A knowledge of physical and physiological load placed on athletes during competitive games is necessary to develop sport-specific training protocols. Although research is abundant in several ball games, such as soccer (21,32,33,42,43) and rugby (19,20,23), the understanding of the metabolic demands of basketball players during competition is limited. In this regard, only 3 comprehensive studies on men were accomplished (66). Of these studies, 1 examined adult male professional players (38), 1 youth male players (7), and 1 looked at senior male players (60). To analyze game activities, the authors defined different movements (e.g., walk, jog, sprint, shuffle) and calculated their frequency and duration during a game (66). These movements were separated into various gait categories to determine the amount of high-, moderate-, and low-intensity activity (38). In these studies, it was argued that in basketball doing movement executed in a relatively small space (e.g., blocking, positioning, and rebounding), the physiological demands of the game with the use of movement speed could be underestimated (38). As a result of this assumption, total distance covered by players throughout a game was not measured in either of the studies. However, knowing what is the total walking-, running-, sprinting-, shuffling- distance players cover throughout the game would presumably help basketball coaches and strength and conditioning in training prescription (65).
The changes in exercise intensity as the game progressed were also examined in basketball studies (65). In this regard, although McInnes et al. (38) found no differences in movement characteristics across quarters of play, Ben Abdelkrim et al. (8) observed a significant decrease in heart rate (HR), blood-lactate level, and time spent in intense activities in the last quarter of the game. It is unclear whether this discrepancy between the above studies is attributable to differences in the fitness level and playing time (or both) of the participating players (65). To obtain more information about the occurrence of physical fatigue during actual basketball competition, comparisons between efforts experienced by players taking part in the entire game, during different parts of the game would be necessary.
Another key issue in basketball is the extent to which the activity profile and physiological strain imposed on basketball players is related to their physical capacities. Within this context, Hoffman et al. (28) showed that basketball performance, considered as seasonal playing time, is affected by anaerobic performance such as 1 repetition maximum (1RM squat), vertical jump, and sprint performances. On the other hand, Narazaki et al. (44) noted a positive correlation between aerobic fitness and activity level of male and female players during a practice game. This suggests the potential benefit of aerobic conditioning in basketball. The inconsistency between these studies clearly illustrates that there is currently an inadequate understanding of the relationship between players' physical capabilities and game performance.
As a result of the above reasoning, the aims of the present study were (a) to examine game demands (i.e., distance covered, work-to-rest ratio, cardiac stress, and blood lactate) of elite junior players during real competition; (b) to establish whether fatigue occurs during a basketball game; and (c) to determine the relationship between players physical capacity and game performance.
Experimental Approach to the Problem
In this study, a descriptive correlation design was assumed. The study was carried out during the play-off stage of the Tunisian junior basketball championship (May-June 2004), 7-9 months after the beginning of the competitive season. These competitions included 6 teams that qualified after a preliminary round (12 teams). In this study, a total of 6 official games of the play-off tournament were studied (1 game per week). This was done to gain meaningful information related to actual basketball game demands (65). Games were played on separate Sunday but all at the same hour (i.e., 11:00 am) and under similar environmental conditions (i.e., 29-33° C). Each game consisted of 4 10-minute quarters with a 15-minute interval at half time and a 2-minute break between the quarters of each half.
Players' game activities were assessed using video time-motion analysis (8,38,41). Physiological game demands were assessed monitoring HR and blood-lactate concentration [BL] (7,8,38,50). Every player involved was examined during 1 game only, but each team was investigated during 2 different games. More details about the games context and players studied are presented in Table 1.
During the game week, the physical fitness of the considered players was assessed using field tests that were reported to be relevant to basketball (8,13,27,65,66). To track the effect of competition on basketball performance and to precisely identify the influence of the physical profile on game activity, only players who were not substituted during game play were considered during this study.
Eighteen Tunisian elite junior basketball players (age 18.2 ± 0.5 years; height 187.5 ± 5.9 cm; body mass 79.5 ± 8.4 kg, body fat 9.5 ± 4.4%), belonging to 6 teams from the first national division were studied during 6 separate junior competitive games. The sample includes 5 players at national level (1 guard, 2 forwards, and 2 centers) and 13 players at an international level (5 guards, 4 forwards, and 4 centers). They participated in the national under-19 basketball championship as regular players, and some of the international players were selected for the senior team of their respective teams. At the moment of the investigation, players had 10 ± 1.2 years of competitive experience.
Players trained 10 hours a week with their teams (5 sessions of 105-120 minutes each) with a championship game every Sunday. Training volume and intensity were standardized for all players. The overall weekly training volume included 90 minutes for strength and conditioning (power, speed, and endurance), 330 minutes for technical-tactical training, 90 minutes for a training game and 90 minutes for a championship game. Training load increased progressively from Monday to Wednesday and decreased afterward with Friday as a tapering day.
Bench press (4 sets at 4-8 RM), half-squat (4 sets at 4-8 RM), and power clean exercises (4 sets at 4-8 RM) were used to maintain or improve strength (1 session per week). These dynamic constant resistance exercises were commonly used to develop upper body, lower-body, and explosive strength, respectively (65).
Specific aerobic interval training was used to enhance players' aerobic fitness (10,30,58). The training session (1 session per week) consisted of 3 sets of 7-minute work periods, with a 5-minute active recovery (dribbling circuit at moderate intensity) between intervals (30,58). During the work period, players performed full court fast-break drills (3 vs. 2 or 4 vs. 3), with work-to-rest ratio of 1:3 (8/24 seconds).
Speed training consisted of 3 sets of 3-4 sprints, interspersed with 3 minutes of recovery. Sprints were performed on 10, 20, and 30 m with several direction changes and were interspersed by 90 seconds of passive recovery (17,66). Basketball skill training consisted of fundamental offensive and offensive drills (dribbling, passing, shooting, rebounding, screening, scrimmage, etc.).
