Agility is a performance quality that directly contributes to success in sports in which athletes have to rapidly change direction and speed (7,32,33). This ability is recognized as one of the most important conditioning capacities in basketball (3,27,38). In general, agility performance comprises: (a) the ability to change direction rapidly with advanced knowledge of the directional change (i.e., preplanned agility, nonreactive agility, closed-skill agility, change-of-direction speed) and (b) the ability to rapidly change direction while responding to an unpredictable visual or audio stimulus (i.e., nonplanned agility, reactive agility, open-skill agility) (14,28,29).
In team sports (i.e., football, handball, soccer, basketball), preplanned agility (i.e., closed-skill agility, change-of-direction speed—CODS) allows an athlete to outperform his or her opponent in situations in which he or she is in a position to define the movement pattern (12,31). Nonplanned agility (i.e., open-skill agility, reactive agility) is accentuated in all situations in which players perform a change in direction while reacting to an external stimulus (e.g., the trajectory of the ball, an opponent's change in direction) (32,37). However, reactive agility and CODS are generally considered independent qualities. In short, studies to date have shown relatively low correlations between tests of these 2 capacities, with common variances ranging from <5 to 20% (5,24,29). Consequently, there is a clear consensus on the need for an independent evaluation and conditioning of these 2 abilities (24,32).
Position-specific tasks and position-specific body builds in basketball are well known (i.e., Centers are the tallest and heaviest, Guards are the shortest and most mesomorphic) (3,15,20). The differences in specific fitness between playing positions have also been studied (1,30). It is regularly reported that Guards possess advanced sprinting capacities and aerobic capacity (2,16). However, results of studies that have investigated agility performances among positions are inconsistent (3,16,27). In some cases, authors have reported better preplanned agility performance in Guards than in Forwards and Centers (3,16). In contrast, another study reported the opposite results, with superior preplanned agility performance observed for frontcourt players (Forwards and Centers) compared with backcourt players (Guards) (27). Finally, a recent study showed no significant positional differences in preplanned agility, as evaluated by 3 different tests (31).
Although agility is hypothesized to be directly related to performance quality in basketball, only few studies reported differences between playing levels for this conditioning capacity (2,16,28). When compared first and second division players from Turkish league, Koklu et al. (16) found no significant differences for preplanned agility measured by test of preplanned agility (change-of-direction speed) over T course (T-TEST). Recently, Scanlan et al. reported better performance of starter players in reactive agility (nonplanned agility), with no significant difference between observed playing levels in preplanned agility (CODS) (15,27). Finally, Ben Abdelkrim et al. (2) compared 3 Tunisan national teams and reported results for T-TEST preplanned agility of 10.53 ± 0.67 seconds (U18 team), 10.05 ± 0.44 seconds (U20 team), and 9.99 ± 0.40 seconds (Senior team), with significant differences between U18 and other 2 teams.
It is clear from this brief literature overview that a limited number of investigations have reported differences in agility performances between playing positions in basketball, and their results are inconsistent. In addition, to the best of our knowledge, only 1 study has investigated nonplanned agility as a position-specific determinant of fitness status in basketball in examining only a few participants (altogether, 12 athletes divided into 2 positions) (27). What is particularly important is that studies investigating differences in agility between playing levels have examined this problem for all athletes without dividing them according to their position in the game (2,16,28). Finally, all studies reported to date have used general and not sport-specific tests of agility performance, and we may therefore suppose that the present results are of limited ecological validity (21,32,35).
The main aims of this study were to determine the reliability and discriminative validity of different agility tests in defining position-specific agility in basketball. In addition, we examined the applicability of agility tests to identify differences among basketball athletes at 2 competitive levels (i.e., top level and high level). A working hypothesis assumed the existence of differences in agility performance related to the playing position and competitive level of basketball players.
Experimental Approach to the Problem
Due to the highly specific movement techniques that occur in basketball, we believe that the agility tests used to date have limited applicability in this sport. Therefore, the main rationale for this study arose from the low ecological validity of tests previously used for evaluating real-game preplanned and nonplanned agility performances in basketball. Our study intended to determine whether newly developed agility tests would be valid for distinguishing between (a) basketball playing positions and (b) the performance levels of basketball athletes.
