The movement demands of basketball game play have been increasingly examined across many time-motion studies (1,2,4,25,27). Consequently, an improved understanding of the game requirements placed on basketball players has emerged. Modern basketball has been characterized as highly intermittent, with changes in movement type reported to occur approximately every 1–2 seconds (1,2,4,25,27). Moreover, high-intensity activity has been observed to account for 16–21% of live time during basketball game play (1,4,25,27). Game demands have also been shown to vary according to playing position, with backcourt players (guards) performing greater intermittent (1,103 vs. 1,022–1,026 total movements) and high-intensity (17.1 vs. 14.7–16.6% of live playing time) workloads than frontcourt players (forwards and centers) (4). Collectively, the observed intermittent, high-intensity requirements of basketball suggest that the ability to rapidly accelerate, decelerate, and change direction is integral to basketball performance and might vary between playing positions.
The measurement of position-specific speed and agility characteristics provides useful information concerning key qualities in basketball. Notably, 2 types of agility are predominant in the literature: closed-skill agility and open-skill agility (30,31). Closed-skill agility assessment involves the measurement of change of direction speed across a preplanned path (31). In contrast, open-skill agility assessment involves measurement of movement speed across an unplanned path in response to relevant stimuli (31). Accordingly, a number of researchers have implemented various tests to assess the linear speed, closed-skill agility, and open-skill agility performance of male and female basketball players of various ages (3,5–7,13,19,21,32). However, no controlled studies have examined linear speed, closed-skill agility, and open-skill agility performance in combination relative to playing position in adult male basketball players. Given the documented importance of speed and agility qualities to basketball performance (3,6,20), such data relative to playing position may provide valuable insight to basketball coaching and conditioning staff. Specifically, positional analyses have been suggested to offer useful information regarding the varied development of and reliance on certain fitness qualities relative to playing position in wider team settings (23,24).
To date, position-specific linear speed and closed-skill agility performance have only been measured in mixed-age (3,21) collegiate (19) male basketball populations. Previously, Ben Abdelkrim et al. (3) observed backcourt players to possess quicker 5-m sprint (21–25%), 10-m sprint (11–13%), and closed-skill agility performance (8–9%) than frontcourt players in mixed-age (16.8–29.6 years) national Tunisian basketball players. In contrast, Montgomery et al. (21) observed comparable performance across 20-m sprint (2–3% difference) and closed-skill agility tests (3% difference) between backcourt and frontcourt groups in mixed-age (19.1 ± 2.1 years) state Australian basketball players. Further, Latin et al. (19) reported quicker 27.4-m (7%) and 36.6-m (6%) sprint times and similar (3% difference) closed-skill agility performance in backcourt players compared with centers in collegiate players. Together, these observations present an equivocal stance on differences in linear speed and closed-skill agility properties relative to playing position in male basketball players. Furthermore, the collective results of these investigations are not transferable to adult basketball players given they represent playing groups containing adolescent and young adult players. Thus, position-specific linear speed and closed-skill agility performance has yet to be determined in adult male basketball players. Given that sprint and closed-skill agility qualities have been shown to vary between age groups in male basketball players (3), the assessment of linear speed and closed-skill agility relative to playing position exclusively in adult male players is warranted.
Although physical attributes have been readily identified as important determinants for basketball performance (3,6,20,22,26,32), limited attention has been given to cognitive properties (13,15). It has been established that team sport athletes are required to frequently perform in-game sprints and directional changes in response to cognitive stimuli (13,30). These cognitive aspects encompass perceptual and decision-making factors, and physical elements are considered to be fundamental during agility performance (30). Accordingly, researchers have begun to investigate the importance of cognitive components during agility performance in sport, measuring open-skill agility qualities in various team sport settings (9–12,13,31). However, open-skill agility assessment has only been performed in junior basketball players (16.3 ± 0.7 years), irrespective of playing position (13). Therefore, the measurement of open-skill agility in adult male basketball players remains to be performed. Further, position-specific open-skill agility data might allow basketball coaching and conditioning staff to discriminate which cognitive qualities are specific to each playing position.
Measurement and comparison of sprint and agility performance between backcourt and frontcourt basketball players are needed to understand the position-specific profiles of key fitness qualities in adult male players. Hence, the aim of this study was to describe and compare linear speed, closed-skill agility, and open-skill agility qualities between backcourt and frontcourt adult semiprofessional male basketball players. Given that backcourt players are required to perform more frequent changes in movement intensity and direction, and a greater volume of sprinting activity than frontcourt players during game play (4); sprinting and agility properties might be better developed in these players. Consequently, it was hypothesized that backcourt players would perform better in speed and agility assessments than frontcourt players.
