DOI: https://doi.org/10.21203/rs.3.rs-1039187/v1
Objective: To investigate maximal aerobic speed (MAS), participants of team sports in terms of certain variables and to determine relationship team sports.
Design: Screening research.
Setting: Elite soccer, basketball and handball players.
Participants: 44 athletes.
Main Outcome Measures: 20-meter shuttle run test (20MSRT) was used.
Results: There was a positive relationship between age, VO2max and speed scores (p < 0.05). Besides, significant difference between VO2max, distance and speed also VO2max, distance and speed were found to be significantly different according to types of sports (p < 0.05). When distance and speed scores of athletes were examined, it was determined that mean scores of football players were higher compared to basketball and handball. Heart rate and MAS scores of participants were not significantly different according to type of sport played.
Conclusions: This study will contribute to strength and strength coaches, trainers and physiotherapists in terms of training programs that they will apply to athletes of various sports.
Many studies are carried out to identify characteristic qualities of competitors in different sports. Thanks to the developing technology, sports science renews itself day by day and tries to discover training techniques that increase success. In this context, contemporary training methods consider effects of high-intensity aerobic running speed training on physiological and performance values in team sports. Moreover, research is conducted on individual training applications related to different types of sports (Özen et al., 2020). MAS is defined in the literature as the speed of movement produced by an athlete at maximal aerobic power or 100% of VO2max (Bosquet et al., 2002; Ermanno Rampinini et al., 2009). MAS measurements are made in km/h. It is more important to know the MAS, which is mandatory to adjust the running speed, which in turn facilitates physiological development, than to know VO2max (Karatosun, 2012). It is thought that being able to adjust running speed of the athletes according to needs of the sports that they play will have an important role in their performances. On the other hand, it is thought that these running speeds may differ between sports, players’ positions, and even genders. It is known that MAS are determined optimally according to type of sport and comparisons can be made accordingly. In this way, it becomes easier and more efficient to follow the physiological development of athletes (Baker & Heaney, 2005). This process is attracting more interest day by day from sports scientists. Furthermore, looking at the current literature on MAS, it is seen that it is next to this (Edis et al., 2007; Mülazımoğlu, 2012). On the other hand, when examining studies conducted outside of Turkey, it is seen that there is slightly more research available in the literature (Baker, 2015; Baker & Heaney, 2005; Dellal et al., 2008; González-Badillo et al., 2015).
As a result, this study will contribute to the existing literature in terms of relationships between types of sports and age of athletes and maximal aerobic speed. Therefore, the purpose of this research is a comparison of maximal aerobic speed (MAS) of young athletes and investigation of optimal training loads.
The research group comprised football (n=16), basketball (n=12) and handball players (n=16) who were active in team sports in the 2018-2019 season in Trabzon, Turkey. There were 44 male participants in total. Current average age was 17.20 ± 1.0 years, average height was 178.6 ± 6.6 cm and average weight was 73.1 ± 11.2 kg. Average age, height, weight and body mass index of football players were 18.00 ± 0.0 years, height 175.03 ± 7.17 cm, weight 68.53 ± 9.33 kg and 22.22 ± 1.80 kg/m2. Average age, height, weight and body mass index of basketball players were 16.41 ± 1.31 years, height 181.33 ± 5.10 cm, weight 75.16 ± 12.15 kg and 22.80 ± 3.10 kg/m2. Average age, height, weight and body mass index of handball players were 17.00 ± .63 years, height 180.18 ± 5.95 cm, weight 76.18 ± 11.47 kg and 23.42 ± 2.98 kg/m2.
The aim of this study is to compare the MAS of young athletes participating in team sports and to determine the optimal training loads. In this study, a quantitative research approach was used. As a methodological approach, the screening method was preferred in order to determine the most obvious characteristics of the participants, such as their skills and attitudes (E. Rampinini et al., 2007). The 20-meter shuttle run test was applied as a data collection technique by the researchers to the athletes forming the working group after obtaining the necessary permissions. Athletes voluntarily participated in the implementation of the data collection tool, and informed consent forms were obtained from them or their families.
