addition, youth athletes who participated in organized sports compared to peer-led play at greater than a 2:1 ratio were found to have an increased injury risk [ 22 , 71 ]. However, the actual risk associated with different amounts of participation still needs validation [ 22 , 71 ]. In addition to training volume, the risk of injury may be greater during the adolescent growth spurt, though further study is needed [ 15 , 16 ].

It is not clear if these data are generalizable to basketball or to more structured sport training settings. Research is also needed to guide long-term, sport-specific development programs.

Injury Prevention

Data on injury prevention programs for sports in general and for basketball in particular are limited. In addition, very little research has focused specifically on injury prevention among young athletes. Aimed at providing youth athletes with a standardized warm-up designed to prevent non-contact knee and lower extremity injuries in soccer, the original FIFA 11 program and the more recent FIFA 11+ modification have had a favorable effect in decreasing certain soccer injuries [ 72 – 75 ]. The program consists of 15 exercises that include running, active stretching, core strength, balance, and agility. A recent study using the FIFA 11+ program in high-level European basketball players also reported a reduction of injury in several categories [ 76 ]. A similar neuromuscular training program has been shown to be effective in high school basketball players [ 77 ].

Other studies have focused on improving balance to decrease injury rates. Such studies have included adolescent and professional basketball players, and have been shown to be effective in reducing acute injuries including ankle and knee sprains, as well as back injuries [ 78 , 79 ]. A program aimed at preventing hamstring injuries has been validated in soccer, but has not yet been studied in basketball [ 80 ].

Strength and conditioning programs may play a role in injury prevention as well. These programs can be safely performed by young athletes if properly implemented and supervised. In particular, preseason conditioning programs appear effective in reducing injuries [ 81 – 87 ].

An often-overlooked component of athlete development and injury prevention is rest. In a study of high school athletes, a 42% increase in self-reported overuse injuries was noted among those who participated all year compared to those who trained in three or fewer seasons per year [ 88 ]. At least one rest day per week, and additional periods of time away from organized sports, are recommended for physical recovery and to avoid burnout [ 15 , 16 ]. In addition, sports events or “tournaments” that involve more than one full-length competition per day, in some cases for multiple consecutive days, may in some circumstances increase injury risk further due to the high-volume loading coupled with limited recovery time [ 15 , 16 ].

Thus, neuromuscular training programs, including a modified FIFA 11+ program, appear promising for reducing lower extremity injuries and should be considered for broader implementation trials in youth basketball. Recent consensus statements from the American Orthopedic Society for Sports Medicine (AOSSM) and the International Olympic Committee (IOC) are supportive of such measures [ 71 , 89 ]. Measures that focus on monitoring and managing training volume—including scheduled rest and recovery, ensuring proper treatment when injuries occur, and addressing issues of relatively low energy availability and bone health—are warranted [ 64 ]. Injury prevention programs aimed at reducing hamstring injuries appear valid but need further study in basketball players.

Early Single Sport Specialization—The Road to Success?

Single sport specialization can be defined as intensive year-round training in one sport to the exclusion of others [ 90 ]. A perception exists among parents, athletes, and coaches that early single-sport specialization is necessary for long-term success. This can lead to a focus on short-term results at young ages rather than the overall development process.

The concept of early sport specialization was popularized in the USA more than 20 years ago based upon studies of chess players and musicians, but not athletes [ 91 ]. The central tenet of this model is that an individual’s ultimate level of performance is directly related to the accumulated amount of deliberate practice (DP). The authors advocated the maximization of DP, which implies an early start, intensification, and subsequent expansion of DP. It was suggested that 10 years of DP is needed to achieve the highest performance levels [ 91 ].

In contrast, the state of empirical research in athletes does not provide much support for these perceptions. Several studies have shown that competitive success at the youth level correlates modestly at best, or not at all, with long-term senior success [ 8 , 9 , 92 – 94 ]. That is, early success has been described as neither a necessary precondition nor a valid predictor of long-term success.