Experimental Approach to the Problem
A cross-sectional between-subject design incorporating intrateam analyses was used to describe and compare the linear speed, closed-skill agility, and open-skill agility of backcourt and frontcourt adult male basketball players. Intrateam comparisons were made to control for team-related confounding variables, including training routine, match scheduling, competition level, and playing style. Consequently, the group sample sizes adopted in the present investigation are typical of those used for intrateam comparative analyses in basketball players (14,15). To test the hypothesis, subjects were categorized as backcourt (point guards and shooting guards) or frontcourt (small forwards, power forwards, and centers) players based on their predominant playing position during competition as selected by coaching staff (25,27). Backcourt and frontcourt classifications also accounted for “hybrid” players who transition between guard positions or between forward and center positions during training and game play. Subjects were required to complete 2 testing sessions. The initial testing session involved the acquisition of descriptive measures and habitualization of the agility tests. The second testing session involved performance of multiple 20-m sprint, closed-skill agility, and open-skill agility test trials. A schematic of the research design is shown in Figure 1. All testing occasions were conducted on regulation hardwood indoor basketball courts, in similar environmental conditions (temperature, 25.2 ± 2.2° C; relative humidity, 61.1 ± 4.2%; barometric pressure, 757.3 ± 2.2 mm Hg).
Twelve adult male basketball players competing in the Queensland Basketball League (QBL) participated in this study. The QBL is a state-level, semiprofessional, Australian competition that involves a 15-week regular season and 3-week postseason. The investigated team was ranked first out of 15 teams (13 wins and 3 losses). The descriptive and playing characteristics for the backcourt and frontcourt playing groups are displayed in Table 1. Subjects were assessed during the midpoint of the regular season to ensure the development of adequate fitness before study commencement. Leading into testing, subjects had completed 16 weeks of training, including 8 weeks of preseason training and 8 weeks of in-season training. The weekly in-season training schedule was organized across 3 sessions and comprised approximately 80 minutes of conditioning drills, 160 minutes of match practice activity, and 120 minutes of tactical play and skill-based drills. All subjects were prescreened for any health conditions or injuries that contraindicated participation. All subjects were older than 18 years (range: backcourt, 18–37 years; frontcourt, 18–32 years) and voluntarily signed individual written consent forms informing them of the aims, procedures, and risks of the study. This study was approved by the Human Research Ethics Committee of the Central Queensland University and was aligned with the ethical requirements for human experimentation in accordance with the Declaration of Helsinki.
During the consent process, subjects were instructed to maintain consistent dietary and sleeping patterns for 48 hours before testing. Subjects were also asked to refrain from strenuous activity for 24 hours before each testing session and consume approximately 500 ml of water 1 hour before testing to protect against performing in a hypohydrated state. To control for subject arousal, the same 2 researchers were present for all testing and gave standardized verbal encouragement. Both researchers held exercise and sport science university degrees. Furthermore, the lead researcher was a certified strength and conditioning coach with the Australian Strength and Conditioning Association and had occupied a basketball strength and conditioning coaching role for the past 5 years.
A range of descriptive variables were measured during the first testing session. Each subject had their body mass and stature determined using calibrated electronic scales (Tanita, Corporation, Tokyo, Japan) and a portable stadiometer (Blaydon, Sydney, Australia). Each subject then had a 3-site skinfold profile taken (triceps, abdomen, and thigh) by a trained researcher (coefficient of variation [CV], 3.5%) using Harpenden skinfold calipers (John Bull British Indicators, St Albans, United Kingdom). Skinfold measures were then used to calculate body composition through an established prediction equation shown to possess acceptable reliability and validity in athletic populations (8). In groups of 3, subjects then completed a single trial of the Yo-Yo intermittent recovery test (level 1) to estimate maximal oxygen uptake (V[Combining Dot Above]O2max) and provide an indication of player intermittent fitness (18). After sufficient recovery, subjects were habitualized with the procedures for the next testing session.
During the second testing session, subjects completed multiple 20-m sprint, change-of-direction speed test (CODST), and reactive agility test (RAT) trials. Electronic timing lights (Fusion Sport, Coopers Plains, Australia) were used to measure performance time to the nearest 0.001 seconds across each of the sprint and agility trials. Court markings were made for timing light placement to ensure consistent measurement across separate testing sessions. Throughout all timing-based testing, subjects were required to position their leading leg 30 cm behind the start line, to avoid initiating timing prematurely. Subjects were also instructed to avoid backward motions before commencing each effort (31).
