Научная статья на тему 'The prediction of repeated sprint and speed endurance performance by parameters of critical velocity models in soccer'

The prediction of repeated sprint and speed endurance performance by parameters of critical velocity models in soccer Текст научной статьи по специальности «Электротехника, электронная техника, информационные технологии»

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Ключевые слова
soccer / critical velocity / repeated sprint / speed endurance / sprint tests

Аннотация научной статьи по электротехнике, электронной технике, информационным технологиям, автор научной работы — Erdal Arı, Gökhan Deliceoğlu

Purpose: The prediction of running anaerobic sprint test and 800 m performance by parameters of critical velocity was examined in this study. Material: The participants of study were consisted of thirteen amateur soccer players (n=13, age=22.69±5.29 years, weight=72.46±6.32 kg, height=176.92±6.73 cm). The 800 and 2400 m running tests were performed for determination of critical velocity and anaerobic distance capacity. The critical velocity and anaerobic distance capacity were determined by three mathematical models (linear total distance, linear velocity, non-linear two parameter model). The repeated sprint and sprint endurance ability was determined by running anaerobic sprint test and 800 m running test. The simple and multiple linear regression analysis was used for prediction of dependent variables (running anaerobic sprint test and 800 m running performance) by independent variables (critical velocity and anaerobic distance capacity) of study. The correlation between variables was determined by Pearson correlation coefficient. Results: It was found that anaerobic distance capacity was a significant predictor of running anaerobic sprint test and 800 m running performance (p<0.05). However, it was determined that critical velocity predicted significantly only time parameters of running anaerobic sprint test and 800 m test (p<0.05). Also, the parameters of 800 m test (except for average velocity) were significantly predicted by running anaerobic sprint test parameters (p<0.05). Conclusions: It may be concluded that anaerobic distance capacity is an indicator of repeated sprint and speed endurance ability in soccer and may be used in improvement of sprint endurance performance.

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Текст научной работы на тему «The prediction of repeated sprint and speed endurance performance by parameters of critical velocity models in soccer»

ORIGINAL ARTICL

The prediction of repeated sprint and speed endurance performance by parameters of critical velocity models in soccer

Erdal Ari1ABCD, Gokhan Deliceoglu2ABCD

'Ordu University, Turkey 2Gazi University, Turkey

Authors' Contribution: A - Study design; B - Data collection; C - Statistical analysis; D - Manuscript Preparation; E - Funds Collection

Abstract

Purpose: The prediction of running anaerobic sprint test and 800 m performance by parameters of critical velocity

was examined in this study.

Material: The participants of study were consisted of thirteen amateur soccer players (n=13, age=22.69±5.29 years,

weight=72.46±6.32 kg, height=176.92±6.73 cm). The 800 and 2400 m running tests were performed for determination of critical velocity and anaerobic distance capacity. The critical velocity and anaerobic distance capacity were determined by three mathematical models (linear total distance, linear velocity, non-linear two parameter model). The repeated sprint and sprint endurance ability was determined by running anaerobic sprint test and 800 m running test. The simple and multiple linear regression analysis was used for prediction of dependent variables (running anaerobic sprint test and 800 m running performance) by independent variables (critical velocity and anaerobic distance capacity) of study. The correlation between variables was determined by Pearson correlation coefficient.

Results: It was found that anaerobic distance capacity was a significant predictor of running anaerobic sprint test

and 800 m running performance (p<0.05). However, it was determined that critical velocity predicted significantly only time parameters of running anaerobic sprint test and 800 m test (p<0.05). Also, the parameters of 800 m test (except for average velocity) were significantly predicted by running anaerobic sprint test parameters (p<0.05).

Conclusions: It may be concluded that anaerobic distance capacity is an indicator of repeated sprint and speed

endurance ability in soccer and may be used in improvement of sprint endurance performance.

Keywords: soccer, critical velocity, repeated sprint, speed endurance, sprint tests.

Introduction

The oxygen uptake of muscles may be accepted as important determinant of aerobic exercise performance and critical velocity (CV) or critical power parameter is closely related to it. It is indicated that the critical power is an important indicator of oxygen uptake ability during exercise [1]. The CV was originated from critical power concept. The critical power concept was firstly defined by Monod and Scherrer [2]. The critical power tests were consisted of a series of exhausting exercises on small muscle groups with different exercise intensities [2]. The critical power (slope of linear regression graph) and anaerobic work capacity (y-intercept of linear regression graph) parameters were yielded by the linear relationship between work (parameter determined by multiplied of power and time) and exhaustion time of test [4-6]. Then, test procedure was performed on cycle ergometer with different power values [3]. The CV and anaerobic distance capacity (ADC) were determined by a series tests performed on treadmill [7, 8]. The CV and ADC parameters corresponded to critical power and anaerobic work capacity parameters in critical power test [9, 10]. The CV is maximum running velocity sustained without fatigue and ADC is distance covered with anaerobic energy sources in muscles [9-11]. The aerobic fitness level of

© Erdal Ari, Gökhan Deliceoglu, 2021 doi:10.15561/26649837.2021.0208

athletes may be evaluated with CV and ADC parameters. The CV tests have various advantages at determination of aerobic fitness. The procedure of CV tests is simple and easy for aerobic performance measurement of athletes. The CV parameter was determined with distance, velocity and time parameters of two or more runnings. Also, the CV test protocol may be performed on treadmill or field.