This cross-sectional, field-based study consisted of 4 phases. In the first one, we consulted 5 top-level experts (3 coaches and 2 players) regarding the agility movement patterns that are relatively common across all playing positions. In addition, they were instructed to determine the technique that would be applicable for testing the agility performance of all athletes, regardless of their primary playing duties in basketball. These experts agreed that the basketball defensive technique usually called “help-and-recover” would be highly applicable for this purpose. In general, this defensive skill consists of a quick forward movement of 1–2 m, followed by a diagonal (i.e., semilateral) shuffle of approximately 2–3 m (left or right, depending on the offensive player's change in direction), and a quick return to the initial position (Figure 1). This technique is familiar to basketball players because all of them do it often during game play. In the second phase of the experiment, the dimensions of the test and type of execution were standardized (see the Procedures section for more test details). In the third phase, we made an a priori estimate of the sample size. To obtain the sample size estimate, we used data obtained in a pilot test of 20 athletes (10 first division and 10 second division players). An analysis using the G*Power software (version 22.214.171.124; Heinrich Heine University Düsseldorf, Düsseldorf, Germany) for an independent 2-way analysis of variance (ANOVA) (performance level × playing position; p-value of 0.05, power of 0.90, and effect size (ES) of 0.5) recommended 58 participants as an appropriate sample size. The fourth phase involved testing of all participants.
We tested 110 high-level male basketball athletes from Bosnia and Herzegovina (height: 194.92 ± 8.09 cm; body mass: 89.33 ± 10.91 kg; body fat: 8.98 ± 3.41%; age: 21.58 ± 3.92 years). All athletes were performing at the highest national (professional/semiprofessional) rank at the moment of testing (beginning of the 2014–15 competitive season). Among the total sample, 58 participants were competing in the first division and 52 in the second division. Testing was performed at the beginning of the season, and all participants had completed a preseason preparation period of at least 1 month before the testing was conducted. Only participants who had no injuries and/or illnesses for 30 days before the experiment were included in this investigation. The players were categorized as Guards (n = 49), Forwards (n = 22), or Centers (n = 39). Playing positions were self-reported by the athletes and additionally checked by the team manager (coach).
The ethics board of the first author's institution provided approval of the research experiment (Ethical Board Approval No: 2181-205-02-05-14-001). All participants were older than 18 years and were informed of the purpose, benefits, and risks of the investigation. Participants voluntarily took part in the testing after they provided written consent. All players had been playing basketball for at least 7 years. In addition to the standard technical and tactical practice sessions (1–4 hours per day; 5 days a week plus competitive games) and competitions, the players were involved in strength and conditioning programs (2–3 times per week) that included resistance exercises and aerobic or anaerobic endurance training. The average training frequency of all players ranged from 4 to 10 training sessions per week, with an average of 5–6 sessions weekly, depending on the level of competition.
The variables included participants' playing position (Guard, Forward, Center), performance level (i.e., first division vs. second division players), anthropometrics (body height, body mass, and body fat percentage), and 5 agility performances. Anthropometrics were measured with Seca measuring equipment (Seca, Birmingham, United Kingdom) and skinfold caliper (Holtain, London, United Kingdom), with testing procedures explained in detail previously (15). The agility tests were tested under similar conditions for all participants (standard basketball court, wooden floor, temperature 20–25° C, 9–11 am, and self-preferred type of footwear) in a single day. After anthropometric measurements, athletes participated in a standardized warm-up protocol consisting of jogging (10 minutes), body weight exercises (5 minutes), various change-of-direction exercises (2–3 minutes), and dynamic stretching (5 minutes). Before each of the agility tests, participants performed 3 familiarization trials to familiarize themselves with the test task, type of execution, and test distances. The rest between familiarization trials and testing was 3–4 minutes. The agility tests were conducted in random order, with 4–5 minutes of rest between different tests and 3–4 minutes between trials for each test. The time was measured in intervals of 0.01 second.
The agility performances observed in this study were a T-TEST, a basketball-specific nonplanned agility test performed on the dominant (BBAGILdom) and nondominant sides (BBAGILnond), and a preplanned (i.e., change-of-direction speed—CODS) basketball-specific agility test performed on the dominant (BBCODSdom) and nondominant sides (BBCODSnond). Measurements were performed by a hardware device system based on an ATMEL microcontroller (model AT89C51RE2; ATMEL Corp, San Jose, CA, USA) as the core of the system. A photoelectric infrared sensor (E18-D80NK) was used as an external time triggering input, and LEDs were used as controlled outputs. The photoelectric infrared sensor has been shown to be as reliable as high-speed sensors, with a response time of less than 2 milliseconds (>500 Hz) and a digital output signal. The sensor's detection distance ranged from 3 to 80 cm and was capable of detecting transparent or opaque objects. Because it has a digital output (high-low state) with an NPN transistor open collector, the sensor is connected through a microcontroller IO port. For the purposes of our study, this device was connected to a laptop PC operating on the Linux OS.