Before proceeding to the analysis stage, the data of the research were tested to determine whether they were suitable for normal distribution with the Shapiro-Wilk W test and kurtosis and skewness values. Parametric techniques were used because the distributions were observed to be normal. The data were analysed with the SPSS 23 package program. In this context, information about the research group and various research variables were evaluated using descriptive statistics techniques. As data analysis techniques, one-way analysis of variance (ANOVA) and Pearson product-moment correlation coefficients were used. In the analysis of the data, significance levels were taken as 0.05.
Within the scope of this research, the Trabzonspor Kadir Özcan Youth Development Centre’s synthetic field was used for the 20-meter shuttle run test. Active male athletes from football, basketball, and handball voluntarily participated in the study. Six running tracks, consisting of a flat area of 20 meters, were used for the test. The starting and ending points of the created areas were determined by marking them with training funnels. The command signals necessary for the test were transferred from a computer environment to the athletes via a sound system. Before starting the test, the athletes were allowed to acclimate to low-intensity shuttle running exercises. In addition, the athletes were encouraged to run at the maximum level during the test and to complete the test at the maximum level. “The running speed at the start of the test is 8.5 km and the running speed is gradually increased by 0.5 km per minute. The test ended with two faults occurring repeatedly or when athletes reached their burnout levels” (Tamer, 2000).
A heart rate monitor (Polar RS 800, Finland), which can measure heart rate instantly, was used to determine the heart rate and save it to the computer.
There are many different formulas and test models to calculate MAS directly and indirectly (Baker and Heaney 2015). Baker and Heaney obtained some normative aerobic fitness data for MAS scores for athletes competing in field sports. In this sense, they used the following tests to determine MAS: laboratory tests, Multistage Montreal Beep, VAMEVAL, YoYoIR1, Carminatti’s test, Multistage Shuttle Beep, Set Time Trial, Set Distance Trial, and 1200-m Shuttle. The test model applied in the current study is the Multistage Shuttle Beep (20-m shuttle run test). The formula for calculating MAS in this test is as follows: (MAS=Latest speed (km/h)×1.34-2.86) [13]. The result of this formula gives us MAS in km/h. It should then be converted to m/s so that training running distances can be more easily calculated. For example, the MAS of an athlete whose test is completed at the 13th shuttle level is calculated as follows: 13th shuttle speed=14.5 km/h. We need to put it into the formula and convert it to m/s as follows: MAS= 14.5×1.34-2.86=16.5 (km/h)×1000/3600=4.6 m/s. There is also a general correction formula for calculating MAS with estimated VO2max (MAS=Estimated VO2max/3.5), which gives us the result in kilometres per hour (Eyüpoğlu, 2016).
In this section, the appropriateness of the variables for normal distribution is examined with skewness and kurtosis values, and demographic information and descriptive values related to the research variables are shown both on a general basis and according to the type of sport played. Afterwards, information about the test results is included. The arithmetic averages of test performance parameters (HR, VO2max, distance, speed, and MAS) of the football, basketball and handball players are presented in Table 1.
