Paper : Soccer

Regression Equations for Estimating vVO2max andvOBLA from 3200-M Run Time in JapaneseMale Junior Youth Soccer Players Using Longitudinal Data
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Kentaro Chuman*,** and Takahiko Nishijima****JUBILO CO., LTD.
2500 Shingai, Iwata, Shizuoka 438-0025 Japanchumank@jubilo.com**College of Life and Health Sciences, Chubu University***Institute of Health and Sport Sciences, University of Tsukuba[Received March 24, 2023; Accepted July 4, 2023]

This study aimed to develop regression equations to estimate running velocity of maximal oxygen
uptake (vVO2max) and onset of blood lactate accumulation (vOBLA) from a 3200-m run time using
a soccer pitch. Forty-four Japanese male junior youth soccer players from a team in the Japan
Professional Football League First Division (J1 League) participated in this experiment. In thelaboratory, vVO2max and vOBLA were measured using an incremental load test, while the 3200m run time was measured on the soccer pitch. Structural equation modeling was applied to analyze
the sequential causal structure and latent growth curve models of aerobic running capability
development during soccer training. A regression equation (1) utilized the 3200-m run time (s) as
the independent variable (x) and vVO2max (km/h) as the dependent variable (y), resulting in theequation y=-0.02461x+36.60 (r2=0.564). The regression equation (2) utilized the 3200-m run time(s) as the independent variable (x) and vOBLA (km/h) as the dependent variable (y), resulting in
the equation y=-0.01833x+29.30 (r2=0.541). The coefficient of determination (r2) for the regression
equations exceeded 0.5. We successfully developed to estimate vVO2max and vOBLA from a 3200-m
run time in Japanese male junior youth soccer players.
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Keywords: structural equation modeling, aerobic running velocity, maximal oxygen uptake, onset ofblood lactate accumulation[Football Science Vol.20, 38-46, 2023]

1. Introduction
High-intensity running is repeatedly performed
during soccer matches (Bangsbo, 1994). In
professional soccer players, the higher the level ofcompetition, the greater the distance covered in highintensity running during a match (Mohr et al., 2003).
The importance of the ability to repeatedly perform
high-intensity running (intermittent endurance) has
increased over the years (Bush et al., 2015). The YoYo intermittent recovery test was developed to assessintermittent endurance in soccer players (Bangsbo etal., 2008). Aerobic capability influences Yo-Yointermittent recovery test performance or intermittent
endurance in professional soccer players(Ingebrigtsen et al., 2012). Therefore, it has been
pointed out that a high level of aerobic runningcapability should be acquired during junior high38

school when aerobic running capability is primarily
developed (Chuman and Hoshikawa, 2015).
The aerobic running capability index includes
running velocity, which corresponds to maximal
oxygen uptake (vV O 2max), and running velocity,which corresponds to the onset of blood lactate
accumulation (vOBLA). vVO2max is the maximal
index of aerobic running capability, and vOBLA is
the submaximal index of aerobic running capability;
both are effective values that can be used directly fortraining (Chuman, 2016, 2021). vV O 2 max andvOBLA in Japanese male junior youth soccer players
show significant development from U-13 to U-15 andcan reach the same level as professional soccer
players at U-15 or U-16 (Chuman, 2016, 2021).
Therefore, regular measurements of vVO2max andvOBLA and systematic training based on these data
are necessary for male junior youth soccer players.
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Regression Equation Estimating Aerobic Running Velocity from 3200-M Run Time

