*Article* **Variations of the Locomotor Profile, Sprinting, Change-of-Direction, and Jumping Performances in Youth Soccer Players: Interactions between Playing Positions and Age-Groups**

**Ana Filipa Silva 1,2,3 , Sümer Alvurdu <sup>4</sup> , Zeki Akyildiz <sup>4</sup> , Georgian Badicu <sup>5</sup> , Gianpiero Greco 6,\* and Filipe Manuel Clemente 1,2,7**


**Abstract:** The purpose of this study was two-fold: (i) analyze the variations of locomotor profile, sprinting, change-of-direction (COD) and jumping performances between different youth age-groups; and (ii) test the interaction effect of athletic performance with playing positions. A cross-sectional study design was followed. A total of 124 youth soccer players from five age-groups were analyzed once in a time. Players were classified based on their typical playing position. The following measures were obtained: (i) body composition (fat mass); (ii) jump height (measured in the countermovement jump; CMJ); (iii) sprinting time at 5-, 10-, 15-, 20-, 25- and 30-m; (iv) maximal sprint speed (measured in the best split time; MSS); (v) COD asymmetry index percentage); (vi) final velocity at 30-15 Intermittent Fitness Test (VIFT); and (vii) anaerobic speed reserve (ASR = MSS − VIFT). A twoway ANOVA was used for establishing the interactions between age-groups and playing positions. Significant differences were found between age-groups in CMJ (*p* < 0.001), 5-m (*p* < 0.001), 10-m (*p* < 0.001), 15-m (*p* < 0.001), 20-m (*p* < 0.001), 25-m (*p* < 0.001), 30-m (*p* < 0.001), VIFT (*p* < 0.001), ASR (*p* = 0.003), MSS (*p* < 0.001), COD (*p* < 0.001). Regarding variations between playing positions no significant differences were found. In conclusion, it was found that the main factor influencing changes in physical fitness was the age group while playing positions had no influence on the variations in the assessed parameters. In particular, as older the age group, as better was in jumping, sprinting, COD, and locomotor profile.

**Keywords:** football; athletic performance; physical fitness; exercise test

#### **1. Introduction**

The soccer match requires from the players a well and multilateral developed physical fitness [1,2]. In fact, considering that the soccer game is an intermittent exercise with a mix of bioenergetic demands it is expectable to observe in players a good ability to sustain prolonged efforts and, at the same time, the availability to intensify the actions

**Citation:** Silva, A.F.; Alvurdu, S.; Akyildiz, Z.; Badicu, G.; Greco, G.; Clemente, F.M. Variations of the Locomotor Profile, Sprinting, Change-of-Direction, and Jumping Performances in Youth Soccer Players: Interactions between Playing Positions and Age-Groups. *Int. J. Environ. Res. Public Health* **2022**, *19*, 998. https://doi.org/10.3390/ ijerph19020998

7

Academic Editor: Paul B. Tchounwou

Received: 17 December 2021 Accepted: 15 January 2022 Published: 17 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

in more powerful movements [3,4]. Looking into the physical demands of the match, most of the time is spent in low-to-moderate running intensities [5,6], although the percentage of time spent in high intensity running or sprinting has been increasing over the years for the same total distance covered [7–9]. Thus, the match is becoming more intense requiring from the players a superior level of conditioning to sustain such intensifications [10,11]. Although ultimate performance in soccer is multifactorial, some of the critical events (e.g., goal-oriented events) are closely related to high-intensity running demands [5,6]. Thus, holding a good physical fitness can be a support for the ultimate performance [12,13].

Characterizing physical fitness is now a well-implemented practice in soccer [14]. The battery of tests is commonly used in different periods of the seasons helping strength and conditioning coaches to individualize the training process, identify the status of the players and observe the evolution trends of the players over the season [14]. Moreover, in the particular case of youth, longitudinal observations/assessments also allow to identify talents across the time. Although talent identification is a complex and multidimensional process [15,16], using physical fitness parameters as part of talent identification processes is still prevalent [17]. As example, using physical fitness batteries allows to identify that in some age groups the taller, heavier and more physical advanced players are those with higher levels [18,19]. Although these facts not being exclusively related to the ultimate players' selection and transition for professional careers, tracking players over time can provide some additional information about which expectations coaches should have regarding their players and the evolution trends of the players over time and also determine how players can cope with match intensity [20].

For the case of physical fitness, it seems that the breaking point of 14/15 years old is the one in which change-of-direction (COD), linear sprint, standing long jump and aerobic capacity tests makes more sense are more sensitive to age-related changes in functional characteristics [21,22]. Moreover, testing batteries consisting in either vertical/horizontal jumps, sprinting and COD and aerobic fitness seems to be sensitive enough to distinguish between different youth age groups [23]. Interestingly, the most common tests as countermovement jump (CMJ), 5-0-5 (COD test), 10- to 20-m linear sprint test or standing broad jump are proven to be highly reliable and valid for youth soccer players [24].

Although the above-mentioned tests present a good consensus about the usability for practice, some other tests can be used directly helping coaches to prescribe the training process and classify the youth players. As example, the 30-15 Intermittent Fitness Test (30-15IFT) has been used for standardize the training intensity while applying high-intensity interval training [25]. Moreover, combining the final velocity at 30-15IFT and the maximal sprint speed (MSS) it is possible to obtain the anaerobic speed reserve (ASR) of the players and classify them into their locomotor profile (e.g., speed, hybrid, and endurance) [26].

Observing positive changes of physical fitness across the age-groups seems to be expectable [27]. However, in the context of soccer, playing positions seems to play an important role to differentiate players [27]. In a study conducted in a large sample of 744 youth players it was observed that after the age of 15, the attackers tends to be more explosive, the fastest and more agile players [28]. This tendency of observing greater differences in the later stages of development programs was also confirmed in a study conducted in 465 youth players [29].

The relevance of characterization the progression of physical fitness across age-groups, while in interaction with playing position seems to be obvious. This may help coaches to better specify and individualize the training process and classify the players based on their abilities to sustain and maintain match intensities. Therefore, the purpose of this study was two-fold: (i) analyze the variations of locomotor profile, sprinting, change-of-direction (COD) and jumping performances between different youth age-groups; and (ii) test the interaction effect of athletic performance with playing positions.