Linear Speed Assessment
Subjects performed three 20-m sprints, each separated by a 2-minute recovery period. The dependent variables were performance times at 5-m, 10-m, and 20-m distances, and peak speed. The fastest times for each interval across the 3 trials were taken as outcome measures for each subject. Peak speed was calculated as the average speed between the quickest 10-m and 20-m performance times for each subject.
Closed-Skill Agility Assessment
The CODST was used to assess closed-skill agility performance and has been described elsewhere (31). During the CODST, subjects were required to perform an initial forward sprint, complete a preplanned directional change around a designated point (1.5 m anterior to the starting point), and continue to the relevant end point. The end points of the test were located 2 m in front and 5 m to the left/right of the starting point (31). Electronic timing lights were positioned at the start and end points of the test, with each paired light and reflector separated by 3 m. The layout of the CODST is displayed in Figure 2. The dependent variable across the CODST was performance time, which was determined as the mean time across 6 separate trials (3 trials in each of the left and right directions), each separated by 2-minute recovery periods.
Open-Skill Agility Assessment
The RAT was used to evaluate open-skill agility performance. The RAT has previously been used to assess open-skill agility qualities in Rugby League (10,11), Australian Rules football (29), and basketball players (13). The dimensions and physical requirements of the RAT closely matched with those of the CODST. A high-speed camera (EX-FH100; Casio Computer, Co., Ltd., Tokyo, Japan) was placed 5 m behind the starting point to collect video data for all RAT trials (10). Court markings were made for camera placement to ensure consistent video capture across separate testing sessions. A frame rate of 240 Hz allowed a precision of 4.2 milliseconds during postprocessing analyses. A tester was positioned 5 m anteriorly to the starting point facing the subject. A layout of the RAT is illustrated in Figure 2.
For the RAT, subjects were positioned at the starting point and began a forward sprint in response to movement initiation by the tester. Subjects then completed a single directional change to mirror the direction taken by the tester and continued to the end point of the test. Timing started when the tester initiated movement (detected through video analysis as a break in foot contact with the ground) and ceased when the timing lights were triggered at either end point (10). To initiate the directional change, the tester performed 1 of 4 scenarios, which included the following: (a) step forward with the right foot and move to the left; (b) step forward with the left foot and move to the right; (c) step forward with the right foot, then left, and move to the right; and (d) step forward with the left foot, then right, and move to the left (31). Subjects were randomly exposed to 3 trials of each movement sequence, completing a total of 12 RAT trials.
Subjects were advised to respond to presented cues while moving forward and react as quickly as possible by sprinting through the timing lights positioned at the relevant end point. The importance of decision-making accuracy and movement speed were highlighted to all subjects. The same tester participated across each RAT trial for all subjects, removing intertester variability in cue appearance (31). The dependent variables taken from the RAT included reaction time, decision-making time, and movement time. Reaction time was measured from movement initiation of the tester until the first set of timing lights were triggered by the participant near the starting point. Decision-making time was determined from the first identifiable foot contact initiating directional change of the tester until the first identifiable foot contact initiating the response of the subject (10). Movement time was calculated from movement initiation of the tester until the timing lights at either end point were triggered by the subject. Pilot testing revealed that the test-retest reliability of the RAT was acceptable for all outcome measures in the present subjects (n = 5; intraclass correlation coefficient [ICC], 0.89–0.99; CV, 1.9–2.0%). Further, the intratester reliability of outcome measures derived from video data was supported across test-retest analyses of 60 RAT trials (ICC, 1.00; CV, 0.69%).
Mean values and SD were calculated for all descriptive and outcome measures. Magnitude-based inferences were used to identify positional differences for each dependent variable and have previously been used to analyze intrateam differences (14,15). Furthermore, magnitude-based inferences have been frequently used as an alternative to parametric statistics to overcome small sample sizes and reduce interpretation errors (14,15,17,28). Inference precision was set at 90% confidence intervals and calculated using p values derived from independent t-tests, as previously described (14,15). Inferences on the true differences between backcourt and frontcourt players were calculated using the spreadsheet method of Hopkins (16). Inference calculations determined the chance that the true group differences were positive, neutral, or negative based on the range of the confidence interval relative to the smallest worthwhile practical effect (0.2 multiplied by the between-subject SD, based on Cohen's effect size classification (17)). If the likelihood of positive and negative differences substantially overlapped, then the true effect was considered unclear. Quantitative probabilities of the chance for positive or negative effects were assessed qualitatively as follows: almost certainly not, <1%; very unlikely, 1–5%; unlikely, 5–25%; possible, 25–75%; likely, 75–95%; very likely, 95–99%; and almost certain, >99% (17). All statistical analyses were performed using Microsoft Excel (Microsoft, Corporation, Redmond, WA, USA) and IBM SPSS Statistics (v20.0; IBM, Corporation, Armonk, NY, USA).