All players were requested to refrain from strenuous exercise for at least 36 hours before the field-testing session and before the competition test. On the competition day, players came to the game gymnasium at 08:00 am (i.e., 3 hours before game start) and had the same breakfast of 400 kcal, which consisted of 200 mL of coffee with semiskimmed milk (130 kcal), a cake (170 kcal), and yoghurt (100 kcal). The energy composition of the breakfast was assessed using dedicated software (Food Processor®, version 8.3, Esha Research 2000, Salem, OR, USA). After that, subjects refrained from food for 2 hours 45 minutes before the competition start. Fifteen minutes before the beginning of the game, players performed on the court a warm-up that consisted of passing, dribbling, and shooting drills at moderate intensity. To prevent hemoconcentration, they were allowed to drink 200 mL of water before the start of the game and 200 mL during each half break. Three blood samples were taken from each subject studied: (a) just before the beginning of game (after warm-up), (b) immediately at the end of the first half, and (c) immediately at the end of the game. Written informed consent was received from all participants and parents or guardians after a detailed explanation about the aims, benefits, and risks involved with this investigation. Participants were told they were free to withdraw from the study at any time without penalty. The study was approved by the Ethics Committee of the Tunisian National Nutrition Institute and the local ethics committee before the commencement of the assessments. The study protocol followed the guidelines laid down by the World Medical Assembly Declaration of Helsinki.
During the week preceding each game, the anthropometric and athletic parameters were evaluated. The subjects' height was measured to nearest 0.1 cm with a portable stadiometer (Seca, Marsten, United Kingdom). Body mass and total body fat mass were measured by a bioelectric body composition analyzer (Tanita TBF-300 increments 0.1%, Tokyo, Japan). Body mass index was calculated as weight in kilograms divided by the square of height in meters.
The subjects also underwent measurements of muscular power (5 jumps) and strength (bench press and squat 1RM), speed (10-, 20-, and 30-m sprint), agility (T-test), and aerobic endurance (20-m shuttle-run test). Each player was instructed and verbally encouraged to give a maximal effort during all tests. A standardized warm-up, consisting of jogging and a series of increasing intensity sprints, was performed before testing. No static stretching was allowed before testing.
A quintuple horizontal jump test (5JT) was performed by each player to estimate the lower-limbs power (15). This test involved the participant attempting to cover the greatest horizontal distance possible by performing a series of 5 forward jumps with alternate left and right foot contacts (with a preliminary run 1 m in length). Participants were allowed 3 trials, with the longest distance covered recorded for analysis. It has been proposed that the 5JT is an appropriate alternative to traditional jumping exercises for estimating lower-limb explosive power in various types of athletes (15).
The participants also performed 3 maximal 30-m sprints (with 10- and 20-m split times). During recovery (2-3 minutes), the participants walked back to the starting line and then waited for the next sprint. Time was recorded using photo-cell gates (Brower Timing Systems, Salt Lake City, UT, USA, accuracy of 0.01 seconds) placed 0.4 m above the ground. The participants commenced the sprint when ready from a standing start 0.5 m behind the first timing gate. Stance for the start was consistent for all participants. The run with the fastest 30-m time was selected for analysis.
Maximal dynamic strength in half-squat and bench-press exercises was recorded as the maximal weight subjects were able to raise (1RM) as described by Chtara et al. (18) and Weiss et al. (61), respectively. In the present study, a free-weight squat-exercise was performed allowing players to bend their knees to reach half-squat position (∼90° angle in the knee joint between femur and tibia) with the barbell held over the shoulders (back-squat). The bar position for the free-weight bench-press exercise began in the up position at full elbow extension, moved to chest level for a momentary pause, and finished back at the starting position. Hand and foot positions were determined for each subject during familiarization and were held constant during all testing.
No bouncing of the bar off the chest was allowed. After the general warm-up, subjects performed a specific warm-up using 50% (10 reps), 75% (6 reps), and 85% (3 reps) of their estimated 1RM. After the specific warm-up, the subjects' resistance was fixed at a critical value of 5% below the expected 1RM and was gradually increased after each successful performance until failure. Three minutes of recovery was allowed between each attempt (63). According to the recommendations of Wisløff et al. (64) and of Chamari et al. (16), half-squat and bench-press 1RMs was expressed in kg·body·mass−0.67, to estimate the relative strength of subjects.
The T-test was administered using the protocol described by Semenick (53). Three test trials were performed, and times were recorded to the nearest one-hundredth of a second using an electronic timing system (Brower Timing Systems, Salt Lake City, UT, USA, accuracy of 0.01 seconds) placed 0.4 m above the ground. The subjects commenced the sprint when ready from a standing start 0.5 m behind the first timing gate. Reliability and validity of the T-test were reported by Pauloe et al. (46).
Maximal aerobic power was estimated with the 20-m shuttle-run test (20-m shuttle test) according to Léger and Gadoury (35) and Léger et al. (36). The endurance test was performed 15 minutes after the end of the above tests. The participants followed the 20-MST protocol by touching the appropriate 20-m line with a foot in tandem with an audio signal played through a sound system from a compact disc player. The test was terminated when the participant voluntarily dropped out because of exhaustion or because they could no longer maintain pace with the audio signals. Both lines were monitored closely by 2 testers at either end. Speed of the last successfully completed stage was recorded as the finishing maximal speed. Participants were instructed to complete as many stages as possible. Maximal oxygen uptake (O2max) was estimated from the appropriate regression equation as described by Léger and Gadoury (35). The test-retest reliability of fitness measures were reported in Table 2.
During the studied games, video-recordings were collected using a video camera (SONY, DSR-PD170P, Tokyo, Japan) positioned 10-12 m away from the sideline at halfway, at an elevation of 22-25 m to allow full coverage of the court (8,38). The present time-motion analysis was accomplished by the PC Team Sports 4.0 software, which constitutes a video-data processing solution for determining the positions of all players during the course of a team-sport event (6). The used system relies on the subtraction of the background through the use of statistic modeling, and consists of 3 important steps: initialization, movement extraction (foreground) and model updating (6). This computer software was successfully previously used in soccer (6) and in basketball (8). The video footages were analyzed frame by frame (i.e., 0.04-second accuracy) for movements' analysis.