#### **2. Materials and Methods**

#### *2.1. Study Design*

This study followed a cross-sectional design. Players were recruited in the same team and no randomization was made. Age groups of 19 and 17 years old were assessed on 31 August 2021 and 1 September 2021. Age groups of 16 and 15 years old were assessed on 1 September 2021 and 2 September 2021. Age group of 14 years old was assessed on 2 September 2021 and 3 September 2021. The study begun after 3 weeks of the beginning of the season. As context, 24 number of training sessions were performed, and 3 friendly matches occurred before the study begun.

#### *2.2. Participants*

The G\*Power (version 3.1.) was used to calculate the a priori sample size. For a partial eta squared of 0.6 (medium effect size), a *p* = 0.05, power of 0.80, numerator df of 8 and number of groups of 10, the suggested total sample size was 20. A total of 124 young male elite male soccer players from U15 (*n* = 29), U16 (*n* = 30), U17 (*n* = 27), U18 (*n* = 25), and U19 (*n* = 12) teams were recruited voluntarily to participate in the study. All these players were regularly involved in two training sessions a week (90 min per session) with participation in one match at the weekend. Players and their guardians were informed about the study design and protocol. After being informed for potential risks of the study, guardians signed informed consent forms. This study followed the ethical principles of the Helsinki Declaration for human research. A local ethics committee also approved the protocol. Inclusion criteria for the participants were (i) being an active player with at least three years license, (ii) no history of any injuries during the previous two months, (iii) participating %85 of the training during the study period.

#### *2.3. Testing Procedures*

The study were carried out in two different days, separated by a minimum of 48 h. On the first day, anthropometric assessment (height, body mass and body fat percentage), and performance tests (vertical jumping, sprinting and change-of-direction ability) were applied respectively. The assessments of the first day occurred at 2:00 p.m. of the day, in a room conditioned at 24 degrees Celsius and 52% relative humidity. Second day, 30-15 IFT test were performed to evaluate the final velocity (VIFT) and anaerobic speed reserve (ASR) in the following conditions: 03:00 p.m., 19 degrees Celsius and 49% relative humidity. Players were familiarized with all test at the previous seasons. All performance tests were conducted on a synthetic turf field (where the players train and compete) after a standardized FIFA 11+ warm-up protocol [30] (ref).

#### 2.3.1. Anthropometry

A measuring tape (SECA 206, Hamburg, Germany) and a digital scale (SECA 874, Hamburg, Germany) with an accuracy of 0.1 kg were used to measure the height and body mass of the participants. Body fat percentage was evaluated with 4-site skinfold measurement (biceps, triceps, iliac crest and subscapular) according to the Durnin–Womersley formula [31]. At least two measurements were taken from each athlete and if there was more than 5 percent difference between the two measurements, the third measurement was taken.

#### 2.3.2. Jumping Performance

Countermovement Jump (CMJ) was used to evaluate participants' jumping performances with Optojump optical measurement system (OptojumpNext, Microgate, Bolzano, Italy). The participants performed three vertical attempts with 2 min recovery and the best attempt was used for the analyses. During the attempt, the participants were asked to jump keeping their hands on the hips and without bending the legs from take-off and landing phase.

#### 2.3.3. Sprinting

The 30 m linear sprint test with 5 m splits (5-, 10-, 15-, 20-, 25-, 30) were measured using the electronic timing gates system (Smartspeed, Fusion Sport, QLD, Australia). The timing gates were positioned at 1.2 m height of the floor. Players positioned 0.5 m far from the first timing gate and were encouraged to sprint at maximum speed and were given to two attempts with three minutes of recovery to prevent fatigue. Players took their preferred foot one step forward before the start and no signal was given. They started in split position, and always with the same preferred leg. The best sprinting time (lower value) was used for the analysis.

#### 2.3.4. Maximal Speed Sprint

The MSS was estimated using the average time over the last 10- and 5-m splits of a 30-m sprint test. A previous study revealed that using both 10- and 5-m splits of a 30-m sprint test while using timing gates can be reliability and present a high level of agreement with the MSS estimated using a gold-standard radar gun [32].

#### 2.3.5. Change-of-Direction Ability

The Arrowhead agility test was used for the participants' COD ability. Electronic timing gates system (Smartspeed, Fusion Sport, QLD, Australia) positioned at the start line with a height of 1.2 m starting from the floor. The participant positioned 0.50 m from the timing gate and sprinted from the start line to the middle marker (A), turned to the left or right side to sprint around the marker (B), sprinted around the top marker (C) and sprinted back through the timing gate to finish the test [33]. Athletes were asked to use their right leg when they turned left, and their left leg when they turned right as breaking legs. The test was performed for left and right sides with four randomized attempts separated by at least three minutes of recovery. The best attempts of each side was recorded for analysis.

The asymmetry index was calculated according to the following formula [34]:

Asymmetry Index percentage (AI%): AI% = [(COD time Dominant − COD time Non-dominant)/COD time Dominant] × 100

#### 2.3.6. Velocity at 30-15 IFT and Anaerobic Speed Reserve

The 30-15IFT was performed by the participants according to the protocol developed by Buchheit [25]. The tests consist in perform 30 s shuttle runs interspersed with 15 s of passive recovery. The test starts with a velocity of 8 km/h. The speed increases by 0.5 km/h after each stage (30-s). Every time the player was unable to reach the line with the pace imposed by the audio beep, was marked. After failing three consecutive times, the final velocity achieved correctly was considered for further analysis. The last completed stage was used to determine the final velocity (VIFT) and anaerobic speed reserve was calculated as the difference between MSS and VIFT with the following equation [35]:

$$\text{ASR (km/h)} = \text{MSS} - \text{V}\_{\text{IFT}}$$

#### *2.4. Statistical Analyses*

Shapiro-Wilk and Levene tests were used to test the assumption of normality and homoscedasticity, respectively. Both, normality and homogeneity were confirmed with *p* > 0.05. Then, Bonferroni homoscedasticity and Two-way ANOVA were used, respectively. The Two Way ANOVA with Bonferroni post hoc test was used to compare player positions and ages. All statistical analyses were performed using RStudio Version: 2021.09.1 + 372. Statistics at a significance level of *p* < 0.05. The following scale was used to classify the effect sizes (ES) of the tests: small, 0.2–0.49; moderate, 0.50–0.79; large, 0.80–1. Partial eta-squared was used ANOVA and Cohen D to pairwise comparisons.