However, regularly incorporating vV O 2max andvOBLA measurements into training plans is difficult
because of the time and cost required to perform thesemeasurements in the laboratory (Chuman et al.,2015). Therefore, a low-cost field test that cansimultaneously measure the vVO2max and vOBLA of
multiple players is required for male junior youth
soccer players.
The 12-minute run test is used as a field test to
determine aerobic running capability because of its
strong relationship with V O 2 max (Kenneth andCooper, 1968). In the 12-minute run test, it isnecessary to mark every 10 m on the track todetermine the running distance, and it is necessary tohave a measurer for each participant. In soccer
training, labor-intensive preparation and measurement
methods are difficult to sustain (Chuman et al., 2015).
Therefore, Chuman et al. (2015) proposed a 3200-m
run test using a soccer pitch to measure aerobic
running capability. In this 3200-m run test, thepreparation is completed only by placing eight cones
on a line 11.1 m away from the four corners of a
105 m × 68 m soccer pitch, and a single measurer canmeasure many players at once. In male junior youth
soccer players, the 3200-m run time showed asignificant correlation with the results of the
12-minute run test, as well as with vVO2max andvOBLA (Chuman et al., 2015). Therefore, the 3200m run test using a soccer pitch can be used to measure
aerobic running capability.
However, the relationship between intra-individual
variability in 3200-m run time and that in vVO2max
or vOBLA has not been clarified using longitudinal
data in male junior youth soccer players. To enablesystematic aerobic training using the 3200-m run test
in male junior youth soccer players, it is necessary to
clarify these relationships and develop regression
equations to estimate vVO2max or vOBLA from the3200-m run time.
Based on the above, two hypotheses were
formulated regarding the development of aerobic
running capability using longitudinal data fromJapanese male junior youth soccer players and therelationship between intra-individual variability in the3200-m run time and that in vVO2max or vOBLA.
The first hypothesis was that aerobic runningcapability, as determined by the 3200-m run time,vVO2max, and vOBLA, affects U-13 to U-14 andU-14 to U-15 in male junior soccer players (thesequential causal structure model). The second
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hypothesis was that a shorter 3200-m run time would
affect the development of vVO2max or vOBLA. Thatis, the 3200-m run time intercepts from U-13 to U-15
affect the vVO2max or vOBLA intercepts from U-13
to U-15, and the 3200-m run time slopes from U-13
to U-15 affect the vVO2max or vOBLA slopes fromU-13 to U-15 (the latent growth curve model). To
verify such complex relationships among latent andobserved variables by presenting them in an easy-tounderstand model using a path diagram, we can use
structural equation modeling (SEM), a statisticalanalysis method (Toyoda, 1992).
The purpose of this study was to test the sequential
causal structure and latent growth curve models of
aerobic running capability development by applying
SEM to aerobic running training in Japanese malejunior youth soccer players and to develop regression
equations to estimate vV O 2 max or vOBLA, an
indicator of aerobic running capability, from a
3200-m run time.
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2. Methods
2.1 Study design
This study collected physical fitness data(anthropometric measurements, incremental load test
(ILT), and 3200-m run test) collected in April of
U-13, U-14, and U-15 from 44 Japanese junior youth
soccer players. In cases where participants could notparticipate in the measurement due to school events,
injuries, or poor health, the measurement was
conducted within one month before or after themeasurement (March to May). Additionally, datafrom participants for whom measurements could not
be performed during these periods were excluded.
This study first conducted a one-way repeatedmeasures analysis of variance (ANOVA) to examine
the longitudinal effects of two years of soccer training
from U-13 to U-15 on the development of aerobic
running capability (3200-m run time, vV O 2max,
vOBLA). The within-participant factors were threelevels of measurement time (U-13, U-14, and U-15).
Next, the sequential causal structure and latent
growth curve models were tested using SEM to
examine the relationship between intra-individual
variation in the development of aerobic runningcapability (3200-m run time, vVO2max, and vOBLA)
in male junior youth soccer players. When thesehypothetical models were proved and a significant
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Chuman, K. et al.

correlation between the 3200-m run time andvV O 2max or vOBLA was confirmed, regression
equations were developed to estimate vVO2max orvOBLA from the 3200-m run time.
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2.2. Participants
The participants were 44 male Japanese junior highschool students belonging to the junior youths of theJapan Professional Football League First Division (J1League). All participants were field players.
Participants U-13, U-14, and U-15 were first-,
second-, and third-year junior high school students,
respectively. The characteristics of the study
participants are shown in Table 1.
The participants were trained for 90 minutes a day,five or six times a week. Physical and technicaltraining was conducted on Tuesdays, technical andtactical training on Wednesdays to Fridays, andmatches or technical and tactical training on
Saturdays and Sundays. Physical training is aimed at
improving aerobic capacity and agility. U-13 had restdays on Mondays and Thursdays, and U-14 and U-15
had rest days only on Mondays.
In this study, data obtained by the clubs as part ofdaily training were used secondarily by the
researchers with the consent of club representatives.
This study was approved by the Research Ethics
Committee of the Institute of Health and Sport
Sciences, University of Tsukuba (No.21-311).

2.3. Morphometric measurements
The morphometric parameters were body height
and weight. Body height was measured in incrementsof 0.1 cm, and body weight was measured inincrements of 0.1 kg.