The aerobic exercises such as walking, runnings with low intensity are frequently performed during soccer game frequently. On the other hand, it was indicated that the explosive power activities, explosive runnings, jumps, repeated sprints are important decisive of match performance [12-15]. The repeated sprint ability may relate to performance at last parts of soccer match observed fatigue. The anaerobic activities such as sprint may have high importance on critical moments of soccer match [16]. The anaerobic activities such as repeated sprints may be related to various performance parameters as CV and ADC. If the probable effects on anaerobic activities (repeated sprint and 800 m) of CV and ADC parameters is determined, the anaerobic activities of soccer players may be organized in the light of CV and ADC parameters. Many studies are available in literature about anaerobic activities such as repeated sprint and 800 m running. However, it has been seen that few studies in literature have focused on relationship between CV test parameters and anaerobic activities such as repeated sprint and 800

m. The aim of this study was to predict repeated sprint and 800 m performances by CV and ADC parameters.

Material and Methods

Participants

The thirteen amateur soccer players (n=13, age=22.69±5.29 years, weight=72.46±6.32 kg, height=176.92±6.73 cm) participated in the study. The participants consisted of players playing in a soccer team competing regional amateur league of Turkey and performing soccer trainings 1.5 hours for five days of a week regularly. The participants were informed about study and they signed informed voluntary consent form. The study was performed according to principles of Helsinki Declaration.

Research Design

Data Collection

800 m and 2400 m Running Tests

The 800 and 2400 m running tests were performed in order to determine CV and ADC [17]. The tests were performed on synthetic grass soccer pitch at same hour of day to eliminate effects of circadian rhythm. The high intensity exercises were not performed within 24 hours before the tests. The measurements were performed at preseason preparation term. The distance of tests was marked with training cones. The 800 m test was firstly performed firstly. The players performed ten minutes warm-up and stretching exercises before the test. Each player entered the test individually. The players tried to run 800 m distance in shortest time with maximum effort (with 100% exercise intensity). The verbal encouragement was given to players by researchers during test. The test was finished when 800 m distance was covered by players. The test time was measured by wireless photocell system (Witty, Microgate, Bolzano, Italy). The test time was recorded in second unit with 0.01 precision. After three days from 800 m test, 2400 m test was performed by players. The procedure of 2400 m test was similar to the 800 m test. The test time of players was measured similarly. After the tests, the players performed warm-down exercises.

Running Anaerobic Sprint Test (RAST)

RAST was a test used for determination of repeated sprint ability [18, 19]. The RAST was consisted of six 35 m sprints performed with 10 seconds rest interval. The test was carried out on synthetic grass soccer pitch at same hour of day after three days from 2400 m running test. The 35 m test track was set on soccer pitch. The gates of wireless photocell system (Witty, Microgate, Bolzano, Italy) were placed at start and finish point of 35 m test track. The players performed warm-up and stretching exercises before test. The players performed six 35 m sprints with 10 seconds recovery interval between each sprint. The verbal motivation was given to players during test. The time of six sprints was recorded in second unit with 0.01 precision. The test parameters were determined as follow:

• Power (watt)= Weight (kg) x Distance (m) 2 ^ Time 3(sec) [20],

• Minimum power (watt): The lowest power value of

six sprints,

• Peak power (watt): The highest power value of six sprints,

• Average power (watt): The mean power value of six sprints,

• Fatigue index (watt/sec) = (Maximum power (watt)-Minimum power (watt)) ^ Total time of 6 sprints (sec) [18],

• Average test time (sec): Mean of six sprint times,

• Total test time (sec): Sum of six sprints times,

• Velocity (km/h)= Sprint distance (km) ^ Sprint time (h),

• Average velocity (km/h): Mean velocity of six sprints,

• Maximum velocity (km/h): The highest velocity of six sprints.

800 m Running Test

The 800 m test was carried out in order to determine relationship between 800 m performance and RAST, CV and ADC parameters. After three days from RAST, 800 m test was performed at same hour of day on synthetic grass soccer pitch. Test track was prepared on soccer pitch. After warm-up and stretching exercises, players started the test. Each player performed the test individually. The players ran with maximum effort and covered 800 m distance at shortest time. Test time was determined by wireless photocell system (Witty, Microgate, Bolzano, Italy). The 800 m test time was recorded in sec unit with 0.01 precision.

Determination of Critical Velocity (CV) and Anaerobic Distance Capacity (ADC)

The three mathematical models were used for determination of CV and ADC. These mathematical models consisted of linear and non-linear regression models. The time (t), distance (D) and velocity (V) at 800 and 2400 m tests were used in three mathematical models. The first mathematical model was linear total distance (Lin-TD) model. The Lin-TD model was derived from linear regression analysis between D and t parameters of 800 and 2400 m tests for each participant [20-27]:

D = ADC + CV x t

In Lin-TD model, the regression slope was CV and y-intercept of distance-time relationship (y-intercept of regression line) was ADC.