#### **3. Results**

Two-way ANOVA tested interactions between age-groups and playing positions. No significant interactions were found on height (*p* = 0.031; η 2 <sup>p</sup> = 0.235), body mass (*p* = 0.235; η 2 <sup>p</sup> = 0.171), body fat (*p* = 0.635; η 2 <sup>p</sup> = 0.121), CMJ (*p* = 0.027; η 2 <sup>p</sup> = 0.239), 5-m (*p* = 0.412; η 2 <sup>p</sup> = 0.146), 10-m (*p* = 0.490; η 2 <sup>p</sup> = 0.137), 15-m (*p* = 0.582; η 2 <sup>p</sup> = 0.127), 20-m (*p* = 0.464; η 2 <sup>p</sup> = 0.140), 25-m (*p* = 0.178; η 2 <sup>p</sup> = 0.182) and 30-m (*p* = 0.252; η 2 <sup>p</sup> = 0.168), MSS (*p* = 0.388; η 2 <sup>p</sup> = 0.149), VITF (*p* = 0.166; η 2 <sup>p</sup> = 0.18 4), ASR (*p* = 0.441; η 2 <sup>p</sup> = 0.143), COD right (*p* = 0.159; η 2 <sup>p</sup> = 0.186), COD left (*p* = 0.662; η 2 <sup>p</sup> = 0.118), and COD-AI% (*p* = 0.598; η 2 <sup>p</sup> = 0.125).

One-way ANOVA tested the variations of physical fitness measures between agegroups. Descriptive statistics can be found in the Table 1 (anthropometrics) and Table 2 (physical fitness). Results revealed that the age group of 14 years old was significantly smaller and lighter (*p* < 0.05) than the remaining age groups. No other significant differences were found regarding anthropometric outcomes.

**Table 1.** Descriptive statistics (mean and standard deviation) for the anthropometric outcomes between age-groups.


Yo: years old; BM: body mass; Body fat: BF; significant different from 14 yo <sup>a</sup> ; 15 yo <sup>b</sup> ; 16 yo <sup>c</sup> ; 17 yo <sup>d</sup> ; and 18 yo <sup>e</sup> at *p* < 0.05.

Results from Table 2 revealed that the younger age group (under-14) had significant smaller values of CMJ (*p* < 0.05), was significantly slower at 5-, 10-, 15-, 20-, 25- and 30-m distances and COD right (*p* < 0.05), and had significant smaller MSS, VIFT, and ASR (*p* < 0.05) than the remaining age-groups.

**Table 2.** Descriptive statistics (mean and standard deviation) for the physical fitness outcomes between age-groups.



**Table 2.** *Cont.*

Yo: years old; CMJ: countermovement jump; MSS: maximal sprint speed; VIFT: final velocity at 30-15 Intermittent fitness test; ASR: anaerobic speed reserve; COD: change-of-direction; COD-AI%: Change-of-Direction Asymmetry Index percentage; significant different from 14 yo <sup>a</sup> ; 15 yo <sup>b</sup> ; 16 yo <sup>c</sup> ; 17 yo <sup>d</sup> ; and 18 yo <sup>e</sup> at *p* < 0.05.

One-way ANOVA tested the variations of physical fitness measures between playing positions. Descriptive statistics can be found in the Table 3 (anthropometrics) and Table 4 (physical fitness. Results from Table 3 revealed that central defenders and forwards were significantly taller and heavier (*p* < 0.05) than the remaining positions. No significant differences were found regarding body fat.

**Table 3.** Descriptive statistics (mean and standard deviation) for the athropometric outcomes between playing positions.


CD: central defender; ED: external defender; CM: central midfielder; EM: external midfielder; F: forward; BM: body mass; Body fat: BF; significant different from CD <sup>a</sup> ; CM <sup>b</sup> ; ED <sup>c</sup> ; EM <sup>d</sup> ; and F <sup>e</sup> at *p* < 0.05

Results from Table 4 revealed no significant differences between playing positions regarding the physical fitness outcomes.

**Table 4.** Descriptive statistics (mean and standard deviation) for the physical fitness outcomes between playing positions.


CD: central defender; ED: external defender; CM: central midfielder; EM: external midfielder; F: forward; BM: body mass; Body fat: BF; CMJ: countermovement jump; MSS: maximal sprint speed; VIFT: final velocity at 30-15 Intermittent fitness test; ASR: anaerobic speed reserve; COD: change-of-direction; COD-AI%: Change-of-Direction Asymmetry Index percentage; significant different from CD; CM; ED; EM; and F at *p* < 0.05.

In Figure 1, descriptive plots for anthropometry, CMJ, 5-m and 10-m were presented. Although no significant differences between playing positions, it is evident a significant difference of the younger age-group for being smaller and lighter than the remaining age groups.

− − − − −

**Figure 1.** Descriptive plots for (a) height (cm); (**b**) body fat (%), (**c**) body mass (kg); and (**d**) CMJ (cm).

In Figure 2, descriptive plots for 15 m, 20 m, 25 m, 30 m, Maximum Speed, and ASR were presented. It seems evident a significant trend for being faster as older as players are (independently of the distance considered in the sprint test). Moreover, maximal speed sprint and anaerobic speed reserve also increase as players are older.

In Figure 3, descriptive plots for COD Right, COD Left, Asymmetry Index, COD-AI%, and VIFT were presented. As older players are, the better COD performance they get. Although no significant differences can be observed in the asymmetry index with exception of the pair of 15 and 16 years old. The VIFT is also significantly rising with the increase of age groups.

**Figure 2.** Descriptive plots for (**a**) 5-m; (**b**) 10-m; (**c**) 15-m; (**d**) 20-m; (**e**) 25-m; and (**f**) 30-m sprint time (s) and (**g**) maximal speed sprint (km/h); and (**h**) anaerobic speed reserve (km/h).

**Figure 3.** Descriptive plots (**a**) COD right leg (s); (**b**) COD left leg (s); (**c**) asymmetry index percentage (%); and (**d**) VIFT (km/h).

#### **4. Discussion**

The current cross-sectional study conducted over 124 youth soccer players revealed that age groups play a significant effect on physical fitness while playing positions were not capable to determine variations in physical fitness. Considering that significant changes in physical fitness were found between age groups, it was also observed that the older the groups, the better the results. Therefore, from 14 to 18 years old, the players turn taller, heavier, faster, while jumping higher and having a greater locomotor profile to sustain the efforts.