According to McInnes et al. (38), 10 movement categories were used to classify mode and intensity of game activity: standing still (0 km·h−1), walking (≤6 km·h−1), jogging or low-speed running (from 6.1 to 12 km·h−1), running or moderate-speed running (from 12.1 to 18 km·h−1), striding or high-speed running (from 18.1 to 24 km·h−1) sprinting or maximal-speed running (>24 km·h−1), low- (less or equal to 6 km·h−1), moderate- (from 6.1 to 9 km·h−1), high-intensity shuffling movement (>9 km·h−1), and jumping. Sideways running (>12 km·h−1) was also included. Each movement category was then arranged according to relative intensity into high- (sprint, stride, sideways, high-intensity shuffle, and jump), moderate-(run and moderate-intensity shuffle), low-intensity activities (jogging and low-intensity shuffle), and recovery (walking and standing) (8). Mean and total distance traveled in each movement category and in the various intensity classes were calculated. Additionally, total time spent in different movements and cumulative times spent in the considered categories were calculated. Total time refers to all occasions that a player was on the court, including all stoppages in play excluding time outs and breaks between quarters (8,38).
In this study, work periods were defined as those when a player was jumping, running, striding, sprinting, sideways running, moderate- and high-intensity shuffling with the remaining activities classified as recovery. Although walking, jogging, and low-intensity shuffling do involve physical exertion, these activities were classified as forms of active recovery. Work-to-rest ratios were then computed and evaluated as mean (20,23). The reliability of the time-motion analysis procedures used in this study was assessed according to Ben Abdelkrim et al. (8) (Table 3).
Game HR was assessed at 5-second intervals using short-range telemetry (Polar Electro Oy, Finland) (8,12,14,38,50). The HR monitor stopwatch was synchronized with the starting time before the game. The HR data were subsequently stored on a personal computer using an interface (Polar Electro). To calculate mean HR during total time, values for time stoppage were removed (time outs, break between quarters). Relative time was thus determined for 4 intensity zones; maximal (>95% of HRmax), high (85-95% of HRmax), moderate (75-84% of HRmax), and low (<75% of HRmax) (20). Maximal HR (HRmax) was assumed as the highest achieved during game play and during the 20-m shuttle-run test for O2max estimation (8,12,14,38,50,62).
Blood samples (10 mL) were drawn from the antecubital vein before the start of the games and at the end of each half (6 games). They were placed in tubes containing EDTA (i.e., ethylenediaminetetraacetic acid) for the determination of lactate analysis. All samples were kept at 4-5°C and immediately sent to the Clinical Biology Laboratory of the Tunisian National Nutrition Institute. Lactate concentrations were then assessed by enzymatic oxidation analysis (Kit Randox LC2389, Crumlin, United Kingdom).
Data are expressed as mean ± SD. After normality inspection (Shapiro-Wilks test) of data, comparison between halves was performed using a dependent samples t test. The reliability of the athletic performances measures and the distance covered at various speeds running and the time spent in each activity category were checked using the method error technique (51). This method calculates the coefficient of variation for the differences between repeated measurements. We also calculated the intraclass correlation coefficient for the same measures.
Pearson's correlation coefficient was used to examine the relationship between the physical capacities of players and their performance during competition (game activity). According to Hopkins (29), magnitude for correlation coefficients were considered as trivial (r < 0.1), small (0.1 < r < 0.3) moderate (0.3 < r < 0.5), large (0.5 < r < 0.7), very large (0.7 < r < 0.9), and nearly perfect (r > 0.9) and perfect (r = 1). The statistical package SPSS-13.0 was used for statistical calculations. Significance was set at 0.05.
Data of athletic test performances are presented in Table 4. During the game players covered 7,558 ± 575 (6,338-8,397) m, with no significant difference between the first and second halves (3,742 ± 304 vs. 3,816 ± 299 m, p > 0.05).
The distances covered when sprinting, striding, running, jogging, and walking were 763 ± 169 (468-1,029), 406 ± 109 (192-592), 928 ± 162 (742-1,377), 1,870 ± 322 (1,140-2,640), and 1,720 ± 143 (1,460-1,960) m, respectively. Sideways running, high-, moderate-, and low-speed shuffling accounted for 218 ± 117 (48-432), 169 ± 54 (84-290), 691 ± 236 (312-1,090) and 606 ± 182 (272-1,044) m, respectively. Compared with the first half, the distance covered by sprinting, striding and sideways running decreased in the second half (411 ± 101 vs. 352 ± 97 m, p < 0.05; 222 ± 73 vs. 185 ± 45 m, p < 0.01; 126 ± 72 vs. 92 ± 53 m, p < 0.01, respectively), whereas the distance covered by jogging and walking increased (886 ± 167 vs. 984 ± 189 m, p < 0.05; 818 ± 88 vs. 901 ± 114 m, p < 0.05, respectively) (Figure 1).
The amounts of low-, moderate-, and high-intensity activity were 2,477 ± 339 (1,828-3,234) m, 1,619 ± 280 (1,256-2,074), and 1,743 ± 317 (1,176-2,196) m, respectively. The distance covered at a high intensity decreased by 16% (945 ± 195 vs. 798 ± 150 m, p < 0.001) during the second half, but distance traveled at low-intensity increased by 10% (1,180 ± 168 vs. 1,296 ± 219 m, p < 0.05).
The average distance of each locomotors activity considered in this study is noted in Figure 2.