The second mathematical model was linear velocity (Lin-V) model. The Lin-V model was consisted of regression analysis between V and inverse of time (1/t) of 800 and 2400 m tests and the 1/t value were used in model to be converted to linear of hyperbolic relationship between V and t [23-25, 27-31]:

V = ADC x (1/t) + CV

In Lin-V model, the regression slope was ADC and y-intercept of V - 1/t relationship (y-intercept of regression line) was CV.

The third mathematical model was known as nonlinear model with 2-parameter (Non-2). The equation

of Lin-V model was solved for t parameter and the hyperbolic relationship between V and t was indicated by Non-2 model [27-30, 32-34]:

t = ADC / (V - CV)

Statistical Analysis

The descriptive statistics of study were presented as mean ± standard deviation and range (minimummaximum) values (table 1). The CV and ADC parameters of each player were determined by linear and nonlinear regression analysis. The Shapiro Wilk test was utilised to examine normality of data. The simple scatter graphs were examined for determination of linearity between dependent and independent variables at regression models. The multicollinearity between independent variables of regression models was examined by VIF (variance inflation factor). The regression models were designed in accordance with ideal VIF. The simple and multiple linear regression analysis was used for prediction of dependent variables (RAST and 800 m test parameters) by independent variables of study (CV and ADC). All data were analysed in the SPSS package program (IBM SPSS 22.0. Armonk, NY: IBM Corp.). The significance level at statistical analysis was performed as p<0.05.

Results

Table 1. The Descriptive Statistics of Test Parameters

There was no significant correlation between CV values and RAST parameters in Table 2 (p>0.05). On the other hand, it was found that the ADC values correlated significantly with RAST parameters (p<0.05).

According to analysis results in Table 3, it was seen that CV and ADC parameters correlated significantly with time and maximum velocity at 800 m test (p<0.05). It was found no significant correlation between average velocity at 800 m test and CV, ADC parameters (p>0.05).

According to results of correlation analysis between RAST and 800 m test parameters in Table 4, there was a significant correlation between RAST parameters and time and maximum velocity at 800 m test (p<0.05). It was found that there was no significant correlation between average velocity at 800 m test and RAST parameters (p>0.05).

In Table 5, the analysis result indicated prediction of the RAST parameters by CV and ADC parameters. The both CV and ADC predicted significantly time parameters of RAST (tverage and ttotal) (p<0.05). The velocity and power parameters of RAST (V , V , minimum, maximum

A v average7 max'

and mean power) were significantly predicted by ADC merely (p<0.05). Also, it was found that the ADC of Lin-TD and Non-2 model was a significant predictor of fatigue index value of RAST (p<0.05).

According to results in Table 6, the parameters of time and maximum velocity at 800 m test were significantly

Tests Parameters Mean ± SD Range

Lin-TD Model CV (km/h) ADC (km) 12.93 ± 0.61 0.22 ± 0.03 11.95 - 14.00 0.15 - 0.28

CV Test (n=13) Lin-V Model CV (km/h) ADC (km) 12.96 ± 0.61 0.22 ± 0.03 11.93 - 14.01 0.16 - 0.28

Non-2 Model CV (km/h) ADC (km) 12.96 ± 0.62 0.22 ±0.03 11.98 - 14.01 0.15 - 0.28

t( )(sec) (average)* ' 5.23 ± 0.27 4.87 - 5.81

t(total)(SeC) 31.40 ± 1.64 29.20 - 34.85

V(average)(km/h) 24.20 ± 1.22 21.75 - 25.95

V(max)(km/h) 26.08 ±1.35 23.38 - 27.88

Min. power|re|aHve|(watt/kg) 7.04 ± 1.06 5.27 - 8.92

RAST Test (n=13) Min. power (watt) 509.89 ± 92.50 393.57 - 713.31

Max. P0Wer(re|ative)(Watt/kg) 10.94 ± 1.66 7.82 - 13.27

Max. power (watt) 791.76 ± 135.62 589.85 - 1061.23

Mean power(re|ative)(watt/kg) 8.82 ± 1.29 6.36 - 10.78

Mean power (watt) 638.38 ± 108.38 479.26 - 862.42

Fatigue index (watt/sec) 9.05 ± 2.42 5.53 - 14.12

t (sec) 155.00 ± 9.35 145.00 - 179.00

800 m running test (n=13) V(average)(km/h) (average) 18.30 ± 1.43 15.00 - 20.00

Vmax)(km/h) 24.50 ± 2.33 19.60 - 27.70

Note. Lin-TD: Linear Total Distance Model, Lin-V:Linear Velocity Model, Non-2: Nonlinear 2-parameter Model, CV: Critical velocity, ADC: Anaerobic Distance Capacity, RAST: Repeated Anaerobic Sprint Test, t: test time, t(average|=average test time, t = total test time, V: velocity, V(average|=average velocity, V(max)=maximum velocity, min. power(relat.ve|=relative minimum power of six 35 m. runnings, min.power=salt minimum power of six 35 m. runnings, max.power(relat.ve=relative maximum power of six 35 m. runnings, max. power=salt maximum power of six 35 m. runnings, mean power(relat.ve|=relative mean power of six 35 m. runnings, mean power=salt mean power of six 35 m. runnings, fatigue index=fatigue index of six 35 m. runnings.