| Parameter | n | Mean | SD | Min. | Max. | Sport branch | Parameter | Mean | SD | Min. | Max. |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Football (n=16) | HR (b.min−1) | 193,81 | 6,69 | 180,00 | 203,00 | ||||||
| VO2max (ml. kg−1 min−1) | 55,47 | 5,98 | 44,60 | 62,60 | |||||||
| Distance (m) | 2176,25 | 487,83 | 1420 | 2800 | |||||||
| HR (b.min−1) | 44 | 196,77 | 7,62 | 180,00 | 212,00 | Speed (km/h) | 13,81 | 0,99 | 12,00 | 15,00 | |
| Mas (m/sn) | 3,92 | 0,60 | 3,11 | 4,79 | |||||||
| VO2max (ml. kg−1 min−1) | 44 | 49,70 | 7,28 | 37,23 | 62,60 | Basketball (n=12) | HR (b.min−1) | 196,58 | 6,96 | 188,00 | 207,00 |
| VO2max (ml. kg−1 min−1) | 49,59 | 4,41 | 43,00 | 56,60 | |||||||
| Distance (m) | 44 | 1647,00 | 613,73 | 680,00 | 2800,00 | Distance (m) | 1548,33 | 412,92 | 940 | 2220 | |
| Speed (km/h) | 12,45 | 0,96 | 11,00 | 14,00 | |||||||
| Speed (km/h) | 44 | 12,65 | 1,34 | 10,50 | 15,00 | Mas (m/sn) | 3,84 | 0,35 | 3,30 | 4,42 | |
| Handball (n=16) | HR (b.min−1) | 199,87 | 8,16 | 83,00 | 212,00 | ||||||
| Mas (m/sn) | 44 | 3,76 | 0,48 | 3,11 | 4,79 | VO2max (ml. kg−1 min−1) | 44,01 | 5,65 | 7,23 | 54,77 | |
| Distance (m) | 1192,0 | 435,0 | 680,0 | 2060,0 | |||||||
| Speed (km/h) | 11,65 | 0,99 | 0,50 | 13,50 | |||||||
| Mas (m/sn) | 3,54 | 0,37 | 0,11 | 4,23 |
Relationships between participants’ heights, weights, ages, Bmi and HR, VO2max, distance, speed, MAS scores was analysed through Pearson correlation. The relationships of the variables with the dependent variables is shown in Table 2. Accordingly, a positive significant relationship was found between the dependent variable of MAS and age, VO2max, distance, and speed. According to these relationships, MAS increases as the age, VO2max, distance, and speed of the athletes increase. When other variables were examined, a positive correlation was observed between age scores and VO2max and speed. A significant positive correlation was found between VO2max, another variable, and distance and speed. When Bmi scores are analysed, a negative relationship was observed between Bmi and VO2max and speed.
| Parameter | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
| Height (cm) | 1 | ,67** | -,13 | -,25 | -,18 | -,27 | -,27 | -,26 | -,18 |
| Weight (kg) | 1 | -,09 | ,89** | -,04 | -,40** | -,36* | -,37* | -,27 | |
| Age (years) | 1 | -,04 | -,12 | ,42** | ,55** | ,56** | ,32* | ||
| Bmi (kg/m2) | 1 | ,06 | -,36* | -,37* | -,33* | -,25 | |||
| HR (b.min−1) | 1 | -,27 | -,24 | -,27 | -,16 | ||||
| VO2max (ml. kg−1 min−1) | 1 | ,98** | ,99** | ,70** | |||||
| Distance (m) | 1 | ,99** | ,68** | ||||||
| Speed (Km/h) | 1 | ,69** | |||||||
| Mas (m/sn) | 1 |
The relationships between some parameters and the MAS values of participants of different sports were tested with ANOVA. The results are given in Table 3. As a result of this analysis, VO2max, distance, and speed were found to differ according to type of sport. In the LSD test performed to find source of difference, VO2max scores favour football, basketball and handball. In favour of football and handball scores showed a significant difference in favour of basketball. Among the distance values of the athletes, the average of football players was highest (X̅ = 2176.25, SD = 487.83) compared to basketball (X̅ = 1548.33, SD = 412.92) and handball (X̅ = 1192.50, SD = 435.72). The average distance score of the basketball players was higher than that of handball players. In terms of speed values, basketball players had the highest average among the three types of sports. There were no significant changes in HR or MAS scores of the participants according to type of sport. In the MAS scores of the athletes, a significant difference was found in favour of football between football and handball players. While there was no significant difference in the ANOVA test, the significant difference seen in the LSD test may be due to the precise measurement of the LSD test.
| Football (n=16) | Basketball (n=12) | Handball (n=16) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Parameter | Mean | SD | Mean | SD | Mean | SD | F | p | Sig. |
| HR (b.min−1) | 193.81 | 6.69 | 196.58 | 6.96 | 199.87 | 8.16 | 2.73 | .76 | 1-3 |
| VO2max (ml.kg−1min−1) | 55.47 | 5.98 | 49.59 | 4.41 | 44.01 | 5.65 | 17.48 | .00 | 1-2, 1-3, 2-3 |
| Distance (m) | 2176.25 | 487.83 | 1548.33 | 412.92 | 1192.50 | 435.72 | 19.53 | .00 | 1-2, 1-3, 2-3 |
| Speed (km/h) | 13.81 | .99 | 12.45 | .96 | 11.65 | .99 | 19.39 | .00 | 1-2, 1-3, 2-3 |
| Mas (m/sn) | 3.92 | .60 | 3.84 | .35 | 3.54 | .37 | 2.94 | 0.64 | 1-3 |
In this research, relationships between athletes’ height, weight, age, and Bmi and HR, VO2max, distance, speed, and MAS scores were examined with Pearson correlations. A positive correlation was found between age, VO2max, and speed and MAS values.