A recent review highlighted that the participation patterns that likely lead to youth success are not the same as those that facilitate long-term development and adult success [ 95 ]. Short-term youth success is indeed correlated with early single sport specialization and intensified, sport-specific practice/training during childhood (age ≤ 12 years) and adolescence (13–18 years) [ 9 , 96 – 102 ]. In contrast, adult world-class athletes from all Olympic sports and different countries typically engaged in only moderate levels of early practice/training intensity in their respective primary sport. Reports of world-class athletes in basketball, field hockey, and soccer show that they attained international success accumulating much less than 10,000 practice h, specifically 4000–4500 h [ 11 , 12 , 81 ]. In this context, world-class athletes (e.g., Olympic and World Champions, medalists or top-ten athletes) did not differ from national-class peers in terms of the amount of sport-specific youth practice/training [ 5 , 9 , 11 , 12 , 95 , 103 – 105 ]. Interestingly, several studies indicate that eventual world class athletes had a relatively lower level of sport-specific training during childhood [ 5 , 9 , 103 , 106 ]. Further, international-level performers typically participated in a diverse set of sport activities, including peer-led play, and organized practice in various sports. Importantly, world-class athletes were more likely than national-class peers to engage in multiple sports [ 13 , 95 , 106 ]. These athletes specialized in their primary sport significantly later than their national-class peers [ 5 , 9 – 12 , 106 ]. These findings have been confirmed even when comparing Olympic and World Championship medalists to non-medalists [ 13 , 106 ]. These findings were also consistent across different countries and types of sports, and were confirmed in a 3-year prospective study [ 9 ].

Further, closer scrutiny of the “micro-structure” of practice of German world-class soccer players highlighted the significance of play . Within their total childhood soccer activities, only 14% involved drill-like training of technical skills or physical conditioning. As much as 86% was a combination of coach-led play (17%; including conditioned, small-sided games), and peer-led play (69%; “kicking around with friends”) [ 11 ].

These observations do not diminish the critical significance of organized sport-specific practice on specific skill development. However, it should be recognized and placed into perspective that early reinforced intensification and specialization is unnecessary and may even be detrimental to long-term success. Alternatively, the interaction of sport-specific practice with multisport practice and play facilitates long-term development [ 9 , 17 , 19 , 90 , 106 , 107 ].

The preceding discussion presumably relies on the interplay of three processes. First, in addition to training volume per se , single-sport specialization may constitute an independent risk factor of overuse injury. Diversified involvement may reduce susceptibility to overuse injury, presumably due to less cumulative stereotypical mechanical impact on certain tissues and may promote prolonged participation [ 15 , 17 , 19 , 22 , 24 , 90 , 107 ]. Second, youth who have explored various sports may make the decision to invest in one primary sport based on their own experiences in different sports. This likely enhances the probability that a child or adolescent elects a primary sport that optimally “fits” him or her (where “optimal fit” may represent talent at a particular sport, experienced performance progress, enjoyment, health, social interaction, etc.) [ 8 , 9 ]. Third, physical conditioning and perceptual-motor and psychological skills can be directly transferred across related sports [ 108 – 112 ]. Perhaps more importantly, early variable learning experiences improve the efficacy of (later) practice within the primary sport (greater performance improvement per invested practice time) [ 5 , 9 , 11 , 12 , 14 , 106 , 113 ]. Athletes acquire a multifaceted repertoire in terms of a wider and closer-meshed “network” of perceptual-motor skills, which facilitates the emergence of functional skill solutions [ 95 , 114 ]. Play , in particular, unlike drill-like practice exercises, involves the interaction of situation dynamics, perception, and motor solutions, and also provides extensive implicit skill learning. This may lead to more robust skills exhibiting less susceptibility to physiological or psychological stress and better retention [ 115 , 116 ]. Peer-led play, for its part, may further amplify tasks and situations (playing different roles/positions, varying rules, surfaces, court sizes, balls, number and skill level of participants) [ 95 ]. Moreover, exploring varying practice designs and learning modes can facilitate the development of individual functional learning solutions , leading to more adaptive, “smarter learners” [ 95 , 106 , 113 ]. In this context, it is important to note that the multisport participation of world-class athletes constituted authentic experiences in that it typically included multi-year competition -related engagement—i.e. long-term dedicated, performance-related learning processes with specialist coaches in broadened ranges of tasks and situations [ 9 , 11 , 12 , 14 , 106 ].

Consistent with this discussion, a number of reviews and position statements have highlighted the potentially negative effects of early specialization and the positive impact of diversified youth experiences among sports and settings [ 13 , 15 , 16 , 71 , 83 , 90 , 117 – 124 ]. This reinforces the idea that childhood/adolescence multisport engagement facilitates long-term performance development—in association with positive health and psychosocial development. At the program and sport system level, this contributes to the growth of prolonged youth sport participation and expands the potential pool of talented youth athletes. In contrast, reinforcing early specialization likely diminishes general participation and the “talent pool.”