2021

Table 2. Pearson Correlation Analysis Results Between CV, ADC and RAST Parameters

RAST

Model Parameters Correlation t(average) (sec) t (total) (sec) V(average) (km/h) V(max) (km/h) Min. power(re|ative) (watt) Max. power(relative) (watt) Mean power(re|ative) (watt) Fatigue index (watt/ sec)

Lin-TD CV (km/h) r p -0.500 0.082 -0.501 0.081 0.480 0.097 0.411 0.163 0.438 0.135 0.384 0.195 0.457 0.116 0.135 0.661

ADC (km) r p -0.635 0.020* -0.637 0.019* 0.629 0.021* 0.671 0.012* 0.594 0.032* 0.666 0.013* 0.621 0.024* 0.593 0.033*

Lin-V CV (km/h) r p -0.504 0.079 -0.505 0.078 0.485 0.093 0.433 0.140 0.424 0.149 0.407 0.167 0.463 0.111 0.199 0.515

ADC (km) r p -0.696 0.008* -0.697 0.008* 0.693 0.009* 0.679 0.011* 0.721 0.005* 0.675 0.011* 0.688 0.009* 0.506 0.078

Non-2 CV (km/h) r p -0.504 0.079 -0.505 0.078 0.485 0.093 0.414 0.160 0.446 0.127 0.388 0.191 0.462 0.112 0.137 0.656

ADC (km) r p -0.633 0.020* -0.635 0.020* 0.626 0.022* 0.668 0.013* 0.596 0.032* 0.662 0.014* 0.618 0.024* 0.596 0.031*

Note. * p<0.05

Table 3. Pearson Correlation Analysis Results Between CV, ADC and 800 m Test Parameters

800 m test

Model Parameters Correlation t

(sec)

V(average)

(km/h)

V )

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max)

(km/h)

Lin-TD

CV (km/h)

-0.452 0.121

-0.064 0.837

0.555 0.049*

ADC (km)

-0.770 0.002*

0.510 0.075

0.681 0.010*

Lin-V

CV (km/h)

-0.475 0.101

-0.071 0.817

0.565 0.044*

ADC (km)

-0.782 0.002*

0.499 0.082

0.726 0.005*

Non-2

CV (km/h)

-0.451 0.122

-0.074 0.810

0.560 0.047*

ADC (km)

-0.762 0.002*

0.493 0.087

0.686 0.010*

Note. *p<0.05

predicted by both CV and ADC (p<0.05). Also, it was seen that CV and ADC were not significant predictors of average velocity at 800 m test (p>0.05).

The results in Table 7 indicated that the RAST parameters were significant predictors of time and maximum velocity at 800 m test (p<0.05). The average velocity at 800 m test was not significantly predicted by RAST parameters (p>0.05). On the other hand, it was found that the fatigue index parameter of RAST predicted significantly time of 800 m test merely (p<0.05).

Discussion

The CV and ADC parameters are products of linear relationship between distance and time of exercise. Also, these parameters are determined from linear relationship between velocity and time-1 or other nonlinear mathematical models. The CV and ADC were frequently investigated in various studies. It was found that CV correlated with maximal aerobic velocity and maximum oxygen uptake [35, 36]. The significant and high correlation (r = 0.80-0.93 range, p<0.01) between CV of five mathematical models and one hour running performance indicated relationship between CV and

PEDAGOGY

of Physical Culture

Table 4. Pearson Correlation Analysis Results Between RAST and 800 m Test Parameters

800 m test

RAST Parameters Correlation t(sec) V(average) (km/h) V(max) (km/h)

^(average) (Sec) r P 0.806 0.001* -0.341 0.254 -0.746 0.003*

t (total) (SeC) r 0.807 -0.342 -0.745

p 0.001* 0.253 0.003*

V(average) (km/h) r -0.799 0.335 0.725

p 0.001* 0.263 0.005*

V(max) (km/h) r -0.815 0.438 0.698

p 0.001* 0.135 0.008*

Min. POW^eiative) (Watt) r p -0.698 0.008* 0.197 0.519 0.762 0.002*

MaX- POWer(relative) (Watt) r p -0.804 0.001* 0.434 0.139 0.678 0.011*

Mean POWer(rebtive) (Watt) r p -0.790 0.001* 0.330 0.271 0.705 0.007*

Fatigue index (Watt/sec) r p -0.661 0.014* 0.420 0.153 0.387 0.191

Note. *p<0.05

Table 5. The Regression Analysis of Effect on RAST Parameters of CV and ADC as Predictor Variables

Dependent Variable

Model

Predictor Variables

B

Standard Error

ß

p

R

R2

Standard Error of Estimate

^average)(SeC)