According to this relationship, MAS scores increase as the age, VO2max, distance, and speed of the athletes increase. It can be said that the MAS score will increase as the age variable increases. This can be described as a developmental process and it shows parallelism with other studies performed (Baquet et al., 1999). However, no study investigating the relationship between VO2max, distance, and speed variables and MAS scores was found. In this sense, when the relationships of these variables are interpreted, according to the formula MAS is calculated as (MAS = Latest speed (km/h) × 1.34 - 2.86). The higher the speed variable, the higher the MAS value will be indirectly. According to the protocol of the 20-meter shuttle run test, when the relationship of MAS with distance is interpreted, distance covered must be increased in order to obtain the speed value of participants or to obtain next speed level. According to the VO2max formulas used in the calculations in our study, for the VO2max value to be high, the speed score should also be high (Baker, 2015).
The relationship between athletes’ HR, VO2max, distance, speed and MAS values and the type of sport was tested with ANOVA. As a result of this analysis, it was found that VO2max, distance and speed differ according to type of sport. In the LSD test conducted to find the source of the difference, there was a significant difference in VO2max. Scores of football players were highest in comparisons of football, basketball and handball, while basketball scores were higher than those for handball. In this sense, when studies on footballers are examined, had similar findings for VO2max values (Crisp et al., 2013; Eyüpoğlu, 2016; Helgerud et al., 2001). In studies of basketball players, similar VO2max findings (Crisp et al., 2013). Finally, when studies of handball players are examined, lower VO2max values were calculated in a previous study (Parlak, 2018). For this reason, the training and conditioning of athletes can be predicted.
The average distance of those who played football was higher compared to basketball and handball. The average distance score of basketball players was higher than that of handball players. When similar previous studies are examined, which examines the distance between 8619 - 10,335m, the distance travelled about basketball, the average distance of athletes is 5587m, and the distance covered in handball found distances covered in their study as 3627m (Crisp et al., 2013; Helgerud et al., 2001; McInnes et al., 1995; Oba & Okuda, 2008). In this sense, it is thought that reason for the difference in distances between different types of sports is due to structural differences. In this sense, what separates games from each other, that is area measures or the playing time of the games. Based on these differences, it can be said that when the data in our study are compared, the same parallelism is revealed.
When looking at the speed values, a difference was observed in favour of football when comparing the scores for all three studied sports and in favour of basketball when comparing handball and basketball. When studies on football are examined, performance values related to the shuttle run test applied to football players in his study (Arslan, 2009). In that study, speed reached by young football players in the shuttle run test was found as 13.7 km/h, which is very close to speed obtained in our study (13.8 km/h). When we look at studies about basketball, results of shuttle tests to determine maximal oxygen consumption among basketball players. The study stated that basketball players were able to run an average of 2152 m in the 20-meter shuttle run test (Gürses & Akalan, 2018). This result corresponds to a speed of approximately 14 km/h in the test protocol. When this result is compared with current research (12.45 km/h), it is seen that previous speed score was higher. It is thought that such a difference may be due to athlete age, training, and conditioning levels. Finally, looking at work on handball, reported that handball players had an average speed of 13.25 km/h in the 20-meter shuttle run test. In this study, it was found that handball players had an average speed of 11.65 km/h. This speed is lower. Such a difference may be due to athlete age, training, and conditioning levels, as mentioned above (Suna et al., 2016).