#### *4.1. Age Group Comparisons*

The normal growth patterns were found in the current research, namely considering the progressive increase of height and body mass until the last stage of youth [36,37]. Thus, the older the player is in youth soccer, the taller and heavier is. Such an evidence is confirmed in previous studies comparing different age groups withing the period of growth [38,39]. Interestingly, in the contrary to a possible expectation of observing an improvement in body fat levels [39,40], no significant differences were found across the age-groups in the current study. One of the causes could be the small body fat levels observed in the current study (mean values were stable around 8% over the ages) which is low, mainly in comparison to the studies reporting body fat in youth soccer players which presented values between 7 and 11% [36]. Also, in the opposite to expected [28,41], no significant differences were found in anthropometric and body composition data between playing positions. Although the current sample does not include goalkeepers (which is one of the positions favorable to be taller than remaining) [42], it would be expectable to observe significant variations between the remaining positions. As average (since interactions with age was not found), playing positions varied from 170 to 180 cm, while body mass between 60 and 70 kg. Although variations were observed, no significances were found, which may indicate that the tendency for selecting players based on playing position may not be too

much implemented in the context of this group of players (considering that all of them belong to the same club).

In the current study it was found that as older the players as faster they are. Considering the different measures related with sprinting performance (e.g., 5-, 10-, 15-, 20-, 25-, 30-, and MSS) and COD performance it was observed a progressive and significant improvement until reach the final stage of youth soccer (i.e., 18 years old). These tendencies are in line with previous reports for youth soccer players [43,44]. Some possible explanations can be related with the growth and maturation that plays an important role in the muscular adaptation and neural drive, and bioenergetics to sustain MSS in late puberty [45]. Lower limb power observed in the improvements of CMJ over the age groups considered can possibly explain those advantages in neural and muscular adaptations over age. Although huge differences in the determinants that explains different linear sprint distances and COD, it was interesting to observe that older were always better in any of sprint test distances, COD measures and CMJ.

Therefore, it can be argued that older tends to accelerate better (possibly explained by the greater concentric force and power which was possible observed by the increases in CMJ performance over the age-groups) [46], achieve higher velocities (possible explained by a greater eccentric force, vertical force and power) [47] and can decelerate and accelerate better due to the better neuromuscular properties [48] developed in accordance to the training process, and normal increase in muscular adaptation and neural drive. In, fact, considering that older can reach a greater MSS than younger [49], it is expectable that such a stimulus in match and training can play an important role in the development of sprinting and COD performance since achieving peak speed is an effective way of improve it [50,51]. However, as major factors can be listed maturation and the related neural function, multi-joint coordination, muscle stiffness, and changes in muscle architecture [52].

In the current study it was also found that locomotor profile determined by ASR and VIFT followed the trend of the older, the better. Considering that locomotor profile is highly associated with aerobic fitness, it is expectable to observe natural and progressive increases after the maturational peak until reach the 16 years old in males [52,53]. These changes and increases are potentially explained by changes occurring in central mechanisms namely considering the increase of heart, lungs, muscles and blood volume [54,55]. Naturally, other factors as hormonal or enzymatic can be also important for ultimately improving the progressive improvement of aerobic fitness during the youth stages [56]. Thus, this can justify improvements in aerobic power as well as in the maximal aerobic speed which may justifies improvements in VIFT [57]. Considering that VIFT is justified by different measures including aerobic fitness, change-of-direction or lower limb power, and taking in consideration that the older, the better in these levels, VIFT tends to be improved also across the age-groups. Moreover, considering that anaerobic systems is improved after peak maturation [58,59], it is also expectable to assist to an improvement of ASR as well [60].

#### *4.2. Playing Position Comparisons*

One of the common trends observed over the current results were the absence of playing position effect on the physical fitness variation of youth soccer players. This is not in line with most of studies conducted in soccer, mainly those conducted in later stages of youth formation [28,29]. Possibly, a better fitness level observed can mask positional differences that traditionally occur in players based on the specificity of the training process and match demands. Future research should focus in analyze if a proper training process can mitigate differences between playing positions, or on the other side, a training process based on the average and not individuality can also decrease differences between playing positions.

#### *4.3. Study Limitations, Future Research and Practical Implications*

The fact of the study has been conducted in only one club can be a source of bias, like many other cross-sectional studies conducted in this field of research. Observational analytic studies to determine differences between age groups and playing positions should be made in the future with more than one context and determine how the context can play a role or not in the evidence collected. Despite the limitation, this study was conducted in 124 players which is substantial and allows a sample enough to confirm the evidence. As practical implications, this study may suggest that as older, as better. Thus, with the progression in age, a more focused stimulus can be provided on the physical fitness, and possible more individualization and specificity of training can occur to ensure the adjustment to the position specificities of the game.

#### **5. Conclusions**

This study revealed that the older, the better in terms of physical fitness in youth soccer players. Considering the age-groups included (14 to 18 years old), improvements in locomotor profile, sprinting, change-of-direction, and jumping performance were significant and obvious. Younger players were significantly smaller and lighter, while were significantly slower, jump smaller and had less maximal speed sprint, anaerobic speed reserve and VIFT. Although this evidence was not found significant interactions of age-group with playing positions and, additionally, playing positions did not differentiate athletes.

**Author Contributions:** Conceptualization, A.F.S., Z.A. and F.M.C.; methodology, S.A.; formal analysis, Z.A.; data curation, S.A.; writing—original draft preparation, A.F.S., S.A., Z.A., G.B., G.G. and F.M.C.; writing—review and editing, A.F.S., S.A., Z.A., G.B., G.G. and F.M.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is funded by Fundação para a Ciência e Tecnologia/ Ministério da Ciência, Tecnologia e Ensino Superior through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of University of Gazi (approval: GAZI/2021).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Raw data of this article are available upon request to corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Specific Physical Ability Prediction in Youth Basketball Players According to Playing Position**

**Jelena Ivanovi´c 1,2 , Filip Kuki´c <sup>3</sup> , Gianpiero Greco 4,\* , Nenad Koropanovski <sup>5</sup> , Saša Jakovljevi´c <sup>6</sup> and Milivoj Dopsaj 6,7**