Players were standing, walking, jogging and running for 32.30 ± 1.35% (28.95-34.0%), 30.98 ± 1.23% (28.66-33.02%), 5.58 ± 0.48% (4.69-6.38%), and 4.54 ± 0.54% (3.68-5.45%) of the total time, respectively. Low-, moderate-, and high-intensity shuffling accounted for 8.54 ± 1.20% (6.88-11.52%), 6.48 ± 0.56% (5.67-7.88%), and 3.10 ± 0.47% (2.34-4.77%) of the total time, respectively. Sprinting, striding, sideways running, and jumping accounted for 2.83 ± 0.62% (1.29-3.84%), 2.37 ± 0.46% (1.58-3.14%), 1.89 ± 0.47% (0.97-2.95%), and 1.34 ± 0.28% (0.78-1.89%) of the total time.
Compared with the first half, the time spent standing, walking, and jogging increased significantly (p < 0.05) in the second half (31.20 ± 2.24 vs. 32.61 ± 1.23%, 30.10 ± 2.61 vs. 31.85 ± 1.48%, and 5.30 ± 0.60 vs. 5.85 ± 0.75%, respectively). The amount of sprinting, striding, sideways running, and jumping was significantly reduced during the second half (3.53 ± 0.97 vs. 2.13 ± 0.46%, p < 0.001; 2.85 ± 0.80 vs. 1.89 ± 0.33%, p < 0.001; 2.23 ± 0.56 vs. 1.56 ± 0.45%, p < 0.01, and 1.60 ± 0.28 vs. 1.08 ± 0.48%, p < 0.01) (Figure 3). No differences were observed in running, high-, moderate-, and low-intensity shuffle between the 2 halves.
Total time spent in high-, moderate-, and low-intensity activities corresponded to 11.54 ± 0.97% (10.98-13.05%), 11.03 ± 0.87% (9.60-13.83%), and 14.14 ± 1.02% (12.67-16.44%), whereas recovery time represents 63.28 ± 2.10% (59.95-65.83%) (Figure 4).
Compared to the second half, a higher total time was spent in high-intensity activity during the first half (13.84 ± 1.48 vs. 10.23 ± 0.85%, p < 0.001), but a lower time was spent in recovery (61.80 ± 3.47 vs. 63.77 ± 1.98%, p < 0.01). No differences were observed in total time spent at low-and moderate-intensity (14.00 ± 0.96 vs. 14.25 ± 1.36%; 10.78 ± 1.10 vs. 11.27 ± 0.95%).
Mean work-to-rest ratio for the players was 1:3.6 ± 1:0.6 with a higher value recoded in the first half compared to the second (1:3.2 ± 1:0.6 vs. 1:4.1 ± 1:0.5, p < 0.05).
Heart rate results showed that players spent 19.3 ± 3.5 and 56.0 ± 6.3% of total time in the maximal- and high-intensity zones, whereas they spent 17.3 ± 5.5 and 7.4 ± 6.1% of total time in moderate- and low-intensity zones. The percent time spent in maximal- and high-intensity zones were greater in the first half compared to the second (21.2 ± 4.9 vs. 17.4 ± 4.1%, p < 0.05 and 57.6 ± 5.5 vs. 54.4 ± 7.6%, p < 0.01, respectively). However, the relative time spent in moderate-intensity zone increased in the second half (15.2 ± 5.3 vs. 19.3 ± 6.5%, p < 0.01) (Figure 5).
Players' mean and peak [BL] were 5.75 ± 1.25 and 6.22 ± 1.34 mmol·L−1, respectively. The [BL] recorded at the end of the first half was significantly higher than those recorded at the end of the second half (6.18 ± 1.35 vs. 5.30 ± 1.36, p < 0.01) (Figure 6).
The correlation coefficients for the interindividual relationships between the various test results and physical game performance are summarized in Table 5. A negative and significant correlation was observed between the amount of high-intensity shuffling and agility T-test performance (r = −0.68, p < 0.01) (Figure 7). The maximal- and high-speed running were positively correlated with shuttle-run test performance (r = 0.52, p < 0.05 and r = 0.49, p < 0.05, respectively) (Figures 8 and 9). A moderate relationship was observed between the sum of high-intensity activities performed during the entire game and shuttle-run test performance (r = 0.64, p < 0.01, Figure 10).
Additionally, a moderate relationship was observed between mean game % HRmax and 20-m-MST performance (r = 0.53, p < 0.05; Figure 11).
This is the first study that assessed the distance covered and the work-rest ratio during basketball competition establishing the relationship between the physical capacities of players and game performance. The main finding of this study was that the total distance covered during a basketball game was 7,558 ± 575 m, of which 23% (1,743 ± 317 m) was performed with high-intensity activities. Interestingly during the second half, sprinting and striding were reduced and the performance of sideways running was impaired.
The average total distance covered during the observed junior basketball games (7,558 ± 575 m) was lower than that reported in other team sports such as soccer and rugby that examined players in play for the whole game duration (22,57). To take into account difference in game duration and players permanence in play, Barbero et al. (5) reported players' work rate in term of distance covered per time unit (i.e., per minute of play). Those authors reported a distance coverage of 117.3 ± 11.6 m·min−1 in professional Futsal players during championship games (5). This is similar to the work rate found in these basketball players that was of 114.5 ± 8.7 m·min−1. This figure is quite different than that estimated from soccer matches that is the range of 123-135 m·min−1 (47,48,57). The lower work rate reported in basketball players (i.e., distance covered per minute of play) may be related to the lower speed that are attained during actual game because of the smaller playing area used (i.e., 15 × 28 vs. 60 × 100 m for basketball and soccer, respectively).
Interestingly, during a competitive game, basketball players covered using sideward movements as much as 1,684 m or the 22% of the total distance covered. This figure is in the range (120-2,130 m) of that reported in top level assistant soccer referees during highly competitive matches that are reported to cover 1,160 m moving sideways (32). This finding is of specific interest to basketball coaches and fitness trainers for training prescription because this nonorthodox directional mode of running has been reported to be metabolically demanding (49,65). The relevance of sideways movement would suggest the use of field tests that enable the assessment of the ability to move quickly with this directional mode. The T-test proposed by Semenik (53) for agility assessment in basketball do involve a significant part of field test distance to be covered at maximal speed shuffling. Interestingly, in this study, a significant and very large relationship was found between T-test performance and the distance covered at high-intensity shuffling during the game. This result provide evidence for direct validity of the T-test in basketball suggesting its use for the assessment of basketball players (9,13). However, the validity of the T-test should not be overemphasized, because performance in this test was able to explain only the 46% of the variability in high-intensity shuffling coverage during the game.