For the T-TEST, a course was arranged in a T-shape, with 1 cone placed 9.14 m from the start line and 2 additional cones placed 4.57 m on either side of the first cone. The participants were asked to sprint forward 9.14 m from the start line to the first cone and touch the top of it with their right hand, shuffle 4.57 m left to the second cone and touch its top with their left hand, shuffle 9.14 m to the right to the third cone and touch its top with their right hand, and shuffle 4.57 m back left to the middle cone and touch its top with their left hand before finally backpedaling to the start line. The timing began on a sound signal and stopped when the participant had passed through the timing gate on their return. The best performance was retained as the final result for each athlete.
The basketball-specific tests of nonplanned and preplanned agilities were all performed in the testing area shown in Figure 1. For the BBAGILdom and BBAGILnond, the participants commenced from the start line such that when crossing the infrared (IR) signal the timing began. At that particular moment, a hardware module (microcontroller—MC) lit one of the 2 LEDs placed inside 30-cm-high cones (labeled A and B). A participant had to assess which cone was lit, shuffle run to that particular cone, rebound the ball placed at the top of the cone, and return to the start line as quickly as possible. When a participant crossed the IR signal on their way back, the timing stopped. To mimic real-game performance in basketball, the participants faced frontwards throughout the entire test. A single-test trial for each test consisted of 5 attempts, and participants had no advanced knowledge of the testing scenario. Three trials were performed. The rest period between attempts lasted 10–15 seconds. The testing of BBCODSdom and BBCODSnond was similar to the testing of the BBAGIL performances, but a participant had advanced knowledge of which cone would light up. The dominant side for BBCODS and BBAGIL was established for each participant independently by comparing the average value for all attempts executed on the right side with the average for attempts executed on the left side. Specifically, if “average right” was numerically lower than “average left,” the right side was regarded as the dominant side (and vice versa). The best achievement on the right side and best achievement on the left side were retained as final results for each participant.
After assessing the normality (by the Kolmogorov-Smirnov test), the mean values and standard deviations were reported for all variables. The intrasession reliability was calculated on a basis of results of all athletes (n = 110). Additionally, a subsample consisting of 24 athletes was tested by testing and retesting (2 days in a row) to establish the intersession reliability of the agility tests. The relative reliability was analyzed using the intraclass correlation coefficient (ICC), and the absolute reliability was analyzed using the coefficient of variation (CV). To calculate ICC and CV for T-TEST performance, the results of all testing trials were used. For the BBCODS and BBAGIL, the best attempts of dominant and nondominant side in each trial were used to calculate reliability parameters. The calculations were performed using the freely available Microsoft Excel 2010 software program (8,10). The homoscedasticity of all variables was proven by Levene's test.
The relationships between the applied variables were established by Pearson's correlation coefficients (23,36). The discriminative validity of the applied tests was evaluated with regard to (a) playing position differences and (b) performance level differences. For anthropometric and agility variables, a 2-way univariate ANOVA (performance level × playing position) was calculated, and differences between 3 playing positions were further evaluated by a Scheffe post hoc test when appropriate. To define the differences between performance levels (first division vs. second division players), within each playing position, Student's t-tests for independent samples were calculated and further analyzed using a magnitude-based Cohen's ES statistic with modified qualitative descriptors. The ES was assessed using the following criteria: <0.02 = trivial; 0.2–0.6 = small; >0.6–1.2 = moderate; >1.2–2.0 = large; and >2.0 very large differences (9).
A level of statistical significance of 95% (p ≤ 0.05) was applied. Statsoft's Statistica ver. 12.0 (StatSoft, Inc, Tulsa, OK, USA) was used for all analyses.
With an ICC of 0.85–0.95 and CV of 3–5% for intrasession reliability and an ICC of 0.81–0.91 and CV of 4–6% for intersession reliability, the overall reliability of the tests was high (Table 1).
Correlations among the studied agility performances were statistically significant (at p ≤ 0.05), but this was because of the large sample of studied participants (n = 110). With correlations ranging from 0.40 to 0.54, preplanned and nonplanned agility performances shared less than 30% of the common variance (Table 2).