Model-1

Constant

Lin-TD-CV

Lin-TD-ADC

8.895 -0.205 -4.543

1.146 0.086 1.462

-0.464 -0.608

7.764 -2.371 -3.107

0.000* 0.039* 0.011*

0.786 0.618 0.184

Model-2

Constant Lin-V-CV Lin-V-ADC

8.904 -0.192 -5.328

1.062 0.081 1.496

-0.435 -0.649

8.385 -2.386 -3.561

0.000* 0.038* 0.005*

0.819 0.671 0.171

Model-3

Constant Non-2-CV Non-2-ADC

8.918 -0.205 -4.611

1.145 0.086 1.494

-0.467 -0.604

7.787 -2.384 -3.086

0.000* 0.038* 0.012*

0.786 0.618 0.184

t(total)(SeC)

Model-1

Constant

Lin-TD-CV

Lin-TD-ADC

53.472 -1.235 -27.414

6.869 0.518 8.765

-0.465 -0.610

7.785 -2.385 -3.128

0.000* 0.038* 0.011*

0.788 0.621 1.107

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Model-2

Constant Lin-V-CV Lin-V-ADC

53.527 -1.160 -32.149

6.360 0.483 8.961

-0.436 -0.651

8.417 -2.403 -3.588

0.000* 0.037* 0.005*

0.821 0.674 1.027

Model-3

Constant Non-2-CV Non-2-ADC

53.607 -1.237 -27.818

6.867 0.516 8.961

-0.468 -0.606

7.807 -2.397 -3.104

0.000* 0.037* 0.011*

0.788 0.621 1.108

V(average)

(km/h)

Model-1

Constant

Lin-TD-CV

Lin-TD-ADC

8.308 0.881 20.204

5.315 0.400 6.783

0.445 0.602

1.563 2.201 2.979

0.149 0.052 0.014*

0.770 0.592 0.857

Model-2

Constant Lin-V-CV Lin-V-ADC

8.232 0.826 23.880

4.911 0.373 6.920

0.416 0.648

1.676 2.216 3.451

0.125 0.051 0.006*

0.807 0.651 0.793

Model-3

Constant Non-2-CV Non-2-ADC

8.200 0.884 20.494

5.313 0.399 6.934

0.448 0.598

1.543 2.214 2.956

0.154 0.051 0.014*

0.769 0.592 0.857

V(max)(km/h)

Model-1

Constant

Lin-TD-CV

Lin-TD-ADC

10.150

0.817

24.112

5.918 0.446 7.551

0.372 0.649

1.715 1.832 3.193

0.117 0.097 0.010*

0.767 0.588 0.954

Model-2

Constant Lin-V-CV Lin-V-ADC

9.934 0.801 26.126

5.879 0.446 8.283

0.365 0.640

1.690 1.796 3.154

0.122 0.103 0.010*

0.770 0.593 0.949

Model-3

Constant Non-2-CV Non-2-ADC

10.060 0.818 24.464

5.932 0.446 7.741

0.374 0.645

1.696 1.834 3.160

0.121 0.096 0.010*

0.765 0.585 0.957

t

Table 5 (continued)

Dependent Variable Model Predictor Variables B Standard Error ß t p R R2 Standard Error of Estimate

Constant -5.692 5.047 -1.128 0.286

Model-1 Lin-TD-CV 0.698 0.380 0.404 1.835 0.096 0.718 0.516 0.813

Lin-TD-ADC 16.677 6.440 0.571 2.590 0.027*

Min. Constant -5.662 4.335 -1.306 0.221

P0W6r(re|ative) Model-2 Lin-V-CV 0.606 0.329 0.351 1.843 0.095 0.801 0.642 0.700

(watt/kg) Lin-V-ADC 21.951 6.109 0.684 3.593 0.005*

Constant -5.885 5.006 -1.176 0.267

Model-3 Non-2-CV 0.706 0.376 0.411 1.877 0.090 0.723 0.523 0.808

Non-2-ADC 17.022 6.533 0.570 2.606 0.026*

Constant -7.583 7.459 -1.017 0.333

Model-1 Lin-TD-CV 0.928 0.562 0.346 1.652 0.130 0.750 0.562 1.202

Lin-TD-ADC 29.310 9.518 0.645 3.080 0.012*

Max. Constant -7.904 7.383 -1.071 0.310

P0Wer(relative) Model-2 Lin-V-CV 0.911 0.560 0.339 1.627 0.135 0.755 0.570 1.192

(watt/kg) Lin-V-ADC 31.895 10.403 0.639 3.066 0.012*

Constant -7.706 7.473 -1.031 0.327

Model-3 Non-2-CV 0.931 0.562 0.348 1.657 0.129 0.748 0.560 1.206

Non-2-ADC 29.740 9.753 0.641 3.049 0.012*

Constant -7.291 5.811 -1.255 0.238

Model-1 Lin-TD-CV 0.883 0.438 0.422 2.016 0.071 0.750 0.563 0.937

Lin-TD-ADC 21.101 7.416 0.596 2.845 0.017*

Mean Constant -7.413 5.362 -1.383 0.197

P0Wer(relative) Model-2 Lin-V-CV 0.825 0.407 0.394 2.027 0.070 0.792 0.627 0.866

(watt/kg) Lin-V-ADC 25.131 7.555 0.646 3.326 0.008*

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Constant -7.414 5.809 -1.276 0.231