Personal Engagement as a Model for a Positive and Successful Youth Basketball Experience

The findings described above have also been highlighted in applied frameworks informing policy-makers and stakeholders of the sport system, such as FTEM (Foundations, Talent, Elite, Mastery) or the DMSP (Developmental Model of Sport Participation) [ 6 , 118 , 125 , 126 ]. The practitioner-derived FTEM highlights the socio-environmental, organizational, and sport-system requirements and applications, while the DMSP more particularly looks into the psychosocial influences and outcomes in terms of positive youth development. Both frameworks emphasize the foundational role of early diversified involvement for either developing sporting excellence or prolonged recreational engagement. This section focuses on the DMSP.

The DMSP posits that personal engagement in sport grows from involvement in sport activities, relationships, and environments that evolve throughout development [ 127 ]. Combining personal engagement in sport with early sport sampling promotes a rewarding youth experience and long-term sport success [ 128 ]. For either recreational or competitive basketball, personal engagement is a primary objective of participation during youth. For this to occur, resources to develop personal meaning are needed, including: access to appropriate sport environments and role models; activities that provide personal relevance; a positive social climate; encouragement in the face of difficulty; opportunities for leadership, challenge, and knowledge-building; and opportunities to feel in-control, competent, and connected with others [ 129 – 134 ].

Within the DMSP, the sampling years lay an important foundation for youth to achieve optimal outcomes in sport over time [ 130 ]. Sampling generally begins during childhood, and is characterized by participation in a variety of different sports, as well as different activities within a given sport (e.g., peer-led play, organized coach-led practice). Following the sampling years, athletes may continue to participate in sport at a recreational level or begin to invest more and perhaps specialize in one sport during adolescence or later.

At any stage within the DMSP, youth may choose to disengage from sport; however, nurturing individual capacities for personal engagement throughout development enhances opportunities for physical and psychosocial development. By focusing on the personal, social, and physical features of different activities (e.g., interest, play, practice, sampling, specialization) across development, the DMSP suggests that the positive outcomes of sport result from the integration of processes that include personal engagement in a sport activity, the social relationships that are formed within this activity, and the physical environment in which this activity takes place [ 135 ]. More recently, the features of the DMSP have been integrated with previous youth sport research and principles from developmental systems theories to create the Personal Assets Framework for sport (PAF) [ 136 – 138 ].

The PAF is, in essence, a set of key elements that should be combined to provide quality sport programs for youth that not only contribute in a positive way to the overall development and well-being of the person, but also to the development of talent in sport. In line with developmental systems theories, the PAF considers personal (i.e., personal engagement in activities), relational (i.e., quality relationships), and environmental factors (i.e., appropriate social and physical settings) as the elements necessary to understand the mechanisms through which development occurs in and through sport. The interaction of the three dynamic elements constitutes a specific sport experience—for example, a game, practice, or team social activity. When repeated over a period of time, such as the span of one season, the specific sport experiences generate changes in an athlete’s personal assets (e.g., confidence, competence, connection, and character) and provide personal meaning to the sport being practiced. Eventually, changes in the personal assets will influence the long-term outcomes of sport in relation to the individual’s participation, performance, and personal development [ 139 , 140 ].

The PAF highlights the dynamic elements and personal assets that should be combined in youth basketball programs that promote performance, participation, and personal development. Different lines of research on sport expertise and youth sport demonstrate that the objective of elite performance and continued participation are not mutually exclusive during childhood and that effectively designed sport programs for children can contribute to the overall development of youth in sport [ 141 ].

Two concepts regarding sport involvement throughout the lifespan consistently emerged from the empirical data that support an early sampling approach: diversity and peer-led play [ 136 ]. Firstly, the concept of diversity describes a level of involvement in different types of sport experiences during childhood (e.g., participating in different sport activities, or playing different positions within a sport activity), before specializing and intense training in one sport. Secondly, the concept of peer-led play relates to the notion that elite-level athletes engaged in sport activities during childhood that were inherently enjoyable and differed from organized sport and adult-led practices [ 136 ]. Peer-led sport-play activities represent a distinctive form of sport activities that add to the breadth of contexts and experiences of the youth sport environment. Together, the concepts of diversity and peer-led play form the backbone of the sampling years and may have a protective effect against burnout, dropout, and/or injuries [ 17 – 19 , 142 ].