Significant main effects were demonstrated for playing positions and performance levels for most of the observed variables, with no significant interaction between positions and performance levels (divisions). Centers were the tallest, heaviest, and had highest BF%, whereas Forwards were taller, heavier, and had higher BF% than Guards (F: 75.30, p = 0.01; 42.05, 0.01; 3.37, 0.03, for body height, mass, and BF%, respectively). Forwards were most successful in T-TEST performance (F test: 13.57; p = 0.01). Guards outperformed Centers with respect to BBCODSdom, BBCODSnond, BBAGILdom, and BBAGILnond (F test: 5.06, p = 0.01; 6.57, 0.01; 6.26, 0.01; 3.37, 0.04, respectively). First division players were taller (F: 27.40, p = 0.01) heavier (F: 14.07, p = 0.01), had lower BF% (F: 19.78, p = 0.01), and outperformed second division players in BBCODSdom (F: 9.21, p = 0.01), BBAGILdom (F: 8.89, p = 0.01), and BBAGILnond (F: 10.29, p = 0.01) (Table 3).
First division Guards were heavier (t: 4.10; p = 0.01; d = 1.22, large differences) and had lower BF% (t: 2.87; p = 0.01; d = 0.87, moderate differences) than second division Guards. Furthermore, first division Guards outperformed second division Guards in BBCODSdom (t: 2.55; p = 0.02; d = 0.80, moderate differences), BBAGILdom (t: 3.04; p = 0.01; d = 0.85, moderate differences), and BBAGILnond (t: 3.06; p = 0.01; d = 0.89, moderate differences) (Table 4).
First division Forwards were taller than second division Forwards (t: 3.56; p = 0.01; d = 1.54, large differences). No significant differences were obtained between performance levels among Forwards with respect to agility, but ES magnitudes were moderate for T-TEST (d = 0.84) and BBCODSdom (d = 0.62) (Table 5).
Centers who compete in the first division were taller (t: 4.22; p = 0.01; d = 1.42, large differences), heavier (t: 2.56; p = 0.02; d = 0.86, moderate differences), had lower BF% (t: 3.08; p = 0.01; d = 0.96, moderate differences), and achieved significantly better results than second division Centers for BBAGILdom (t: 2.50; p = 0.02; d = 0.81, moderate differences) (Table 6).
This study aimed to investigate the reliability and validity of basketball-specific tests to evaluate preplanned and nonplanned agility performances. With respect to the main study aims, several key findings emerge. First, the newly developed tests of preplanned and nonplanned agilities are found to be reliable measuring tools. Next, the tests are found to be applicable for defining position-specific agility performance. Finally, the basketball-specific nonplanned agility tests are found to be more valid for determining differences between basketball athletes involved at 2 competitive levels than preplanned agility tests.
The reliability of a test is an elementary prerequisite of the test's applicability because it directly indicates the error of measurement (35). Therefore, studies have frequently reported reliability parameters of measurement protocols aimed at evaluating different conditioning capacities, including those tests seeking to evaluate agility performances (4,29,32). The reliability of the T-TEST is similar to that in previous reports that have investigated basketball athletes at an advanced level (31).
For the tests of preplanned and nonplanned basketball-specific agility evaluated herein, we may highlight reliability coefficients with similar values as those previously reported for other similar testing protocols in college-level athletes, various team-sport athletes and handball players (24,29,32). However, in a study that tested reactive agility in basketball players, the authors reported a CV of 1–2% for nonplanned reactive performance (26). Indeed, this is somewhat stronger within participant reliability than the level we obtained for nonplanned agility tests. However, it must be stated that the above-mentioned study demonstrated reliability while observing 5 participants, whereas we evaluated reliability for a larger sample of athletes. Furthermore, the test used in the cited study consists of only one change in direction performed as part of a “nonstop” movement pattern (26). In contrast, the protocol presented here includes (a) the “stop'n'go” template and (b) 2 changes in direction.
What is also important is that the reliability of the agility tests does not show great differences between the standard test used herein (i.e., T-TEST) and the newly developed basketball-specific tests of preplanned and nonplanned agility. Moreover, somewhat better reliability of the BBCODS tests compared with the reliability of the BBAGIL tests is expected and in accord with previous studies (24,29,32). In short, nonplanned agility performance includes perceptual and reactive components, which do not occur in the testing of preplanned agility (24,29). These specific “covariates of performance” are naturally sources of mistake, potential sources of measurement error, and consequently factors that could directly alter reliability (24).