No significant changes were observed in the HR and MAS scores of participants according to types of sports. However, in the LSD test, a significant difference was observed in the HR scores of the football and handball players in favour of handball. When we examine maximal HR results in studies on basketball, reported that HR of basketball players in 20-meter shuttle run test as 198.46 beats/min (Gürses & Akalan, 2018). In the current study, it was found to be 196.58 beats/min. When we examine other studies, found that average heart rate during basketball competitions to be 169 ± 9 beats/min (McInnes et al., 1995).
When we examine studies about football players, found that maximal heart rate to be 196.92 beats/min in a shuttle run test applied to young footballers (Arslan, 2009). Analysed HR of footballers and reported an average of 162 beats/min (Çiçek et al., 2004). Examining other sources, (Bangsbo et al., 1991) reported 164 beats/min for Danish players and different study reported that 171 beats/min for professional players (Ali & Farrally, 1991). As a result of the study, it was found to be 193.81 beats/min. When we examine works on handball, reported that HR of 180 beats/min for handball players in 20-meter shuttle run test. In the current study, it was found to be 199.87 beats/min. In addition, in a study testing physiological and physical capacities of elite male handball players, found maximal HR of handball players in the Yo-Yo test to be 191 beats/min. (Michalsik et al., 2015; Suna et al., 2016)
For the MAS scores of athletes, a significant difference was found between football and handball players in favour of football. While there was no significant difference in ANOVA test, significant difference in LSD test may be due to precise measurement of LSD test. As a result of our current study, MAS of football players was 3.92 m/s, while that of basketball players was 3.84 m/s and that of handball players was 3.54 m/s. When we examine studies in literature, it can be seen that there is very limited research on MAS. The value for Italy Series A footballers was reported as 4.91 m/s with rampinini test (Rosenblatt, 2014). The value for English Premier League football players was 4.85 m/s (test unknown) (Büyüköztürk et al., 2017), for France’s 1st League players was 4.75 m/s (VAMEVAL test) (Dellal et al., 2008), for Spain’s professional U16 club footballers was 4.5 m/s (Montreal test), for Spain’s professional U18 club footballers was 4.44 m/s (Montreal test), and for Spain’s professional U21 club footballers was 4.41 m/s (Montreal test) (González-Badillo et al., 2015).
1. It was found that as age, VO2max, and distance and speed scores of athletes increased, the MAS also increased.
2. A positive correlation was found between age and VO2max and speed scores.
3. A positive correlation was found between VO2max and distance and speed.
4. A negative relationship was found between Bmi and VO2max and speed. It was found that VO2max, distance, and speed differed according to types of sports. In the LSD test conducted to find the source of difference, there was a significant difference in VO2max in favour of football compared to basketball and handball scores, and in favour of basketball compared to handball scores. For the distance values of the athletes, it was observed that average of football players was higher compared to basketball and handball. Similarly, the average score of basketball players was higher than that of handball players.
5. When speed values for different types of sports were analysed, a difference was found in favour of football compared to basketball and handball, and scores for basketball were higher than those for handball.
6. No significant changes were observed in HR and MAS scores of the participants according to types of sports. However, in LSD test, a significant difference was found between football and handball in favour of football in MAS scores of the athletes.
1st day (92% MAS, 6 × 3 minutes) × 2 sets. 2nd day (96% MAS, 5 × 2 minutes) × 2 sets. 3rd day (4 × with 90% MAS, 90 seconds) × 2 sets. Between sets, rest time is given with 40% MAS for 2 minutes [8]. Program is shown in Table 4.
| Days | 1st day | 2nd day | 3rd day | |||
|---|---|---|---|---|---|---|
| MAS % | %92 | %40 | %96 | %40 | %100 | %40 |
| Distance (m) | UZ | KK | UZ | KK | UZ | KK |
| Time | 6 x 3dk | 2dk | 5 x 2dk | 2dk | 4 x 90sn | 2dk |
| Football | 649,15m | 188,16m | 451,58m | 188,16m | 352,8 | 141,12m |
| Basketball | 653,90m | 184,32m | 442,36m | 184,32m | 134,6m | 138,24m |
| Handball | 584,22m | 169,92m | 407,80m | 169,92m | 318,6m | 127,44m |
| UZ; Long edge, KK; Short edge, MAS; Maksimal aerobic speed | ||||||
1:1 Running/Rest-Active (15 s: 15 s). Long edge run with 100% MAS. Short edge run with 70% MAS, work starts with 6 minutes, 2-4 sets if 8 minutes run time is preferred. If 10 minutes is preferred, 1-2 set method can be used. 2-4 minutes rest time should be given between sets. Accordingly, the grid training model for football players, basketball players, and handball players is presented visually in Figure 1. [8].