Examining Australian professional players, McInnes et al. (38) reported that 38% of total play time was made up of phases of walking, with jogging, running, sprinting-striding, and shuffling accounting for the 8, 7, 4, and 17% of game time. For a series of English national and international games, Miller and Bartlett (41) reported that the low-intensity movements (standing and walking) accounted for between 67% (forwards) and 72% (centers) of total on-court activity, whereas the amount of time running and side shuffling represents 16 and 9%, respectively. According Tessitore et al. (60) an older basketball player spent 48% walking, 19% positioning, 17% running, 15% inactive, and 1% jumping. Recently, Narazaki et al. (44) noted that the male and female players at collegiate level spent 34.1% of play time running and jumping, 56.8% walking, and 9.0% standing. This findings reveals that competitive basketball like other team sports do involve important phase of the game that are performed at low-intensity activities (5,20,22,48,57). This supports the assumption that a large contribution of low-intensity activities to player game time is a common trait of team sports (3,37,39,40).
These findings also demonstrate that total distance covered during a game would really underestimate the physiological demands of game play and probably led to erroneous conclusions about the overall demands of this sport (38). The considerable distance traveled shuffling must be considered as a better indicator of the demanding periods of the game, because it has been noted that shuffling contribute a considerable portion of the energy demands of basketball competition (38). The distance covered at high-speed running (4,31), and sideways running that has been shown to be more demanding of energy than forward running (49) should also be constituted an important marker of the physical requirements of basketball. The proportion of direction changes can also be taken into account, because this provides an indication of the work required for accelerations and decelerations during a game (38). Therefore, the number of change of game activities and distances covered striding and sprinting and in sideways movements (i.e., running and shuffling) indicate that the physical stress on basketball player is high during several parts of a game.
During the competitive games studied, players spent 77% of the time in low-intensity activities. This finding is similar to those previously reported by other authors that addressed basketball games of different competitive levels (65). However, despite high to moderate intensities that accounted for only the 23% of the game time exercise, HR resulted in the high to maximal zone for the 80% of competition time. Specifically in the present study, we found that HR was above 95 and 85% of HRmax for more than 19 and 74% of game play, respectively. Although cardiac stress can be affected by other factors such as isometric contractions, thermal and emotional stress (3), these values are comparable to those previously reported in men's and women's basketball competitions (65). However, with the research design used, it is not possible to provide evidence as per the causes of these results (2,3).
In this study, small between-player variations were observed in the total distance covered (6,338-8,397 m, SD = 7.6% of mean), whereas large variations were found in the distance covered by maximal- and high-speed running (468-1,029 m, SD = 22% and 192-592 m, SD = 27%, respectively) and sideways running (48-432 m, SD = 56%). These findings support the notion that it is the amount of high-intensity activity, rather than total distance, the main factors differentiating between good and poor physical performances. It could be speculated that the small variation in total distance would be attributed to the ongoing attack and defense transfer that game rules impose on players (i.e., attack and defense shift). However, factors such as training status, differences in tactics, the style of play, the importance of the games, and the quality of the opponent may explain the large variation in high-intensity movements observed in this study.
The mean distance of single sprint (16.8 ± 3.1 m), stride (16.9 ± 3.6 m), high-intensity shuffling (4.3 ± 0.7 m), and sideways running (10.7 ± 2.1 m) recorded in the present study suggest that most of the energy required to perform high-intensity exercise is derived from the ATP-CP system (1). Although a mean work-to-rest ratio was 1:3.6 evident (i.e., 6 seconds of high- to moderate-intensity activity followed by approximately 22 seconds of rest or low intensity activity), approximately one-fifth (∼19%) of the work periods completed was followed by breaks of an equal or shorter duration. This would suggest an insufficient time for total replenishment of creatine-phosphates stores and thus a considerable reliance on anaerobic glycolysis during subsequent work periods (2). This is partly supported by mean and peak [BL] of 5.75 and 6.22 mmol·L−1, respectively, with individual values exceeding 10 mmol·L−1. These values are similar to those recorded for professional players (38) and demonstrate that the anaerobic mechanisms are highly stimulated, in particular toward the end of the halves. Indeed, it was reported that [BL] obtained during competitive games represent the activity players performed 5 minutes before blood sampling (4,8,38). Collectively, these findings demonstrate the need for specific training of the anaerobic alactic (ATP-CP) and anaerobic glycolytic systems in basketball players (11-13).
Basketball like other team sports is considered as a multisprint sports (11). However, the average distance of the single sprint accomplished in the present (16.8 ± 3.1 m, range 12-21 m) and previous studies that reported that 66% of sprint and strides are shorter than 2 seconds suggest that acceleration capabilities are of primary importance to basketball players (13,38). This may be supported by the number of high-intensity shuffling bouts (∼4 m) a basketball player is required to perform during the game (i.e., 169 ± 54 m). This may suggest that the ability to perform intense short-duration shuffles repetitively during a game is an integral fitness component of basketball (65). In this regard, McInnes et al. (38) have mentioned that improving the ability to maintain high-intensity shuffling movements throughout the game may be an important aspect of preparing athletes for competition.