Correlation of the tests showed that nonplanned and preplanned agility should be observed as relatively independent qualities (less than 30% of the common variance between tests), as already noted in previous investigations (29,32). First, regardless of the type of testing protocol (single vs. multiple changes of direction) and the movement pattern that occurs during testing (i.e., nonstop vs. stop'n'go templates), the perceptual and reactive capacities are challenged only during nonplanned agility performances (6,18,26). Moreover, those conditioning capacities that are proven to be important in preplanned agility (i.e., horizontal displacement abilities such as jumping and sprinting) are less important determinants of success in nonplanned agility (24).
Basketball is a sport with well-defined game responsibilities that are differentiated among playing positions (2,22,34), which is reflected in position-specific body build, and physical fitness differences among playing positions (20). Studies have frequently reported that backcourt players (i.e., Guards) possess quicker linear speed and better aerobic endurance than frontcourt players (i.e., Centers and Forwards) (2,16,27). However, despite consistent reports of differences in anthropometric and fitness status, there is an evident lack of agreement on differences in agility between playing positions in basketball (27).
More precisely, in a recent study, authors compared 6 frontcourt (i.e., Guards) and 6 backcourt players (i.e., Forwards and Centers) and indicated that the frontcourt players were superior in preplanned agility (i.e., closed-skill agility, CODS) (27). In another study that sampled elite Belgian basketball players, the authors reported Guards (i.e., backcourt players) as being superior in preplanned agility performance, and similar results are reported for Tunisian players (2,3). Meanwhile, Turkish athletes did not differ in preplanned agility performance when different positions were compared (16).
Because of the similarity to basketball movement techniques (forward-backward running, lateral shuffling), the T-TEST is regularly used to demonstrate preplanned agility in basketball (16,31). In our study, Forwards were the most successful of all players in T-TEST performance. Almost certainly, due to the relatively long duration of the T-TEST (i.e., 9.3 seconds on average), Forwards used their advantage in terms of body dimensions (i.e., height and consequent step length) to perform efficiently in this test. Because Forwards are actually “frontcourt players,” our results may even likely explain the contradictions indicated in studies previously overviewed (2,3,27).
Our results indicating that Guards (i.e., frontcourt players) are more successful in nonplanned agility performance than Centers are dissimilar to those reported in a previous Australian study (27). In that study, the authors reported no difference in nonplanned agility between playing positions (27). However, there are 2 key differences between these investigations. First, herein, we grouped athletes into 3 playing groups, whereas the Australian study revealed differences when the players were placed into 2 groups (i.e., frontcourt players and backcourt players). According to our results, it seems that the agility performances of Forwards are not significantly different from those of Guards (see Results; Guards achieved better results numerically but not significantly). Second, the Australian players were actually tested using a test protocol originally designed for other sports (i.e., rugby and Australian football), whereas we evaluated players with basketball-specific agility tests, which more accurately describe the existing differences among playing positions.
Over the past few years, a growing body of evidence has emerged in support of the high applicability of sport-specific testing protocols for evaluating those capacities that are challenged during sport competitions (i.e., a game) (32,35). Therefore, authors are increasingly aware of the need to develop test procedures that mimic the movement patterns and psychophysiological conditions that occur in the sport of interest (4,25). In general, tests and procedures that objectively simulate real-life sport conditions are found to be more valid in demonstrating sport-specific capacities and consequently are observed as being more ecologically valid than general nonspecific testing procedures (35). Accordingly, our results indicating that basketball-specific agility tests are applicable in defining position-specific agility performances are in accordance with previous investigations of other sports (13,23).
The only valid test is one that distinguishes the groups of interest (36). Our intention was to demonstrate the validity of the newly developed tests in defining differences between playing positions and competitive levels. Therefore, the appropriate discriminative validity of the agility tests for defining players' level of performance is likely the most important finding of this study. To some extent, our results proved the discriminative validity of the tests for the total sample of players; nevertheless, we observed specific competitive-level differences for playing positions as well.
Basketball-specific nonplanned agility should be considered an ability that more significantly distinguishes first division from second division players than preplanned agility. This finding is in accord with previous studies that have compared nonplanned agility (i.e., reactive agility) between groups of athletes and found superior performance among those classified as more successful with regard to characteristic sport achievement (28,32). However, to the best of our knowledge, this is the first study to examine differences in various agility performances between playing levels of basketball athletes within each of the 3 playing positions (i.e., separately for Guards, Forwards, and Centers).