**Abstract:** This study investigated the hierarchical structure of physical characteristics in elite young (i.e., U17-U19) basketball players according to playing positions. In addition, their predictive value of physical characteristics was determined for the evaluation of players' physical preparedness. Sixty elite male basketball players performed 13 standardized specific field tests in order to assess the explosive power of lower limbs, speed, and change-of-direction speed. They were divided into three groups according to playing positions (guard [*n* = 28], forward [*n* = 22], center [*n* = 10]). The basic characteristics of the tested sample were: age = 17.36 ± 1.04 years, body height = 192.80 ± 4.49 cm, body mass = 79.83 ± 6.94 kg, and basketball experience = 9.38 ± 2.10 years for guards; age = 18.00 ± 1.00 years, body height = 201.48 ± 3.14 cm, body mass = 90.93 ± 9.85 kg, and basketball experience = 9.93 ± 2.28 years for forwards; and age = 17.60 ± 1.43 years; body height = 207.20 ± 3.29 cm, body mass = 104.00 ± 9.64 kg, and basketball experience = 9.20 ± 1.62 years for centers. For all playing positions factor analysis extracted three factors, which cumulatively explained 76.87, 88.12 and 87.63% of variance, respectively. The assessed performance measures were defined as significant (*p* < 0.001), with regression models of physical performance index (PPINDEX). PPINDEX of guards = −6.860 + (0.932 × *t*-test) − (1.656 × Acceleration 15 m) − (0.020 × Countermovement jump); PPINDEX of forwards = −3.436 − (0.046 × Countermovement jump with arm swing) − (1.295 × Acceleration 15 m) + (0.582 × Control of dribbling); PPINDEX of centers = −4.126 + (0.604 × Control of dribbling) − (1.315 × Acceleration 15 m) − (0.037 × Sargent jump). A model for the evaluation of physical performance of young basketball players has been defined. In addition, this model could be used as a reference model for selection procedures, as well as to monitor the efficacy of applied training programmes within the short, medium and long-term periodization.

**Keywords:** measurement; power test; speed test; change of direction speed test; guard; forward; center

#### **1. Introduction**

Body height, muscular power, speed, and strength are all important elements of the basketball player profile. Power, speed, and change of direction speed significantly contribute to the movement efficiency of basketball players with the ball and without it, as well as in technical and tactical elements of basketball game [1–3]. While body height

**Citation:** Ivanovi´c, J.; Kuki´c, F.; Greco, G.; Koropanovski, N.; Jakovljevi´c, S.; Dopsaj, M. Specific Physical Ability Prediction in Youth Basketball Players According to Playing Position. *Int. J. Environ. Res. Public Health* **2022**, *19*, 977. https:// doi.org/10.3390/ijerph19020977

Academic Editor: Richard B. Kreider

Received: 14 December 2021 Accepted: 13 January 2022 Published: 16 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

is genetically predetermined, power, speed and change of direction speed are subject to training adaptation and could be used for the assessment of players' physical potential to overcome the challenges of a basketball game [3–5]. This is important in selection as well as training evaluation processes. Identification of younger players who have good physical potentials for basketball game reduces the probability of false selection, while early detection of deficits in the main physical abilities indicates that the training could be adjusted and may reduce the risk of unwanted injuries.

Managing the selection and training process depends on the adequacy of the assessment system in collecting information on athlete's or a team's training level in order to provide a precise evaluation of training level [6–8]. Furthermore, the usability of results obtained by the assessment depends on the specificity and sensitivity of the applied tests. The more specific the test is with regard to sport, the representation of competitive readiness is more valid [1,8,9]. If the correct data is collected from athletes, the coach can follow the trend in core physical abilities of basketball players through the age categories, and he can timely correct the training program to attain the short, medium, and long-term goals.

The available bibliography reveals the lack of design and use of specific tests to assess the physical attributes of the young basketball players, especially according to age categories and playing positions [1,8]. Growth and maturation affect physical abilities and physical performance [10–12], while different basketball positions present different demands and require specific physical attributes [13–15]. According to the results of a previous systematic review [1], the least common evaluated capacities in basketball players in literature are speed and agility. Tests of a generic nature have more frequently been used for assessing physical fitness in basketball players, e.g., aerobic and anaerobic capacity or jump performance [1,3,8,9,13–16]. Besides, only a few pieces of research have dealt with specific tests while dribbling the ball in basketball [1,8]. Consequently, talent identification, selection, and evaluation of training processes are very important parts of the systematic approach to the consistent competitive success of basketball team.

Considering the aforementioned factors, this study aimed to determine the hierarchical structure of physical characteristics in elite young (i.e., U17-U19) basketball players according to playing positions. In addition, their predictive value of physical characteristics was determined for the evaluation of players' physical preparedness. It was, firstly, hypothesized that significant hierarchical structure of physical abilities will be determined. Secondly, it was hypothesized that the highest ranked variables from the hierarchical structure could be the best predictors of players' physical performance.

#### **2. Materials and Methods**

#### *2.1. Participants*

The sample consisted of 60 male basketball players from the U19 and U17 Serbian national team. In order to obtain the most informative indicators to improve the technological process of managing, we recruited relatively large samples of participants to secure a sufficient statistical power. Besides, we selected a group of elite basketball players who won eight international medals in a period of four years during the biggest World and European competitions. They were allocated into three groups according to playing positions, as follows: Guards (*n* = 28, i.e., point guard and shooting guard), forwards (*n* = 22, i.e., small forward and power forward) and centers (*n* = 10). Basic characteristics of the tested sample were: age = 17.36 ± 1.04 years, 18.00 ± 1.00 years, 17.60 ± 1.43 years; body height = 192.80 ± 4.49 cm, 201.48 ± 3.14 cm, 207.20 ± 3.29 cm; body mass = 79.83 ± 6.94 kg, 90.93 ± 9.85 kg, 104.00 ± 9.64 kg; and training experience = 9.38 ± 2.10 years, 9.93 ± 2.28 years, 9.20 ± 1.62 years for guards, forwards and centers, respectively. All participants (athletes, coaches, and parents) were informed that their data may be used anonymously for scientific purposes and they were informed about the potential risks and discomforts associated with the investigation, and measurements were conducted out with their parental consent in line with the Helsinki Declaration. The Institutional Ethics Committee approved the research.

#### *2.2. Measurement Procedure*

All the tests were performed by the Serbian Institute of Sport and Sports Medicine at the beginning of the main pre-competitive mesocycle. Players were requested to refrain from strenuous exercise for at least 48 h, and from eating 2 h before testing. The testing session was carried out during morning hours between 10:00 and 12:00 a.m.

Before tests, players had performed a standardised warm-up, consisting of 5 min jogging, 5 min dynamic stretching, and 5 min of short acceleration-decelerations, gradual building of running velocity, submaximal jumping, and agility exercises. For the last five minutes of warm-up, players performed tests with submaximal intensity to potentiate specific muscles and joints. It is of note that the Serbian Institute of Sport and Sports Medicine asses the best Serbian athletes (i.e., members of the national teams) on a regular basis so the used tests were familiar to athletes. The assessment protocol for basketball athletes consists of sprint tests (with and without the ball), change of direction speed tests (with and without the ball), and vertical jump tests. Straight run speed, change of direction speed, and vertical jump heights were measured using Infrared timing gates and contact mat (Fusion sport, SmartJump and SmartSpeed, Grabba International Pty Ltd., Australia). The time of the run dribble was measured in seconds, with an accuracy of ±0.01 s. Jump tests are characterized by a very good test–retest reliability (in general Intraclass correlation coefficients are higher than 0.90) [13,16,17].