Model-3 Non-2-CV 0.887 0.437 0.426 2.031 0.070 0.750 0.562 0.938

Non-2-ADC 21.396 7.581 0.592 2.822 0.018*

Constant -4.682 13.152 -0.356 0.729

Model-1 Lin-TD-CV 0.392 0.991 0.100 0.396 0.701 0.602 0.362 2.121

Lin-TD-ADC 38.975 16.783 0.588 2.322 0.043*

Fatigue Constant -6.274 13.978 -0.449 0.663

index (watt/ Model-2 Lin-V-CV 0.574 1.061 0.146 0.542 0.600 0.527 0.277 2.257

sec) Lin-V-ADC 35.738 19.695 0.491 1.815 0.100

Constant -4.883 13.100 -0.373 0.717

Model-3 Non-2-CV 0.392 0.985 0.101 0.398 0.699 0.605 0.366 2.115

Non-2-ADC 39.983 17.096 0.590 2.339 0.041*

Note. *p<0.05

aerobic exercise performance [37]. It was reported that CV was significantly correlated with both maximal lactate steady state and onset of blood lactate [38]. Also, performance at 3000 m running was closely related to CV parameter [39]. These findings support that CV is an aerobic performance indicator. In our study, it was determined that CV parameter of three models correlated with maximum velocity at 800 m test (p<0.05) (table 3). However, it was seen that the significance level of correlation was not high (0.44-0.49 range of p value). In this context, it may be said that the 800 m test is an anaerobic test dominantly and the contribution on test performance of aerobic fitness is low. Simoes et al. [40] found a significant correlation between CV and 500 m,

3 and 10 km running velocity and this result showed parallelism to correlation between CV and maximum velocity at 800 m test in our study. Particularly, the findings of our study were similar to correlation between CV and 500 m running velocity in mentioned study. This relationship in mentioned study is remarkable although the anaerobic contribution to 500 m performance is higher than 800 m test. Similarly, Bosquet et al. [41] reported that the 40-62 % of variance in velocity at 800 m running was explained by CV estimates of five mathematical models. The CV and ADC estimates of three models used in our study predicted significantly maximum velocity at 800 m test and both parameters explained 73-76.8 % of total variance in mentioned variable (table 6). It was seen that

Table 6. The Regression Analysis of Effect on 800 m Test Parameters of CV and ADC as Predictor Variables

Dependent Variable

Model

Predictor Variables

Standart Error

P

Standard Error of Estimate

Model-1

Constant

Lin-TD-CV

Lin-TD-ADC

277.148 -6.160 -190.976

31.199

2.351

39.813

-0.408 -0.746

8.883 -2.621 -4.797

0.000* 0.026* 0.001*

0.871 0.759 5.032

t (sec)

Model-2

Constant Lin-V-CV Lin-V-ADC

278.619 -6.000 -207.828

30.667

2.327

43.209

-0.396 -0.739

9.085

-2.579

-4.810

0.000* 0.027* 0.001*

0.875 0.766 4.953

Model-3

Constant Non-2-CV Non-2-ADC

276.729 -6.101 -192.589

32.118

2.414

41.915

-0.405 -0.737

8.616 -2.527 -4.595

0.000* 0.030* 0.001*

0.862 0.744 5.186

Model-1

Constant

Lin-TD-CV

Lin-TD-ADC

16.620 -0.218 20.290

8.354 0.629 10.660

-0.094 0.516

1.990 -0.347 1.903

0.075 0.736 0.086

0.519 0.269 1.347

V(average)

(km/h)

Model-2

Constant Lin-V-CV Lin-V-ADC

17.214 -0.293 22.165

8.363 0.635 11.783

-0.126 0.513

2.058 -0.462 1.881

0.067 0.654 0.089

0.515 0.265 1.350

Model-3

Constant Non-2-CV Non-2-ADC

16.997 -0.242 20.093

8.426 0.633 10.996

-0.105 0.500

2.017 -0.383 1.827

0.071 0.710 0.098

0.504 0.254 1.360

Model-1

Constant

Lin-TD-CV

Lin-TD-ADC

-9.908

1.947

41.474

8.228 0.620 10.500

0.517 0.650

-1.204

3.141

3.950

0.256

0.011*

0.003*

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0.854 0.730 1.327

V(max) (max)

(km/h)

Model-2

Constant Lin-V-CV Lin-V-ADC

-10.023 1.861 47.182

7.615 0.578 10.729

0.493 0.674

-1.316

3.221

4.398

0.217

0.009*

0.001*

0.876 0.768 1.229

Model-3

Constant Non-2-CV Non-2-ADC

-10.215

1.950

42.619

8.060 0.606 10.519

0.520 0.654

-1.267 3.219 4.052

0.234

0.009*

0.002*

0.860 0.740 1.301

Note. *p<0.05

the finding of our study was similar to results of Bosquet et al. [41].