Positional comparisons of the descriptive and playing characteristics were performed to identify possible mechanism variables (Table 1). Magnitude-based inferences revealed frontcourt players to have a greater body mass (almost certain), stature (very likely), body fat percentage (likely), mean playing time (likely), and age (possible) than backcourt players. Playing experience (unclear) and intermittent fitness (unclear) were consistent across playing position.
Comparisons of the dependent variables across the sprint and agility tests between playing positions are shown in Table 2. Magnitude-based inferential analyses showed backcourt players to exhibit likely quicker linear sprint times than frontcourt players across 5-m, 10-m, and 20-m distances. However, frontcourt players possessed possible better closed-skill agility performance than backcourt players during the CODST. Furthermore, comparisons revealed consistent performance for reaction time (unclear), decision-making time (unclear), and total movement time (unclear) between backcourt and frontcourt players during the RAT.
To our knowledge, this is the first study to describe the position-specific sprinting and closed-skill agility qualities explicitly in adult male basketball players. Furthermore, the present data are the first for open-skill agility according to playing position in basketball players on the whole. Contrary to our hypothesis, frontcourt players possessed possible superior closed-skill agility performance compared with backcourt players, and unclear positional differences were observed for open-skill agility performance. However, in partial support of our hypothesis, backcourt players had likely better sprint ability than frontcourt players. These observations indicate that backcourt and frontcourt male basketball players might carry distinct sprinting and agility profiles.
The importance of linear speed during basketball game play has been highlighted in a number of time-motion investigations (1,4,12,14,25). Specifically, sprinting has been observed to comprise up to 6% of live playing time in male basketball competition (1,25). Therefore, linear speed is an important measurable attribute for basketball players. Our findings showed that backcourt players possessed likely quicker linear speed measures across 5-m, 10-m, and 20-m distances. These findings are congruent with results reported in other investigations examining basketball players (7,19,21). Previously, similar positional differences have been observed for sprint performance in junior male (21), collegiate male (19), and adult female (7) basketball players. Collectively, our results and those made previously (7,19,21) suggest that backcourt players possess greater linear speed than frontcourt players irrespective of age and gender. These findings might be explained by the game-related functions observed for different playing positions in basketball. Specifically, backcourt players have been suggested to initiate and defend transition and fast-break plays, move extensively on offence without the ball, and perform frequent dribble penetration (33), all of which rely heavily on sprinting and agility maneuvers. In support of this notion, time-motion data have shown backcourt players to execute a greater number of sprints (16–36% difference) and spend a larger proportion of playing time sprinting (8–24% difference) than frontcourt players during game play (4).
In addition to the extensive sprinting requirements, male basketball players have been observed to perform 21–44 changes in movement type per minute of game play (1,4,25). These demands, combined with the small playing area (∼28 × 15 m) of basketball competition, indicate that players are required to undergo repeated changes in direction while maintaining high movement velocities. Consequently, closed-skill agility tests targeted at measuring change of direction speed provide valuable insight for basketball performance during player assessment. Contrary to the positional differences for sprinting performance, we observed frontcourt players to perform possibly better during the CODST. This reversal of positional differences supports previous investigation, which suggests that linear speed and change of direction speed are independent tasks, requiring separate training stimuli for performance optimization (5,34). Thus, the superior closed-skill agility performance observed for frontcourt players might also be related to position-specific tasks typically performed during game play (33). More precisely, frontcourt players likely perform more frequent cutting actions involving single changes in direction compared with backcourt players because defensive and offensive rebounding efficiency, post play, and inside shooting have been identified as the most important functional roles for frontcourt positions (33). These functions are likely to necessitate sharp cuts toward a new plane of progression when pursuing offensive and defensive rebounds and gaining position for ball entry into the post (33). Resultantly, position-specific roles of frontcourt players might predispose to physical and mechanical qualities important for change of direction speed (29). Although these findings provide insight into the position-specific change of direction speed in adult male basketball players, they are not consistent with those reported for more complex closed-skill agility tests in other basketball playing groups (3,7,19,21).