When comparing the junior basketball players' activity profiles in different parts of the game, total distance covered during the first and second halves was comparable. However, significantly more sprinting (411 vs. 352 m), striding (222 vs. 185 m), and sideways running (126 vs. 92 m) were performed in the first half than in the second half (p < 0.05). This is partly supported by higher [BL] levels at half time and by greater permanence in the >95% HRmax zone during the first compared to the second half (i.e., 21.2 vs. 17.5%). The finding of a reduction in high-intensity activity performance throughout the game is consistent with several studies of male and female basketball players (8,50). In this regard, previous researches (7,8,26) have speculated that player fatigue may be responsible for the reduction in high-intensity running performances that occur during the course of a game. However, these results should be interpreted with caution as the physiological demands of basketball competition can be influenced by tactical factors (8). Indeed, although the present results reveal a reduction in overall distance covered at high intensity, the fraction performed at high-intensity shuffling did not change significantly during the course of the game. If fatigue alone was responsible for the reduction in both high-speed and sideways running performance, then it would also be reasonable to expect a reduction in shuffling performance during the game. Thus, a possible lower pace of the game in the last phase of the second half, may explain such decrement. In this regard, Ben Abdelkrim et al. (8) observed that during the last minutes of a game, teams are likely to manage a further control of ball possession, and therefore, the proportion of straight play and fast breaks decreases, causing the whole pace to slow down.
To examine the effects of physical fitness on game performance, we carried out several physiological tests and related the results to the game activities. From these comparisons, we found that there were large correlations between 20-MST performance and the distance covered at maximal-speed running (r = 0.52, p < 0.05), and high-intensity activities during a game (r = 0.64, p < 0.01). However, a moderate association was found with high-speed running (r = 0.49, p < 0.05). These findings suggest that this test is a good predictor of elite male basketball players' ability to perform sprinting, striding, and intense movements throughout competitive games. The logical explanation of this observation is that the movement patterns of this test correspond to the multiple changes of direction and in exercise intensity experienced by a player during a game. Despite the reported significance, the common variance explained in high-intensity activities by 20-MST was only 24-41%. It could be speculated that a higher direct validity could have been disclosed if an intermittent high-intensity endurance (i.e., Yo-Yo intermittent recovery test) test was used (13). These findings provide preliminary (i.e., descriptive evidence) that aerobic performance may play a role in basketball actual game play activities.
This is in line with the investigation of Narazaki et al. (44) who observed that players' O2max was correlated to game O2 (r = 0.67, p < 0.05) and to percent of time spent running and jumping (r = 0.93-0.96, p < 0.01). Therefore, these results support the potential benefit of aerobic conditioning in basketball (7,65). A possible reason for the association between high-intensity activities and aerobic performance may found in the reported support of the aerobic pathway in the ability to reiterate sprints and high-intensity accelerations with short recovery intervals (55). However, these findings were in disagreement with those suggesting that the aerobic capacities of elite basketball players are not as important as their anaerobic or physical characteristics, and that a O2max in the range of 50-55 ml·kg−1·min−1 may be considered as adequate for elite basketball players (34,45,59). To spread light on this interesting issue structured training studies should be undertaken (30).
Although sprinting performance, strength, and muscular power are thought to be important for successful participation in basketball (28,45,52,54,56,66), no significant association was reported between game activities and anaerobic performance in this study. This may mean that although the components of anaerobic performance (63,65,66) are crucial during high-intensity activities common to basketball, such as quick change of direction, sprinting-accelerations and jumping, they did not preserve the success of these intense movements repetitively throughout the different parts of the game.
Because of the physical, physiological, and game-demand similarities of this study's players with those reported for international-level basketball players, the results obtained in this study may be regarded as being representative of elite-level basketball (17,65,66).
This descriptive study showed that game performance is affected by players' individual physical fitness. Specifically, our results provided evidence that agility assumed as T-test performance was associated with the distance traveled in high-intensity shuffling. This further supports a previous study that showed that agility was a consistent predictor of playing time in National Collegiate Athletic Association division I male basketball players (27). Furthermore aerobic performance (i.e., 20-MST) was associated with high-intensity performance during the game. In light of this study findings, basketball coaches and strength and conditioning professionals should consider in their basketball-specific testing batteries agility and aerobic fitness to test and to check players' preparedness to cope with game demands, together with strength and explosive-power ability performance assessment (24,25,65,66).
The authors would like to thank the staff of the Department of Study and Planning of the National Institute of Nutrition and the athletes and the technical staff of the basketball teams considered in this study.
1. Balsom, PD, Seger, JY, Sjodin, B, and Ekblom, B. Maximal-intensity intermittent exercise: Effect of recovery duration. Int J Sports Med
13: 528-533, 1992.
2. Balsom, PD, Seger, JY, Sjodin, B, and Ekblom, B. Physiological responses to maximal intensity intermittent exercise. Eur J Appl Physiol
65: 144-149, 1992.
3. Bangsbo, J. The physiology of soccer-With special reference to intense intermittent exercise. Acta Physiol Scand
151(Suppl 619): 1-155, 1994.
4. Bangsbo, J, Norregaard, L, and Thorso, F. Activity profile of competition soccer. Can J Sport Sci
16: 110-116, 1991.
5. Barbero Alvarez, J, Soto Hermoso, V, and Granda Vera, J. Effort profiling during indoor soccer competition. J Sports Sci
22: 500-501, 2004.
6. Battikh, T, Jabri, I, and Annabi, M. Automatic detection of player positions and trajectories during a soccer match for the measurement of physical and tactical performance. Can J Elec Comp Eng
32: 113-119, 2007.
7. Ben Abdelkrim, N, Castagna, C, El Fazaa, S, Tabka, Z, and El Ati, J. Blood metabolites during basketball competitions. J Strength Cond Res
23: 765-773, 2009.
8. Ben Abdelkrim, N, El Fazaa, S, and El Ati, J. Time-motion analysis
and physiological data of elite under-19 basketball players during competition. Br J Sports Med
41: 69-75, 2007.
9. Boddington, MK, Lambert, MI, and Waldeck, MR. Validity of a 5-meter multiple shuttle run test for assessing fitness of women field hockey players. J Strength Cond Res
18: 97-100, 2004.
10. Bravo, DF, Impellizzeri, FM, Rampinini, E, Castagna, C, Bishop, D, and Wisløff, U. Sprint vs. interval training in football. Int J Sports Med
29: 668-674, 2008.