It was found that the time and distance values of test performed with 120 % of maximal oxygen uptake velocity were correlated with curvature constant (W' parameter corresponded to ADC) of Lin-TD and Lin-V models in professional young soccer players [42]. The ADC parameter was a significant predictor of the most parameters of RAST (table 5) and 800 m tests (table 6) in our study and this finding was in agreement with results of mentioned study. These findings show that the effect of ADC is too distinct in anaerobic exercises. Beck et al. [43] found high and significant correlation (r = 0.680.83 range, p<0.05) between RAST power and maximum velocity parameters and times of short distance anaerobic running (50, m running) [43]. Also, the related study reported a significant correlation between mean power at RAST and time of 300 m running. However, it was determined that the correlation between ADC (constant curvature (W') in related study) and times of 50, 100 and 300 m runnings was not significant in mentioned study. It was found a significant relationship between 800 m performance and ADC in our study (table 3). The discrepancy of findings may be arisen from difference of

running distances (800 m v 50, 100 and 300 m) in these studies. It may be indicated that 800 m performance may be highly affected by ADC.

Chatzakis et al. [44] reported that there was a significant correlation between RAST minimum and mean power parameters and 300 and 1000 m running time in children and young adolescents. In mentioned study, it was reported that maximum power parameters of RAST were only correlated with 300 m running time. It was found that RAST parameters were significant predictors of time and maximum velocity at 800 m test in our study (table 7). The 800 m running test is dominantly anaerobic test. The parameters of RAST involving repeated explosive sprints are indicators of anaerobic exercises such as 800 and 1000 m running. However, maximum power parameter of RAST is the highest power in exercise. Therefore, it may be said that maximum power parameter of RAST may be more dominant in exercises requiring high contribution of anaerobic energy system such as 300 m running. Zagatto et al. [45] researched relationship RAST parameters and results of Hoff test (a soccer-specific test developed by Hoff et al. [46] for anaerobic fitness level in soccer players) in professional soccer players and found no significant correlation between test results. It was

ß

2

B

R

R

t

Table 7. The Regression Analysis of Effect on 800 m Test Parameters of RAST Parameters as Predictor Variables

Dependent Variable Model Predictor Variables B Standard Error ß t p R2 Standard Error of Estimate

Mode -1 Constant t(average) 10.394 27.613 32.047 6.112 0.806 0.324 4.518 0.752 0.001* 0.650 5.781

Mode -2 Constant t(total) 10.753 4.593 31.875 1.014 0.807 0.337 4.531 0.742 0.001* 0.651 5.770

Mode -3 Constant 302.458 -6.093 33.557 1.385 -0.799 9.013 -4.400 0.000* 0.001* 0.638 5.881

t (sec) Mode -4 Constant V(max) 301.431 -5.615 31.417 1.203 -0.815 9.594 -4.667 0.000* 0.001* 0.664 5.659

Mode -5 Constant Mm. Power,rpati„e, 198.001 -6.108 13.460 1.892 -0.698 14.711 -3.229 0.000* 0.008* 0.487 7.000

Mode -6 Constant Max. Power,rpl3ti„p, 204.570 -4.530 11.171 1.010 -0.804 18.312 -4.484 0.000* 0.001* 0.646 5.809

Mode -7 Constant Mean Power,retati„e, 205.360 -5.709 11.902 1.336 -0.790 17.254 -4.273 0.000* 0.001* 0.624 5.990

Mode -8 Constant Fatigue index 178.091 -2.549 8.165 0.873 -0.661 21.811 -2.920 0.000* 0.014* 0.437 7.332

Mode -1 Constant t(average) 27.722 -1.798 7.828 1.493 -0.341 3.542 -1.204 0.005* 0.254 0.116 1.412

Mode -2 Constant t(total) 27.710 -0.299 7.798 0.248 -0.342 3.553 -1.207 0.005* 0.253 0.117 1.411

Mode -3 Constant 8.790 0.393 8.076 0.333 0.335 1.088 1.180 0.300 0.263 0.112 1.415

V(average) (km/h) Mode -4 Constant V(max) 6.220 0.464 7.499 0.287 0.438 0.829 1.614 0.425 0.135 0.191 1.350

Mode -5 Constant Min. PoWer1relati„e, 16.439 0.265 2.832 0.398 0.197 5.805 0.667 0.000* 0.519 0.039 1.472

Mode -6 Constant Max. P°Wer,„la«,,,, 14.195 0.376 2.603 0.235 0.434 5.453 1.597 0.000* 0.139 0.188 1.353

Mode -7 Constant Mean PoWer,re|ati„e, 15.074 0.367 2.818 0.316 0.330 5.349 1.159 0.000* 0.271 0.109 1.418

Mode -8 Constant Fatigue index 16.049 0.249 1.518 0.162 0.420 10.573 1.537 0.000* 0.153 0.177 1.363

Mode -1 Constant t(average) 57.869 -6.372 8.979 1.712 -0.746 6.445 -3.721 0.000* 0.003* 0.557 1.619

Mode -2 Constant t(total) 57.696 -1.057 8.967 0.285 -0.745 6.434 -3.707 0.000* 0.003* 0.555 1.623

Mode -3 Constant -8.873 1.379 9.564 0.395 0.725 -0.928 3.494 0.373 0.005* 0.526 1.676

V(max) (km/h) Mode -4 Constant V(max) -6.765 1.199 9.672 0.370 0.698 -0.699 3.237 0.499 0.008* 0.488 1.742