#### 2.2.1. Sprint Tests

A 20 m sprint was performed from the standing position with the front foot placed on the line 30 cm behind the photocells. Times were recorded by infrared timing gates placed at the start, at 5 m (first-step quickness [Q5m]), 15 m (acceleration [A15m]), and finish line (Sprint 20 m [S20m]). Players performed the 20 m sprint two times without the ball and two times while dribbling the ball (S20mD). The best time obtained from the trial was used for statistical analysis [8,13,18].

#### 2.2.2. Change of Direction Speed with and without the Ball

The following five tests were used to assess change-of-direction speed: *t*-test (TTEST), Slalom, Control dribble test (COND), Defensive movements test (DM), Change of direction speed test [2,3,8,16,18]. For the purpose of this study, we applied the standardized procedures used in the previous study [8].

TTEST requires the athlete to move in a T-shaped pattern. According to earlier described procedures [8,13,16,18], the photocells were placed at the starting line and in line with central cone positioned 9 m away from starting position. The athletes started from the standing position, and ran forward 9 m as fast as possible. Then, they shuffled 4.5 m laterally to the left without crossing their feet to another cone. After touching this cone, they shuffled laterally 9 m to the right to a third cone, touched it, side shuffled back to the middle cone, and ran backward to where they started.

In case of Slalom (Slalom), and Slalom while dribbling the ball (SlalomD), each participant started the test with his feet behind the baseline of the basketball court. Subjects were required to run (dribble), as fast as possible up and down the course around the three cones placed linearly with 2.6 m distance. They performed two trials with and without the ball and the fasters ones were used for the analysis [8].

The COND test was performed at the 5.8 m × 3.6 m rectangle polygon marked by six cones positioned as follows: two at both ends of the free-throw lane, two at the baseline aligned with those at the free-throw line, one in the middle of the rectangle, and one that marked the starting point [8]. The athletes were required to navigate dribbling through a course as fast as possible. The athletes started with their non-dominant hand on the non-dominant side of cone A. They dribbled with non-dominant hand to the non-dominant side of cone B, and then proceed to cone C and cone D, dribbling with the dominant hand. The course continued with the non-dominant hand to cone E and then with the dominant hand to cone F where the test was completed (Figure 1). Three trials were completed for the

test. The first was a practice trial and the sum of the second (starting with non-dominant hand) and third trials (starting with dominant hand) was retained for analysis.

**Figure 1.** Schematic illustration of the Control of dribbling test. A, B, C, D, E, F—cones; dotted arrow—direction of dribbling.

The DM was used to evaluate the performance of defensive movements. It was performed at the same rectangle polygon as COND, but two cons were positioned at the halfway point of the longer edges of the rectangle. This test was carried following the procedure described in a previous study [8]. The player was required to shuffle laterally without crossing the feet in a sequence of seven changes of direction. Whenever the players changed direction, they were required to touch the floor and execute a drop-step (changing direction by moving the trailing foot in the sliding motion to the new direction (Figure 2). The fastest of the two trials was recorded for the analysis.

A change of direction test (COD) consists of a sprint with several changes of direction. The athletes started in the triple-threat position behind the baseline of the basketball court. Players were required to run (dribble) and to change direction as fast as possible to two different lines, namely, the near free-throw line (5.8 m) and the half-court line (14 m). The athletes sprinted to the free-throw line first and back to the baseline, then to the half-court line and back to the baseline, and finally to the free-throw line again and back to the baseline. Before every change of direction, they were required to step on the line with one foot. After changing direction, they were required to change the dribbling hand. Each athlete was allowed two trials with and without the ball and the fastest one was retained for analysis. Two players performed the test at the same time to encourage maximal effort [8].

**Figure 2.** Schematic illustration of the Defensive movements test. A, B, C, D, E, F—cones; dotted arrow—direction of movements.

#### 2.2.3. Vertical Jumps

The following four types of vertical jump were performed: Sargent jump (SGJ), Squat Jump (SJ), Countermovement jump with arm swing (CMJAS) and Countermovement jump (CMJ). In case of SGJ, the athlete chalked the end of his/her fingertips, stood sideways onto the wall, kept both feet on the ground, reached up as high as possible with one hand and marked the wall with the tips of the fingers (M1). From a static position, they jumped as high as possible and marked the wall with the chalk on their fingers (M2). The distance between M1 and M2 was used to calculate jump height. The athlete repeated the test 2 times [2]. SJ and CMJ vertical jump height were performed according to well-established procedures [13,16–18]. In short, SJ was performed from the 90-degree semi-squat position using only the maximal contraction of lower limbs, while CMJ was performed utilizing the energy from the stretch–shortening cycle. In SJ and CMJ, hands were kept at the hips for the entire movement to eliminate any influence of the arm. A CMJAS was performed the same way as CMJ but players were allowed to swing with their hands upward. Two maximal jumps were performed, and the highest result was registered as the final result.

#### *2.3. Statistical Procedures*

The mean and standard deviation values for each test were calculated for each subgroup (guards, forwards, and centers). For all the tests involving several trials, test–retest reliability was assessed using intraclass correlation coefficients (ICC). For defining the structure, i.e., real qualitative relationships between variables, the principal component analysis (PCA) was used. A multivariate assessment of the adequacy of the raw data was carried out using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's Tests of sphericity (*p* < 0.001), for which statistical significance was expressed in terms of a chi-square (χ 2 ). Eigenvalues > 1 were considered for the extraction of principal components. A Direct Oblimin rotation method was performed in order to identify the high correlation of components and guarantee that each principal component offered different information [19]. A criterion variable from factor analysis was used as a representation of the player's multidimensional physical performance index (PPINDEX) according to playing position so each player could be compared against the criterion value for their playing position [20]. Multiple regression analysis with the PPINDEX as the criterion variable and the performance test variables as predictor variables determined the unique evaluation of specific preparedness of basketball players according to playing position [20]. Statistical significance for all analyses was defined as *p* < 0.001. All statistical operations were carried out by applying the Microsoft® Office Excel 2010 and the SPSS for Windows, Release 20.0 (Copyright © SPSS Inc., Chicago, IL, USA, 1989–2002).