Previously, backcourt players have been observed to possess enhanced closed-skill agility performance than frontcourt players in junior (20), mixed-age (3), collegiate (18), and female basketball groups (7). Comparisons between the present observations and those made previously (3,7,18,20) are difficult considering the differences in age, gender, and playing level of the subjects investigated. In addition, previous closed-skill agility assessments have been largely made using the agility T-test, which involves multiple changes in direction, forward and backward running, and shuffling activity (3,7,19). Superior performance by backcourt players during the agility T-test might be expected because of multiple factors. First, frontcourt players have been observed to have a greater body mass and body fat proportion than backcourt players, and these morphological traits have been shown to be significant (r = 0.58–0.80; p < 0.05) determinants of agility T-test performance in basketball players (5). Second, positional constraints during game play require backcourt players to exert extensive defensive pressure and perform more shuffling activity than frontcourt players (4,33). Therefore, backcourt players might be inherently more adept at performing shuffling activity, which comprises half of the distance traveled during the agility T-test. Therefore, the present observations are specific to closed-skill tasks involving a single change in direction.
Although closed-skill tests permit the assessment of important physical attributes related to basketball activity (2), complete agility performance is also influenced by perceptual and decision-making factors (31). Basketball players are likely to initiate changes in direction while moving at high velocities in response to external cues during game play. Accordingly, the present investigation provides the first position-specific data for open-skill agility properties in basketball players. We found comparable reaction, decision-making, and movement times during the RAT between backcourt and frontcourt players. Although the precise mechanisms for the similarities in open-skill agility performance between positions are difficult to identify given the observational design of our study, it might be theorized that the functions of backcourt and frontcourt positions similarly expose players to movement cues and response patterns akin to those elicited by the RAT. For instance, cutting maneuvers involving single directional changes in response to one-on-one cues are likely to be inherently performed when evading opponents, gaining offensive position, and maintaining defensive contact, irrespective of playing position. Further research examining various cognitive demands specific to playing position during basketball training and game play should be undertaken to elucidate these suggestions. This information might also permit the development of a test that incorporates position-specific basketball contexts (e.g., rebounding, face-guarding, shot contesting, pass anticipation) (33) accompanied by relevant movement responses (e.g., jumping, shuffling, sprinting, multiple directional changes) to more accurately predict perceptual and cognitive constraints (29) of open-skill agility performance relative to playing position in basketball players.
The present investigation provides the first position-specific data for linear speed, closed-skill agility, and open-skill agility performance in adult male basketball players. Magnitude-based inference analyses revealed that backcourt players possessed likely superior sprinting ability than frontcourt players. In contrast, frontcourt players were observed to possess possible quicker change of direction speed than backcourt players. Furthermore, unclear group differences were found between playing positions for open-skill agility properties. Although our work provides valuable insight into the speed and agility qualities of adult male basketball players, the results are only indicative of the team analyzed. Our findings are not intended to be interpreted as normative data for adult male basketball players and might not be representative of other teams in the same league and across different playing levels. Other basketball teams might adopt varied conditioning practices that emphasize different fitness qualities to the players we investigated. Future research should examine position-specific speed and agility qualities in multiple basketball teams competing across varied playing levels.
Our findings provide useful information regarding sprinting and agility qualities relative to playing position in adult male basketball players. It has been suggested that information of this type might provide useful insight to coaches and strength and conditioning professionals for informing training objectives, talent selection, and ultimately enhancing player performance (3). Specifically, our results demonstrated that accelerative and near-maximal speed phases of sprint performance (23) were superior in backcourt players compared with frontcourt players in the present playing group. Based on these findings, it can be postulated that position-targeted speed development programs might necessitate inclusion as a fundamental part of the conditioning plan, as opposed to a more traditional team training focus. Specifically, position-targeted speed training might (a) better develop relevant phases of sprinting in frontcourt players during the preseason phase; and (b) ensure maintenance or improvement of speed qualities in backcourt players across seasonal phases when team training predominates. In contrast, closed- and open-skill agility maneuvers involving single changes in direction were homogenous across playing positions. Consequently, position-specific functions during general team conditioning and game play might impose common single-point directional changes across playing positions. However, it was also identified that further investigation of position-specific responses in basketball players during more complex sport-relevant closed- and open-skill agility tasks should follow our work to identify important constraints of overall agility performance. Regardless, basketball coaching and conditioning staff should be cognizant of both physical and cognitive (e.g., anticipation, visual scanning, pattern recognition, and decision making) agility components and specifically incorporate position-specific movements in training drills to target these qualities.
The authors thank Mr Greg Capern for his technical assistance and Mr William Sarandria and Miss Carlie Hill for their involvement in data collection during the present study. They also acknowledge the involvement of the players and coaching staff.
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