11. Castagna, C, Abt, G, Manzi, V, Annino, G, Padua, E, and D'Ottavio, S. Effect of recovery mode on repeated sprint ability in young basketball players. J Strength Cond Res
22: 923-929, 2008.
12. Castagna, C, D'Ottavio, S, Manzi, V, Annino, G, Colli, R, Belardinelli, R, and Lacalaprice, F. HR and o2
responses during basketball drills. In: Book of Abstract of the 10th Annual Congress of European College of Sport Science
. Dikic, N, Zinanic, S, Astojic, S, and Tornjanski, Z, eds. Belgrade, Serbia, 2005. pp. 160.
13. Castagna, C, Impellizzeri FM, Rampinini, E, D'Ottavio, S, and Manzi, V. The Yo-Yo intermittent recovery test in basketball players J Sci Med Sport
11: 202-208, 2008.
14. Castagna, C, Manzi, V, Marini, M, Annino, G, Padua, E, and D'Ottavio, S. Effect of playing basketball in young basketball players. In: 11th annual Congress of the European College of Sport Science 05-08 July
. Lausanne, Switzerland. Hoppeler, H, Reilly, T, Tsolakidis, E, Gfeller, L, and Klossner, S, eds. 2006. pp. 325.
15. Chamari, K, Chaouachi, A, Hambli, M, Kaouech, F, Wisløff, U, and Castagna, C. The 5-Jumps for distance as a field test to assess lower limbs explosive-power in soccer players J Strength Cond Res
22: 944-950, 2008.
16. Chamari, K, Hachana, Y, Ahmed, YB, Galy, O, Sghaier, F, Chatard, JC, Hue, O, and Wisløff, U. Field and laboratory testing in young elite soccer players. Br J Sports Med
38: 191-196, 2004.
17. Chaouachi, A, Brughelli, M, Chamari, K, Levin, GT, Ben Abdelkrim, N, Laurencelle, L, and Castagna, C. Lower limb maximal dynamic strength and agility determinants in elite basketball players. J Strength Cond Res
23: 1570-1577, 2009.
18. Chtara, M, Chaouachi, A, Levin, GT, Chaouachi, M, Chamari, K, Feki, Y, Amri, M, and Laursen, PB. Effect of concurrent endurance and circuit resistance-training sequence on muscular strength and power development. J Strength Cond Res
22: 1037-1045, 2008.
19. Coutts, A, Reaburn, P, and Abt, G. Heart rate, blood lactate concentration and estimated energy expenditure in a semi-professional rugby league team during a match: A case study. J Sports Sci
21: 97-103, 2003.
20. Deutsch, MU, Maw, GJ, Jenkins, D, and Reaburn, P. Heart rate, blood lactate and kinematic data of elite colts (under-19) rugby union players during competition. J Sports Sci
16: 561-570, 1998.
21. Di Salvo, V, Baron, R, Tschan, H, Calderon Montero, FJ, Bachl, N, and Pigozzi, F. Performance characteristics according to playing position in elite soccer. Int J Sports Med
28: 222-227, 2007.
22. Docherty, D, Wenger, HA, and Neary, P. Time motion analysis related to the physiological demands of rugby. J Hum Mov Stud
14: 269-277, 1988.
23. Doutreloux, JP, Tepe, P, Demont, M, Passelergue, P, and Artigot, A. Exigences énergétiques estimées selon les postes de jeu en rugby. Sci Sports
17: 189-197, 2002.
24. Drinkwater, EJ, Hopkins, WG, McKenna, MJ, Hunt, PH, and Pyne, DB. Modelling age and secular differences in fitness between basketball players. J Sports Sci
25: 869-878, 2007.
25. Drinkwater, EJ, Pyne, DB, and McKenna, MJ. Design and interpretation of anthropometric and fitness testing of basketball players. Sports Med
38: 565-578, 2008.
26. Glaister, M. Multiple sprint work: Physiological responses, mechanisms of fatigue
and the influence of aerobic fitness. Sports Med
35: 757-777, 2005.
27. Hoare, DG. Predicting success in junior elite basketball players the contribution of anthropometic and physiological attributes. J Sci Med Sport
3: 391-405, 2000.
28. Hoffman, JR, Tenenbaum, G, Maresh, CM, and Kreamer, WJ. Relationship between athletic performance tests and playing time in elite college basketball players. J Strength Cond Res
10: 67-71, 1996.
30. Impellizzeri, FM, Marcora, SM, Castagna, C, Reilly, T, Sassi, A, Iaia, FM, and Rampinini, E. Physiological and performance effects of generic versus specific aerobic training in soccer players. Int J Sports Med
27: 483-492, 2006.
31. Krustrup, P and Bangsbo, J. Physiological demands of top-class soccer refereeing in relation to physical capacity: Effect of intense intermittent exercise training. J Sports Sci
19: 881-891, 2001.
32. Krustrup, P, Mohr, M, and Bangsbo, J. Activity profile and physiological demands of top-class soccer assistant refereeing in relation to training status. J Sports Sci
20: 861-871, 2002.
33. Krustrup, P, Mohr, M, Ellingsgaard, H, and Bangsbo, J. Physical demands during an elite female soccer game: Importance of training status. Med Sci Sports Exerc
37: 1242-1248, 2005.
34. Latin, RW, Berg, K, and Baechle, T. Physical and performance characteristics of NCAA Division I Male basketball players. J Strength Cond Res
8: 214-218, 1994.
35. Léger, L and Gadoury, C. Validity of the 20 m shuttle run test with 1 min stages to predict o2
max in adults. Can J Sport Sci
14: 21-26, 1989.
36. Léger, LA, Mercier, D, Gadoury, C, and Lambert, J. The multistage 20 metre shuttle run test for aerobic fitness. J Sports Sci
6: 93-101, 1988.