At a population level, youth sport programs that focus on diversity before specialization and play before practice may better maximize the potential impact that youth sport activities can have on youth development and long-term performance in sport. As suggested by the different pathways of the DMSP, the diversity and play aspect of sport activities during the sampling years should not be viewed as a discriminating factor that predicts sport expertise, but rather as a foundation for optimal development in an elite performance or recreational pathway. The nurturing of talent through sampling without an intense focus on performance in one sport during childhood can have more positive outcomes and less negative consequences for all children involved in sport, while still facilitating the development of expertise.

Growth, Maturation, and Readiness for Basketball

Sport readiness is the relationship between a child’s stage of growth and development and the physical and cognitive requirements of that sport [ 143 ]. Understanding that motor skills as well as social and emotional development influence a young athlete’s ability to perform physical tasks and to understand instructions is essential to promote a rewarding experience. Given inter-individual variability, chronologic age is not a reliable marker for these development levels [ 15 ].

It is clear that if a child is expected to learn too many skills that are beyond his or her ability, the child may become less motivated to learn new skills, and may eventually cease participation in the sport [ 144 ]. Conversely, a child who begins to master new tasks will develop a feeling of competence that may motivate further skill acquisition, and further interest in the sport [ 143 ].

Coaches and parents who are not aware of these issues may unintentionally create unrealistic expectations that can cause children and adolescents to feel as if they are not making progress, especially compared to chronological peers who may simply be at a different stage of growth and maturation. This, in turn, can result in loss of self-esteem and sport discontinuation [ 144 ].

Although there is no straightforward way to determine if a child is ready for basketball or another sport, important factors to consider include sport-related skills, knowledge about the sport, motivation, and socialization [ 142 , 145 ]. Parents and youth coaches should recognize the need to nurture young athletes. It is important to appreciate that even for talented individuals, the ups and downs along the path might be more related to biological maturation than to specific coaching and training techniques [ 123 ].

Conclusions

Basketball, both competitive and recreational, is a sport that has many positive attributes with respect to health and wellness. It involves moderate to high levels of sustained activity, has a relatively low injury rate, engenders positive psychosocial interactions, and is perceived as a fun game to play. The last point is significant in that it encourages long-term involvement, which in turn provides for benefits that extend into adulthood.

Recommendations

Based upon the preceding review of the literature and the consensus of this working group, the NBA and USA Basketball offer the recommendations described in Table  2 for young athletes, parents, coaches, and basketball organizations. Each recommendation is graded using the SORT system [ 27 ].

Table 2

Recommendations for youth basketball participation

a Each recommendation in this table has been classified using the Strength of Recommendation Taxonomy system (SORT) defined in Table  1 [ 27 ]. Recommendations of strength B are based on inconsistent or limited-quality patient-oriented evidence and recommendations of strength C are based on expert opinion consensus

The following guidelines (Tables  3 , ​ ,4, 4 , ​ ,5) 5 ) are based upon the consensus recommendations of the NBA and USA Basketball working groups on Playing Standards and Health & Wellness. These guidelines draw on the available scientific evidence at this time, as well as the expert opinion of the working groups and current and former men’s and women’s players, coaches, and administrators from all levels of basketball. These recommendations may need to be updated as new research and information develops.

Table 3

Recommended participation guidelines

Table 4

Maximum participation guidelines

a Organized basketball includes game competition and practice time and structured training in which an athlete works in a focused way to improve his or her game, typically with or at the direction of a coach. Unstructured peer-led on-court activities do not constitute organized basketball for the purpose of this table (e.g., pickup games, a player shooting baskets by themselves, a player working with a peer to practice a skill). Youth basketball camps can be a positive experience for young players. Camp program content and duration is variable and may exceed the practice guidelines above. Camp directors should, however, keep the above guidelines in mind, and seek to include activities other than on-court basketball as well as rest days. The research team also recommend additional rest days following camp attendance. Residential youth sport academies also exist, particularly outside the USA. Studies in Europe point to earlier specialization, enhanced specific practice intensity and increased risks of impaired well-being, health and academic performance in the sport-students [ 147 ]. Therefore, attention to these issues is warranted. As such, academy directors and coaches should recognize the risks of early specialization and benefits of diversified participation. Their sport curricula should involve activities other than basketball to a significant portion up to age 14 years or beyond, including both organized and non-organized settings

b Youth basketball players, parents, and coaches should demonstrate caution in scheduling or participating in more than one game per day, especially on consecutive days. If young athletes participate in an event or tournament in which more than one game is played per day on consecutive days, players should have additional time off from sports activities following the event to allow for recovery

c It is recommended that young athletes in these age ranges who are approaching the maximum recommended hour limits do not participate in another sport concurrently