The differences found between the first division and second division Guards indicate a similar level of importance of both BBAGIL and BBCODS for this playing position. Indeed, Guards are most frequently involved in game duties in which both preplanned and nonplanned agilities are challenged. For example, advanced preplanned agility (i.e., CODS) allows Guards to achieve a fast and efficient transition from defense to offence. In defensive duties, advanced nonplanned agility (i.e., reactive agility) allows defensive Guards to effectively react to an opponent's changes in direction, ensure a superior “lock-down” of a player with the ball, keep opponents away from the basket, and possibly cause a turnover (17).
We have found no significant difference between first and second division Centers for preplanned agility (BBCODSdom, BBCODSnond and T-TEST). Knowing the position-specific roles of Centers, these results are expected. Indeed, Centers are not as frequently involved in duties in which they have to demonstrate preplanned agility. They are positioned close to the basket, and almost never have to transfer the ball from defense to offense. If involved in counterattacks, their duties are almost exclusively related to final scoring without requiring maneuvers that involve CODS (19). However, Centers are frequently responsible for defensive duties in which they have to accurately and appropriately respond to an opponent's change in direction. Therefore, their nonplanned agility (reactive-agility) is frequently challenged, which was likely the cause of the significant dominance of the first division Centers with respect to BBAGILdom.
The lack of statistically significant differences between playing levels among Forwards can be described by the relatively fewer athletes we tested who play that position (49 Guards, 39 Centers, and 22 Forwards), which altered possibility to reach appropriate statistical significance of the differences among performance-levels (11). However, effect size differences for certain variables between playing levels among Forwards reached moderate magnitudes (i.e., moderate differences were found between performance levels for T-TEST and BBCODSdom). Therefore, it is reasonable to observe preplanned agility as important quality in Forward players. The authors of this study are of the opinion that the cause of such findings (i.e., higher importance of the preplanned agility) is similar to that previously discussed for Guards and their offensive duties, where preplanned agility is mostly challenged. However, the lack of differences in nonplanned agility for Forwards might be explained by different playing duties between 2 types of Forward players (i.e., Power-forwards and Small-forwards). In short, while Power-forwards are taller and heavier and therefore frequently execute some typical Center duties (i.e., rebounding), the Small-forwards are shorter, quicker, and more involved in different agile maneuvers (17). Unfortunately, the number of players studied herein did not allow for more profound analysis of differences between 2 types of Forward players.
There are several limitations of this study. First, nonplanned agility was tested by using a test in which players responded to a visual stimulus in which the choice of direction was limited to just 2 (i.e., left or right). Therefore, future studies should pay attention to this stimulus and develop testing protocols that include multiple reaction possibilities and possibly another type of stimulus. Second, we observed players of senior age (i.e., older than 18 years), and it is questionable whether the tests used are applicable to the position orientation of players in their younger age. However, our primary goal was to develop and evaluate a test that consists of basketball-specific movement patterns, and according to the presented results, we succeeded in this respect to some extent.
The testing protocols developed and evaluated in this study, which are based on the “help and recover” defensive basketball strategy, are found to be reliable measurement tools for evaluating agility performances among basketball players. The results presented can be used as normative data for other basketball athletes who perform at a similar level.
Preplanned and nonplanned agilities should not be observed as a unique quality. Therefore, separate testing of these capacities is required to objectively determine the conditioning level for each of these abilities. However, attention is needed when each of these protocols is used to define position-specific and competition-level-specific agility performances.
Guards achieved better results than Centers in both the preplanned and the nonplanned basketball-specific agility tests. Therefore, both measurement procedures are applicable for defining position-specific differences in agility performance between players involved in these 2 positions in basketball. At the same time, a standard T-TEST is found applicable for distinguishing Forwards from the other 2 playing positions.
Both preplanned and nonplanned agilities are important for differentiating between Guards who perform at 2 competitive levels (performance levels). For these players, both capacities should be applied to objectively evaluate their characteristic efficacy in offense (by testing preplanned agility) and defense (by testing nonplanned agility).
The agility of Center players is chiefly challenged during defensive duties, when they have to efficiently react to opponents' changes in direction. Our results confirm the importance of testing nonplanned agility when evaluating the true game agility of Centers. Moreover, preplanned agility is not identified as a factor that distinguishes Center players at different competitive levels.
Authors are particularly grateful to all athletes who voluntarily participated in the study. The authors declare that they have no conflict of interests relevant to the content of this manuscript. The results of this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.
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