1:1 Running/Rest-Passive (15 s:15 s) with 120% MAS. The total distance of departure and return can be applied as shown in Table 4. The work starts with 5 minutes; the intensity can be increased up to 8 or 10 minutes. It can be applied in the form of 1-2 sets and a rest period of 2-4 minutes can be given between sets. Accordingly, the Eurofit training model for football players, basketball players, and handball players is presented visually in Figure 2. [8].
2:1 Running/Rest-Passive (20 s:15 s), with 120% MAS. The total distance of departure and return can be applied as shown in Figure 1. The intensity of working from 5-6 minutes to 8 minutes can be increased. 2-5 sets can be applied and 4 minutes rest time can be given between sets. Accordingly, the Tabata training model for football players, basketball players, and handball players is presented visually in Figure 3. [8].
Ethical statements
Trabzon University internal ethics committee (81614018-30/22-10-2018).
Funding
This research did not receive any specific grant from fundingagencies in the public, commercial, or not-for-profit sectors.
Declaration of competing interest
None declared.
Acknowledgments
The authors would like to thank all the teams involved in this study who registered their data.
Ali, A., & Farrally, M. (1991). Recording soccer players’ heart rates during matches. Journal of Sports Sciences, 9(2), 183–189. https://doi.org/10.1080/02640419108729879
Arslan, E. (2009). Genç futbolcularda treadmille belirlenen maksimal oksijen tüketimi ile yo-yo ve mekik testine verilen performans cevaplarının incelenmesi. Ankara Üniversitesi.
Baker, D. (2015). Journal of Australian Strength and Conditioning. Journal of Australian Strength and Conditioning, 23(3), 29–38.
Baker, D., & Heaney, N. (2005). Review of the literature normative data for maximal aerobic speed for field sport athletes: a brief review. Journal of Australian Strength and Conditioning, 23(7), 60–67. https://ro.ecu.edu.au/ecuworkspost2013/6856/
Bangsbo, J., Nørregaard, L., & Thorsoe, F. (1991). Activity profile of competition soccer. Canadian Journal of Sport Sciences, 16(2), 110–116. https://europepmc.org/article/med/1647856
Baquet, G., Berthoin, S., Gerbeaux, M., & Van Praagh, E. (1999). Assessment of the maximal aerobic speed with the incremental running field tests in children. Biology of Sport, 16(1), 23–30.
Bosquet, L., Léger, L., & Legros, P. (2002). Methods to determine aerobic endurance. Sports Medicine, 37(11), 675–700.
Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel Araştırma Yöntemleri (Pegem Akad). https://doi.org/10.14527/9789944919289
Çiçek, Ş., Batchev, V., & Bizati, Ö. (2004). Maç Esnasinda Kalp Atim Hizi Professional Soccer Players During the Match. Gazi Beden E¤itimi ve Spor Bilimleri Dergisi, 9(3), 59–66.
Crisp, A. H., Verlengia, R., Sindorf, M. A. G., Germano, M. D., Cesar, M. de C., & Lopes, C. R. (2013). Time to exhaustion at VO2max velocity in basketball and soccer athletes. Journal of Exercise Physiology Online, 16(2), 82–91.
Dellal, A., Chamari, K., Pintus, A., Girard, O., Cotte, T., & Keller, D. (2008). Heart rate responses during small-sided games and short intermittent running training in elite soccer players: a comparative study. Journal of Strength and Conditioning Research, 22(5), 1449–1457.
Edis, A. Ş., Hazır, T., Şahin, Z., Hazır, S., Aşçı, A., & Açıkada, C. (2007). Genç futbol oyuncularinda saha ve laboratuvar koşullarinda submaksi̇mal ve maksi̇mal egzersi̇z şi̇ddetleri̇ne veri̇len fi̇zyoloji̇k cevaplar. Spor Bilimleri Dergisi, 18(2), 57–67.