Mode -5 Constant Min. PoWer,re|ati„e, 12.801 1.662 3.033 0.426 0.762 4.220 3.898 0.001* 0.002* 0.580 1.577

Mode -6 Constant Max. PoWer,re«,,,, 14.077 0.952 3.439 0.311 0.678 4.093 3.063 0.002* 0.011* 0.460 1.788

Mode -7 Constant Mean PoWer,re|ati,,e, 13.304 1.269 3.431 0.385 0.705 3.877 3.295 0.003* 0.007* 0.497 1.727

Mode -8 Constant Fatigue index 21.130 0.372 2.500 0.267 0.387 8.453 1.392 0.000* 0.191 0.150 2.244

Note. *p<0.05

indicated that the Hoff test was used for measurement of aerobic fitness level with soccer specific exercises (dribbling and activities with ball) [46]. The RAST power parameters were not significantly correlated with CV in our study (table 2) and this finding sustained the results of Zagatto et al. [45].

It was indicated that 20 m sprint time was a powerful predictor of total time and sprint decrement score at RAST test in national level soccer players [47]. Similarly, the ADC parameter was a significant predictor of RAST parameters in our study (table 5). The sprint time decrement index (score developed by Glaister et al. [48]) of RAST had significant correlation with maximum oxygen uptake (VO2max) in low and high level VO2max groups (positive correlation for low level VO2max group, negative correlation for high level VO2max group) but no significant correlation was found for medium level VO2max group [49]. There was no correlation between CV and fatigue index of RAST in our study (table 2) and this finding drew parallelism with the correlation result of medium level VO2max group in mentioned study. The decrement index used in study of Alizadeh et al. [49] was different from fatigue index in our study and this difference might cause discrepancy in results of two studies. Keir et al. [50] indicated that contribution of aerobic metabolism in RAST was higher than Wingate test although there was no significant difference between VO2max values of two tests. There was no comparison between tests in our study and it was not seen a significant correlation between CV and RAST parameters (table 2). The mentioned study has focused on comparison of RAST and Wingate tests and interpreted aerobic metabolism effects on tests by VO2max graphs. Our study was based on prediction of RASt parameters by CV and ADC and it was seen that the CV predicted significantly time parameters (t(average) and t(total)) of RAST (table 5). The only CV effect onüme parameters of RAST has sustained findings of Keir et al. [50] emphasizing aerobic contribution in RAST.

In study performed on professional soccer players, it was found that the correlation between RAST parameters and soccer match performance (total distance, maximum speed, high intensity and sprint count during soccer match) was not significant statistically [51]. Although the parameters determined in mentioned study were anaerobic activities except for total distance, the relationship between RAST and match parameters was not found by Redkva et al. [51]. Loures et al. [52] reported

that the anaerobic work capacity (equivalent of ADC) of soccer players under seventeen age did not correlate with power and fatigue index parameters of RAST but velocity parameters (mean and maximum velocity) had significant correlation with ADC. Similarly, it was determined a non-significant correlation between RAST and anaerobic running capacity (equivalent of ADC) in male futsal players [53]. Unlike finding of mentioned study, there was a significant correlation between all the RAST parameters and ADC (as an anaerobic parameter) in our study (table 2).

Conclusion

The CV and ADC parameters are yielded by linear and non-linear mathematical models. The CV is defined as aerobic fitness indices although ADC is an indicator of distance covered with anaerobic energy sources. The repeated sprint and sprint endurance ability is critical for performance in soccer involving repeated sprints. Therefore, RAST and 800 m performance that are indirect indicators of anaerobic performance is tried to predict by CV and ADC parameters in this study. It was found that ADC was a strong indicator of RAST performance. Also, it was seen that 800 m performance might be predicted by ADC and RAST parameters. CV parameter was not a significant predictor of RAST and 800 m performance except for time parameters of tests. It may be concluded that ADC may be used as an indicator of repeated sprint and sprint endurance performance while CV is a determinant of aerobic endurance performance. The CV and ADC parameters may be easily determined by simple methods without expensive measurement equipment and the performance of soccer players may be tracked by these parameters.

Acknowledgements

This study was written by abridging Erdal Ari, Gokhan Deliceoglu. No grants or financial aids were taken in this Project.

Financial support

There is no financial support.

Conflict of interest

The authors report no conflict of interest.

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2021

Information about the authors:

Erdal Ari; (Corresponding author); https://orcid.org/0000-0003-1348-7930; arierdal@hotmail.com; Physical Education and Sports School, Ordu University; Ordu, Turkey.

Gokhan Deliceoglu; https://orcid.org/0000-0003-2459-9209; deliceoglugokhan@hotmail.com; Faculty of Sports Sciences, Gazi University; Gazi, Turkey.;

Cite this article as:

Ari E, Deliceoglu G. The prediction of repeated sprint and speed endurance performance by parameters of critical velocity models in soccer. Pedagogy of Physical Culture and Sports, 2021;25(2):132-143. https://doi.org/10.15561/26649837.2021.0208

This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/deed.en).

Received: 02.10.2020

Accepted: 08.11.2020; Published: 30.04.2021

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