Table 5

Rest guidelines

a For 12-year-olds, 9–12 h of sleep is recommended

Implementation and Future Directions

The NBA and USA Basketball are committed to driving positive change in youth basketball that promotes a healthy and positive experience for players. Efforts aimed at the grass roots level is essential for this to occur. To achieve this, these guidelines are now being implemented across their youth programming, and they have partnered with key organizations across youth basketball to similarly endorse and adopt the guidelines. Further, the NBA launched in October 2017 the Jr. NBA Flagship Network to provide a more consistent and positive youth basketball experience for players, parents, and coaches. Members of the network include 15 best-in-class organizations that share the Jr. NBA’s vision for how the game should be taught and played at the grassroots level [ 146 ]. They have committed to adhering to NBA and USA Basketball Youth Guidelines, including USA Basketball coach licensing requirements and providing resources to educate coaches and parents. Finally, the NBA and USA Basketball have begun to assess the extent of basketball participation among youth, the adoption of the basketball guidelines, and the response to this initiative in youth basketball.

No sources of funding were used to assist in the preparation of this article.

Conflict of interest

John DiFiori is the NBA Director of Sports Medicine. Brian Hainline is the Chief Medical Officer of the NCAA. Edward Ryan III is an athletic trainer for USA Basketball. Arne Güllich, Joel Brenner, Jean Côté, and Robert Malina declare that they have no conflicts of interest relevant to the content of this review.

• Election 2024
• Entertainment
• Newsletters
• Photography
• Personal Finance
• AP Investigations
• AP Buyline Personal Finance
• AP Buyline Shopping
• Press Releases
• Israel-Hamas War
• Russia-Ukraine War
• Global elections
• Asia Pacific
• Latin America
• Middle East
• Election Results
• Delegate Tracker
• AP & Elections
• Auto Racing
• 2024 Paris Olympic Games
• Movie reviews
• Book reviews
• Personal finance
• Financial Markets
• Business Highlights
• Financial wellness
• Artificial Intelligence
• Social Media

Many say Biden and Trump did more harm than good, but for different reasons, AP-NORC poll shows

Americans generally think that President Joe Biden and former President Donald Trump did more harm than good on a range of key issues while holding the White House, according to a new poll from the AP NORC Center for Public Affairs research.

In this combination photo, President Joe Biden speaks in Milwaukee, March 13, 2024, left, and former President Donald Trump speaks in New York, Jan. 11, 2024. (AP Photo)

• Copy Link copied

WASHINGTON (AP) — There’s a reason why President Joe Biden and former President Donald Trump are spending so much time attacking each other — people don’t think either man has much to brag about when it comes to his own record. Americans generally think that while they were in the White House, both did more harm than good on key issues.

But the two candidates have different weak spots. For Biden, it’s widespread unhappiness on two issues: the economy and immigration. Trump, meanwhile, faces an electorate where substantial shares think he harmed the country on a range of issues.

A new poll from the AP-NORC Center for Public Affairs Research finds that more than half of U.S. adults think Biden’s presidency has hurt the country on cost of living and immigration , while nearly half think Trump’s presidency hurt the country on voting rights and election security, relations with foreign countries , abortion laws and climate change.

“Considering the price of gas, the price of groceries, the economy — I did very well during those four years,” Christina Elliott, 60, a Republican from Texas, said of the Trump presidency. “I didn’t have to worry about filling up my tank or losing half of my paycheck to the grocery store.”

Elliott wasn’t too keen on Trump’s handling of abortion and said that when it comes to the former president’s rhetoric, “He just needs to learn how to be tactful and shut his mouth.”

“But other than that, like I said, I did very well during the Trump years,” she added.

The polling underscores why certain issues — such as abortion for Biden and immigration for Trump — have been persistent focal points for each of the campaigns. The former president regularly decries the number of asylum-seekers who have arrived in the U.S. under Biden, describing the situation in apocalyptic and dark terms. And Biden has gone on the offensive against Trump on abortion, especially after this week’s ruling from the Arizona Supreme Court that essentially criminalized the procedure in the state.

When asked which president did more to help people like them, roughly one-third say Donald Trump and about one-quarter say Joe Biden. Yet 30% of adults said neither Biden nor Trump benefitted them. It’s another data point reflecting an electorate that has been largely disappointed with this year’s general election choices , generating little enthusiasm among key parts of the Biden and Trump political coalitions.