#### **3. Results**

Results for the descriptive statistics (Mean and Standard deviation) of the observed characteristics with regard to different playing position and Intraclass correlation coefficients (ICCs) for relative test–retest reliability are shown in Table 1. It can be observed that, in terms of positions, forwards were faster than guards and centers in Q5m and Q5mD, while guards were faster than forwards and centers in the majority of change-of-direction speed and sprint tests. In addition, guards achieved a greater jump height compared with forwards and centers. The average inter-item correlation in all variables described mutual correlation within a correlation matrix at a statistically significant level at *p* < 0.001 (Bartlett's test of Sphericity) and ranged between 0.689 for A15m<sup>D</sup> and 0.992 for CMJ, indicating a good reliability.

**Table 1.** Descriptive statistics and Intraclass correlation coefficients.


\* TTEST: *t* test total time; DM: Defensive movements; S20mD: Sprint with dribbling 20 m; CODD: Change of direction with dribbling; Slalom: Slalom; SlalomD: Slalom with dribbling; A15mD: Acceleration with dribbling 15 m; COD: Change of direction; COND: Control of dribbling; S20m: Sprint 20 m; A15m: Acceleration 15 m; Q5mD: Quickness with dribbling 5 m; Q5m: Quickness 5 m; CMJ: Countermovement jump without arm swing; SJ: Squat jump; CMJAS: Countermovement jump with arm swing; SGJ: Sargent jump; \* *p* values: *p* = 0.000.

The KMO showed a high statistical significance of multivariate adequacy of the given variables at the level of 0.561 (χ <sup>2</sup> = 848. 338, *p* < 0.001) for guards, at the level of 0.677 (χ <sup>2</sup> = 689.135, *p* = 0.001) for forwards, and at the level of 0.558 (χ <sup>2</sup> = 744.770, *p* = 0.001). For all playing positions, the factor analysis extracted three significant factors (Table 2), which cumulatively explained 76.867, 88.123 and 87.633% of variance in guards, forwards, and centers, respectively.

**Table 2.** Saturated factors with the structure indicators of the explained variance.


Table 3 shows the structure matrix with the variable saturation for each playing position. Measured physical characteristics provide a similar factor structure for each position, with a lateral change of direction speed being highly ranked in guards, jumping ability in forwards, and change of direction speed between baseline and free-throw line in centers. The second factor included straight-run speed measures with and without the ball for all three positions. The third factor included a jumping performance in guards, change of direction speed while dribbling the ball, defensive movement in forwards, and jumping performance in centers (with emphasis on jumps with arm swings). This suggests that the measured characteristics with regard to different playing positions have different structures in the function of isolated factors, which may be attributed to their adaptation to specific training process.


**Table 3.** Factor analysis structure matrix for each playing position.

TTEST: *t*-test total time; DM: Defensive movements; S20mD: Sprint with dribbling 20 m; CODD: Change of direction with dribbling; Slalom: Slalom; SlalomD: Slalom with dribbling; A15mD: Acceleration with dribbling 15 m; COD: Change of direction; COND: Control of dribbling; S20m: Sprint 20 m; A15m: Acceleration 15 m; Q5mD: Quickness with dribbling 5 m; Q5m: Quickness 5 m; CMJ: Countermovement jump without arm swing; SJ: Squat jump; CMJAS: Countermovement jump with arm swing; SGJ: Sargent jump.

The results of the defined regression analysis have shown high predictive potential for PPINDEX of guards (AdjR<sup>2</sup> = 0.893, F = 165.597, *p* < 0.001, Standard Error of the Estimate = 0.33), forwards (AdjR<sup>2</sup> = 0.896, F = 170.577, *p* < 0.001, Standard Error of the Estimate = 0.31), and centers (AdjR<sup>2</sup> = 0.875, F = 138.412, *p* < 0.001, Standard Error of the Estimate = 0.34). The final mathematical models for evaluation of PPINDEX of guards, forwards, and centers is as follows:

PPINDEX of guards = −6.860 + (0.932 × *T* test) − (1.656 × Acceleration 15 m) − (0.020 × Countermovement jump),

PPINDEX of forwards = −3.436 − (0.046 × Countermovement jump with arm swing) − (1.295 × Acceleration 15 m) + (0.582 × Control of dribbling),

PPINDEX of centers = −4.126 + (0.604 × Control of dribbling) − (1.315 × Acceleration 15 m) − (0.037 × Sargent jump).

In this manner, by a very simple mathematical model, coaches could be provided with a tool for the evaluation of players' physical preparedness according to position, in terms of a deterministic, fully controlled system.

The regression analysis further reduced the multidimensionality of players' physical preparedness to the most essential components that predict the PPINDEX of young players with high precision. The best predictors in guards included TTEST, A15m, CMJ. The best −

predictors in forwards were CMJAS, A15m, COND. The best predictors in centers were COND, A15m, and SGJ. Thus, the highest-ranked variables in each factor were the best predictors of PPINDEX for the corresponding positions. The regression model allows for the qualitative and quantitative evaluation of players on the three investigated positions (See Figure 3).

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**Figure 3.** Positioning of players according to playing position from the aspect of specific preparation level.

#### **4. Discussion**

This study investigated the hierarchical structure of physical characteristics in elite young basketball players and evaluated their predictive value in the evaluation of players' physical preparedness. The main findings showed that specific change of direction speed performance was the highest-ranked characteristic in guards, specific jumping performance was the highest-ranked characteristic in forwards, while the control of specific movements while dribbling the ball was the highest-ranked characteristic in centers. Moreover, a significant prediction model for the evaluation of physical preparedness was defined for each playing position. These findings are of high importance as they provide a screening tool for selection and training evaluation processes.

Considering the structure of the basketball game, players are required to perform numerous technical–tactical elements characterized by agile movements in space in a planned manner or as a response to the opponent's actions [21]. Shooting guards within their roles and duties perform a higher number of lateral shuffles, forward and backward sprints on relatively bigger area than centers or small forwards. This could be attributed to the role in the game that guards have, such as losing the defender further from the basket by quick directional changes with and without the ball and quick return to defend the basket. Therefore, running speed, agility, and rapid recovery are critical fitness components, particularly for this position [9,13–15].