37. Lothian, F and Farrally, M. A time-motion analysis
of women's hockey. J Hum Mov Stud
26: 255-265, 1994.
38. McInnes, SE, Carlson, JS, Jones, CJ, and McKenna, MJ. The physiological load imposed upon basketball players during competition. J. Sports Sci
13: 387-397, 1995.
39. McKenna, MJ, Patrick, JD, Sandstrom, ER, and Chennells, MHD. Computer-video analysis of activity patterns in australian rules football. In: Science and Football
. Reilly, T, Lees, A, Davids, K, and Murphy, WJ, eds. London: E & F.N. Spon, 1988.
40. Meir, R, Arthur, D, and Forrest, M. Time and motion analysis of professional rugby league: A case study. Strength Cond Coach
1: 24-29, 1993.
41. Miller, SA and Barlett, RM. Notational analysis of the physical demands of basketball. J Sports Sci
12: 181, 1994.
42. Mohr, M, Krustrup, P, Andersson, H, Kirkendal, D, and Bangsbo, J. Match activities of elite women soccer players at different performance levels. J Strength Cond Res
22: 341-349, 2008.
43. Mohr, M, Krustrup, P, and Bangsbo, J. Match performance of high-standard soccer players with special reference to development of fatigue
. J Sports Sci
21: 519-528, 2003.
44. Narazaki, K, Berg, K, Stergiou, N, and Chen, B. Physiological demands of competitive basketball. Scand J Med Sci Sports
19: 425-432, 2009.
45. Ostojic, SM, Mazic, S, and Dikic, N. Profiling in basketball: Physical and physiological characteristics of elite players. J Strength Cond Res
20: 740-744, 2006.
46. Pauloe, K, Madole, K, Garhammer, J, Lacourse, M, and Rozenek, R. Reliability and validity of the t-test as a measure of agility, leg power, and leg speed in college-aged men and women. J Strength Cond Res
14: 443-450, 2000.
47. Rampinini, E, Coutts, AJ, Castagna, C, Sassi, R, and Impellizzeri, FM. Variation in top level soccer match performance. Int J Sports Med
28: 1018-1024, 2007.
48. Rampinini, E, Impellizzeri, FM, Castagna, C, Coutts, AJ, and Wisløff, U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue
and competitive level. J Sci Med Sport
12: 227-233, 2009.
49. Reilly, T and Bowen, T. Exertional cost of changes in directional modes of running. Percep Mot Skills
58: 49-50, 1984.
50. Rodriguez-Alonso, M, Fernandez-Garcia, B, Perez-Landaluce, J, and Terrados, N. Blood lactate and heart rate during national and international women's basketball. J Sports Med Phys Fitness
43: 432-436, 2003.
51. Sale, DG. Testing strength and power. In: Physiological Testing of the High-Performance Athlete. MacDougall, JD, Wenger, HA, and Green, HJ, eds. Champaign, IL: Human Kinetics Books, 1991. pp. 21-106.
52. Sallet, P, Perrier, D, Ferret, JM, Vitelli, V, and Baverel, G. Physiological differences in professional basketball players as a function of playing position and level of play. J Sports Med Phys Fitness
45: 291-294, 2005.
53. Semenick, D. The T-test. NSCA J
12: 36-37, 1990.
54. Simenz, CJ, Dugan, CA, and Ebben, WP. Strength and conditioning practices of National Basketball Association strength and conditioning coaches. J Strength Cond Res
19: 495-504, 2005.
55. Spencer, M, Bishop, D, Dawson, B, and Goodman, C. Physiological and metabolic responses of repeated-sprint activities specific to field-based team sports
. Sports Med
35: 1025-1044, 2005.
56. Stapff, A. Protocols for the Physiological Assessment of Basketball Players. In: Physiological Tests for Elite Athletes
. Gore, CJ, ed. Champaign. IL: Human Kinetics Publishers, 2000. pp. 1-27.
57. Stølen, T, Chamari, K, Castagna, C, and Wisløff, U. Physiology of soccer: An update. Sports Med
35: 501-536, 2005.
58. Stone, NM and Kilding, AE. Aerobic conditioning for team sport athletes. Sports Med
39: 615-642, 2009.
59. Tavino, LP, Bowers, CJ, and Archer, CB. Effects of basketball on aerobic capacity, anaerobic capacity, and body composition of male college players. J Strength Cond Res
9: 75-77, 1995.
60. Tessitore, A, Tiberi, M, Cortis, C, Rapisarda, E, Meeusen, R, and Capranica, L. Aerobic-anaerobic profiles, heart rate and match analysis in old basketball players. Gerontology
52: 214-222, 2006.
61. Weiss, LW, Wood, LE, Fry, AC, Kreider, RB, Relyea, GE, Bullen, DB, and Grindstaff, PD. Strength/power augmentation subsequent to short-term training abstinence. J Strength Cond Res
18: 765-770, 2004.
62. Whyte, GP, George, K, Shave, R, Middleton, N, and Nevill, AM. Training induced changes in maximum heart rate. Int J Sports Med
29: 129-133, 2008.
63. Wisløff, U, Castagna, C, Helgerud, J, Jones, R, and Hoff, J. Maximal squat strength is strongly correlated to sprint-performance and vertical jump height in elite soccer players. Br J Sports Med
38: 285-288, 2004.
64. Wisløff, U, Helgerud, J, and Hoff, J. Strength and endurance of elite soccer players. Med Sci Sports Exerc
30: 462-467, 1998.
65. Ziv, G and Lidor, R. Physical attributes, physiological characteristics, on-court performances and nutritional strategies of female and male basketball players. Sports Med
39: 547-568, 2009.
66. Ziv, G and Lidor, R. Vertical jump in female and male basketball players-A review of observational and experimental studies. J Sci Med Sport
[Epub Ahead of Print doi:10.1016/j.jsams.2009.02.009].
Keywords:© 2010 National Strength and Conditioning Association
field tests; team sports; game analysis; physiological loading; fatigue; time-motion analysis