Americans rate Biden particularly negatively on a few specific issues. Only about 2 in 10 Americans think Biden’s presidency helped “a lot” or “a little” on cost of living, and 16% say that about immigration and border security. Nearly 6 in 10 say his presidency hurt a lot or a little on these issues. Nearly half, 46%, of Americans, by contrast, say that Trump’s presidency helped a lot or a little on immigration or border security. Four in 10 say it helped on cost of living.

Texas resident Trelicia Mornes, 36, said she feels the Biden presidency has hurt a lot when it comes to everyday expenses.

“Now that he’s in the office, the cost of living has spiked out of control, and there’s nothing being done about it,” Mornes, a Democrat, said, pointing to rising costs of rent and food. She said she believes Biden can do more, “He just chooses to do other things.”

The pandemic hurt Trump in terms of employment as the economy lost 2.7 million jobs under his watch. But the pandemic lockdowns also dramatically curbed inflation as the consumer price index dipped from an annual rate of 2.3% to as low as 0.1%. At the same time, low interest rates and historic levels of deficit-funded government stimulus left many households feeling better off under Trump.

Coming out of the pandemic, Biden gave the economy a boost with additional aid that helped spur job gains of 15.2 million under his watch. But supply chain issues, Russia’s war in Ukraine and Biden’s aid package are judged by many economists as having contributed to rising inflation, hurting the Democrat’s approval ratings.

Trump’s advantage on the cost of living and immigration is driven partially by Democrats’ lack of enthusiasm about Biden’s performance. About one-third of Democrats, for example, think Biden’s presidency hurt on cost of living, and another third think Biden neither helped nor hurt. Just one-third of Democrats think Biden’s presidency helped on cost of living. About 3 in 10 Democrats think Biden’s presidency helped on immigration and border security, a similar share think his presidency hurt, and about 4 in 10 think it made no difference.

Nadia Stepicheva, 38, a Democrat from Pennsylvania, is unhappy with how Biden has handled immigration.

“The problem is, I really don’t like illegal type of immigration,” Stepicheva said. She thinks that people who enter the U.S., even if they come in illegally, should be allowed to work so that taxpayer dollars aren’t used to care for them and house them.

Stepicheva said she has always leaned in favor of Democrats and the party’s policies, “But the last four years, I feel like it’s getting too much in terms of money spent for immigration, forgiving all these student loans.” She said she’s torn in terms of who she will vote for this November.

But independents also rate Biden low on these issues: Nearly 6 in 10 independents say Biden’s presidency has hurt the country on cost of living. About 4 in 10 independents say Biden’s presidency has hurt the country when it comes to the cost of health care and relations with other countries.

Trump has a different problem.

The former president doesn’t have any asked-about issues where more than half of Americans think he did more to hurt things than to help, but the overall sense of harm is somewhat broader. Nearly half of Americans think his presidency did more to hurt than help on climate change, voting rights and election security, abortion laws and relations with foreign countries.

Catherine Scott, a Republican who recently moved to New York from Florida, said she found Trump’s approach to foreign policy particularly concerning.

“I understand that some people really admire Trump’s ability to be a spitfire and just say whatever is at the top of his mind,” said Scott, 30. But, pointing to Trump’s complimentary comments toward autocrats like Russian President Vladimir Putin, Scott said, “I don’t think he has all the foresight to understand that might not always be the thing to do.”

The best issue for both Biden and Trump overall is job creation. Trump has a small edge here: Nearly half say his presidency helped, while 36% say Biden’s presidency helped. About half of Americans also think Trump’s presidency helped on immigration and 4 in 10 think his presidency helped on cost of living.

On every other issue, the share of Americans who say that Biden or Trump helped the country a lot or a little is around 3 in 10 or less. But Republicans, overall, tend to see more of a benefit from Trump’s presidency than Democrats do from Biden’s — even on issues where Biden has worked to highlight his victories.

For example, only about half of Democrats say that Biden’s presidency has helped on climate change or the cost of health care. On abortion laws, 77% of Democrats think that Trump’s presidency was at least a little harmful, but only about 4 in 10 say that Biden’s presidency helped a lot or a little, and a similar share think Biden’s presidency hasn’t made a difference.

Meanwhile, around 8 in 10 Republicans say that Trump’s presidency helped on immigration and border security, creating jobs and cost of living.

The poll of 1,204 adults was conducted April 4-8, 2024, using a sample drawn from NORC’s probability-based AmeriSpeak Panel, which is designed to be representative of the U.S. population. The margin of sampling error for all respondents is plus or minus 3.9 percentage points.

Associated Press writer Josh Boak contributed to this report.