Forwards, on the other hand, typically perform a high number of jumps whether offensively to score the basket or defensively when rebounding, which is also emphasized in the training process. Thereby, jumping characteristics are of high importance for this position. Basketball players typically perform 40–50 jumps per game, generating force rapidly to perform various tasks such as rebounding, blocking opponent shot attempts, and creating elevation for a jump shot [2]. The movement structure of CMJ and CMJAS corresponds to the bilateral vertical jumps that players most often perform when they are shooting from the distance to advance their ball release height and when they are trying to block the opponent. In addition, forwards are also responsible for the quick return to defence and to defend the space by quick lateral shuffles.

Centers are usually referred to as "frontcourt", often acting as their team's primary below-the-basket rebounders and shot blockers. They also receive passes to take inside shots for which they must control their body, opponent, and the ball. Therefore, it is not surprising that centers, as major players on the team, require a high level of control of the specific movement with the ball to maintain their body position when battling with the opponents for important positions under the basket. However, considering the game rules that do not allow staying below the basket longer than 5 s, center is required to move constantly in a square-shaped space from the baseline to the free-throw line. They need to be agile compared to other centers so they could position themselves in a good position repeatedly.

The second factor consisting of acceleration and sprint for all positions indicated the importance of these characteristics for basketball players. Short sprints represent a multidimensional movement skill that requires an explosive concentric and SSC force production of a number of lower-limb muscles [22]. During a game, the players are rarely in a situation where they have to sprint across the whole court. Therefore, sprint tests over shorter distances and acceleration are more appropriate to administer to basketball players [3,8,22]. Indeed, to a large degree (certainly more than power and agility) speed is genetically predetermined, thereby fast players are selected rather than "made fast", especially considering the sample of this study that consisted of elite players for their age category [23]. This does not reduce the importance of this factor, but additionally suggests that the applied strength and conditioning training could include strength and power exercises that may additionally improve running speed and acceleration or reduce the risk of injury caused by these activities [24–26]. It is interesting to mention that center were slightly faster than forwards in the A15 and A15<sup>D</sup> tests (Table 1). However, if an index of technical efficiency is calculated (the ratio between the A15 and A15<sup>D</sup> tests), it may be concluded that forwards are still more efficient than centers. Although there are no data in the available literature on the 15 m acceleration test in basketball, results of some previous research showed no significant difference between playing position in the 20 m sprint [9,13]. Even more, these authors suggested that despite their size and weight, centers are as fast as smaller players. Besides that, significant effect of playing position on sprint performance increase in shorter (10 m and shorter) and longer (20 m and longer) distance [13,14] which strongly support our findings.

The last factor is vertical jump performances in guards and centers, showing that these characteristics, although not dominant, are a very important pillar of a basketball player's physical preparedness. The most representative variable in the third factor was the countermovement jump for guards and Sargent jump for centers. The obtained differences in hierarchy of this factor correspond to differences in how guards and centers perform jumps in the game. Guards are typically jumping free from the opposing player (i.e., no contact with opposing player) and from previously performed movement, while centers are typically jumping from the spot, while in contact with the opposing player with one hand and reaching high with the other to block, rebound or score. Unlike guards and centers, the third factor extracted change of direction speed and speed variables, whereby most representatives were the control of dribbling. This is not surprising, given that forwards often perform dribble penetration to advance to the basket [9,13–15].

There is a scarcity of studies that address the specific characteristics of physical fitness in basketball players, even though the battery of basic performance tests are widely used. The reason for that could lie in a fact that possibility of providing a sample of the best selected players for the age category is low. Research dealing with the hierarchical structure and equation of specification in relation to specific performance tests in basketball is practically non-existent in the available literature. The lack of reference to these problems has certainly reduced the possibility to compare our findings with other studies. Data on the defined latent structure of standard indicators of situational efficiency in the game of basketball [27] or in relation to the tests of generic nature [28] can be found in the available literature. The results of that research have shown that the highest total variance in 13 male and 13 female semiprofessional basketball players was represented by aerobic capacity and in-game physical conditioning [28]. In addition, as one of the main limitations of this study, the authors mentioned the need for the inclusion of specific basketball-field tests (e.g.,

agility with and without the ball, anaerobic capacity) to evaluate the physical performance of basketball players [28]. In relation to the research that used similar methodology [20], the results suggest that it is possible to create sport- position-specific prediction model for evaluation physical preparedness.

#### *Limitations*

However, some limitations should be acknowledged when interpreting the results of this research. An apparent limitation of this study is the results may not be generalized to other age groups or females. In order to apply the obtained results in general, it is necessary to conduct extensive research that includes the examination of physical ability on a large sample of basketball players, of different ages, competitive level and for both genders. Another limitation originates from the cross-sectional design that does not allow for the identification of the effects of physical activity from the initial selection of the subjects.

#### **5. Conclusions**

The results obtained in this research show that the measured characteristics with regard to different playing positions have different structures in the function of isolated factors under the influence of different mechanisms with regard to the training process. As a factor analysis has a primarily discriminatory character, the first factor with observed variables where the basketball players differ most is the most important one. Specific change direction agility abilities, i.e., specific locomotion on the court is the most important element within guard position in elite youth basketball players. Specific jumping ability is the most important element within the forward position. Control of specific movement with the ball is the most important element within center position.

#### *Practical Applications*

With the multiple regression analysis, the influence of the selected variables on the physical performance index (PPINDEX) was obtained, and the equation of specific basketball preparedness according to playing position. This index represents the position of the participant on a hypothetical scale with a minimum of 0 and a maximum of 100 points. In this manner, it is possible to obtain relevant data in relation to physical ability characteristics and, indirectly, to obtain the performance potential of a given athlete. Thus, a useful means for the level of physical fitness determination of youth basketball players has been obtained, as well as a comprehensive reference model for use in selection procedures, screening candidates, or to monitor the efficacy of training regimes.

**Author Contributions:** Conceptualization, J.I. and M.D.; methodology, J.I., M.D. and S.J.; validation, F.K. and G.G.; formal analysis, J.I., M.D. and F.K.; investigation, J.I. and S.J.; resources, J.I., G.G., F.K. and S.J.; data curation, J.I., G.G., F.K. and N.K.; writing—original draft preparation, J.I., M.D., G.G. and F.K.; writing—review and editing, J.I., F.K., G.G. and N.K.; supervision, M.D., F.K. and G.G.; project administration, J.I. and F.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Commission of the Faculty of Sport and Physical Education, University of Belgrade (protocol code 482-2, February 2011).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The paper was realized as part of project III47015 sponsored by Ministry of Science and Technological Development in the Republic of Serbia.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