Eyüpoğlu, E. (2016). Determination of Motoric Features of U-15 Football Teams Players. International Journal of Science Culture and Sport, 4(20), 864–864. https://doi.org/10.14486/intjscs636
González-Badillo, J. J., Pareja-Blanco, F., Rodríguez-Rosell, D., Abad-Herencia, J. L., del Ojo-López, J. J., & Sánchez-Medina, L. (2015). Effects of velocity-based resistance training on young soccer players of different ages. The Journal of Strength & Conditioning Research, 29(5), 1329–1338.
Gürses, V. V., & Akalan, C. (2018). Basketbolcularda aerobik performans , mekik koşusu ve YoYo aralıklı toparlanma testlerinin ilişkilerinin belirlenmesi. CBÜ Bed Eğt Spor Bil Dergisi, 13(1), 12–21. https://dergipark.org.tr/tr/download/article-file/498292
Helgerud, J., Engen, L. C., Wisløff, U., & Hoff, J. (2001). Aerobic endurance training improves soccer performance. Medicine and Science in Sports and Exercise, 33(11), 1925–1931. https://doi.org/10.1097/00005768-200111000-00019
Karatosun, H. (2012). Futbol’da Fiziksel Performans Gelişimi. ALTINTUĞ OFSET.
McInnes, S. E., Carlson, J. S., Jones, C. J., & McKenna, M. J. (1995). The physiological load imposed on basketball players during competition. Journal of Sports Sciences, 13(5), 387–397. https://doi.org/10.1080/02640419508732254
Michalsik, L. B., Madsen, K., & Aagaard, P. (2015). Physiological capacity and physical testing in male elite team handball. Journal of Sports Medicine and Physical Fitness, 55(5), 415–429.
Mülazımoğlu, O. (2012). Genç basketbolcularda yorgunluğun şut tekniğine etkisi. Selçuk Üniversitesi Beden Eğitimi ve Spor Bilim Dergisi, 14(1), 37–41.
Oba, W., & Okuda, T. (2008). A Cross-sectional Comparative Study of Movement Distances and Speed of the Players and a Ball in Basketball Game. International Journal of Sport and Health Science, 6, 203–212. https://doi.org/0904170015-0904170015
Özen, G., Atar, Ö., Yurdakul, H., Pehlivan, B., & Koç, H. (2020). The effect of pre-season football training on hematological parameters of well-trained young male football players. Pedagogy of Physical Culture and Sports, 24(6), 303–309. https://doi.org/10.15561/26649837.2020.0605
Parlak, O. (2018). 14-17 yaş genç erkek basketbol ve hentbolcuların bazı fizyolojik ve motorik özelliklerinin karşılaştırılması [Aydın Adnan Menderes Üniversitesi]. http://adudspace.adu.edu.tr:8080/jspui/bitstream/11607/3394/1/OLCAY PARLAK.pdf
Rampinini, E., Bishop, D., Marcora, S. M., Ferrari Bravo, D., Sassi, R., & Impellizzeri, F. M. (2007). Validity of simple field tests as indicators of match-related physical performance in top-level professional soccer players. International Journal of Sports Medicine, 28(3), 228–235. https://doi.org/10.1055/s-2006-924340
Rampinini, Ermanno, Impellizzeri, F. M., Castagna, C., Coutts, A. J., & Wisløff, U. (2009). Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. Journal of Science and Medicine in Sport, 12(1), 227–233. https://doi.org/10.1016/j.jsams.2007.10.002
Rosenblatt, B. (2014). High Performance training for Sport (D. Joyce & D. Lewindon (eds.)). Human Kinetics, Champaign.
Suna, G., Beyleroğlu, M., & Hazar, K. (2016). Comparison of aerobi̇c ,anaerobi̇c power features basketball and handball team players. Niğde Üniversitesi Beden Eğitimi Ve Spor Bilimleri Dergisi, 10(3), 379–385.
Tamer, K. (2000). Sporda fiziksel-fizyolojik performansın ölçülmesi ve değerlendirilmesi. Bağırgan Yayınevi.