2.4. Incremental load test (ILT)
Following the method described by Hoshikawa etal. (2000), an ILT on a treadmill was used as alaboratory test to measure aerobic running capability.
The ILT protocol consisted of four to six repetitionsof 3 min submaximal running and 1 min rest. The
heart rate, oxygen uptake, and blood lactate
concentration were measured at each running velocity.
The submaximal running velocities were 10.8, 12.0,
13.2, 14.4, 15.6, and 16.8 km/h. Oxygen uptake wasmeasured using MetaMax (Cortex Biophysic GmbH),
and blood lactate concentration was measured using
Lactate Pro (Arkray, Inc.). Blood samples werecollected from the fingertips within 20 s of running at
each velocity. Two or more blood samples werecollected, and the mean blood lactate concentration ateach running velocity was calculated. Outliers were
excluded from the three blood draws, and the average
blood lactate concentration was calculated.
After submaximal running, participants rested for 2
min and then ran for 3 min at 16.8 km/h (Hoshikawa
et al., 2000). Thereafter, the incline was increased by
1% every minute until exhaustion was achieved. The
vVO2max, which corresponds to oxygen uptake at
exhaustion, was estimated (Morgan et al., 1989). The
vOBLA, corresponding to a blood lactate
concentration of 4 mmol/L, was also estimated.
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2.5. 3200-m run test
Following the method of Chuman et al. (2015), the3200-m run time was measured on a soccer pitch as afield test to measure aerobic running capability. We
used a 105 m × 68 m artificial turf soccer pitch, which
was usually used for soccer training, and cut four
corners of the pitch by 11.1 m each to form anoctagonal shape with a circumference of 320 m.
Participants started at the signal of the measurer andTable 1 Characteristics of the participants (n=44)

Variables

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U-13

U-14

U-15

Mean ± SD

Mean ± SD

Mean ± SD

Age (yr)12.8 ± 0.2

13.8 ± 0.2

14.8 ± 0.2

Body height (cm)157.8 ± 8.2

164.1 ± 7.2

168.6 ± 6.1

Body weight (kg)46.1 ± 7.9

51.7 ± 8.3

56.6 ± 7.6
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Regression Equation Estimating Aerobic Running Velocity from 3200-M Run Time

did ten laps around the 320-m running track. The
3200-m run time was measured with a stopwatch andconducted during the training session at 18:45 on
Tuesday. Participants performed with soccer shoes.

2.6. Statistical analysis
A one-way repeated measures ANOVA was
conducted to examine the effects of two years ofsoccer training from U-13 to U-15 on the 3200-m runtime, vVO2max, and vOBLA. The measurement times
were U-13, U-14, and U-15. When Mauchly’s
sphericity test was significant, the GreenhouseGeisser test was used to test the effect of the time of
measurement. A multiple comparison test using the
Bonferroni method was conducted as a post hoc test.
The maximum likelihood estimation method, themost widely used method in applied SEM research(Toyoda, 1992), was used for parameter estimation in
the SEM. The parameters were restrained to ensure
discrimination. The restraint conditions were as
follows: 1) the path coefficient from the error variable
to the endogenous variable was fixed at 1; 2) the pathcoefficient from latent variables that were endogenous
to observed variables was fixed at 1; 3) the pathcoefficient from latent variables that were exogenous
to observed variables was fixed at 1; and 4) the
variance of the error variable was fixed at zero. GFI,AGFI, NFI, CFI, RMSEA, AIC, and CMIN were usedas goodness-of-fit indices.
Pearson’s product-moment correlation coefficient
was used to analyze the correlation between
vVO2max, vOBLA, and the 3200-m run time. Whensignificant correlations were found between thevariables, regression equations were created to44estimate vVO2max or vOBLA from the 3200-m run
time using regression analysis. According to Hopkins
et al. (2009), the magnitude of correlation coefficient(r) was classified into 0.1 ≤ r < 0.3, 0.3 ≤ r < 0.5, 0.5≤ r < 0.7, and r ≥ 0.7 based on its absolute value andjudged as "Small,” "Moderate,” "Large,” and "Verylarge,” respectively. In addition, since r≥0.7 wasjudged to be "Very large,” the coefficient ofdetermination (r2) of 0.5 or greater was considered to
be a good fit.
SPSS ver. 26.0 for Windows was used for statisticalanalysis. The significance level for statistical
hypothesis testing was set at α=0.05.
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3. Results
Table 2 shows the results of the ANOVA for the3200-m run time, vVO2max, and vOBLA. For the3200-m run time, Mauchly’s sphericity test result was
not significant (W=0.885, p=0.076). The main effects
analysis revealed that the within-participants factor
was significant at the three measurement times (F(2,86)=111.01, p=0.000). The multiple comparison test
showed that there were significant differences
between U-13 and U-14 (p=0.000) or U-15 (p=0.000)
and between U-14 and U-15 (p=0.001). For
vVO2max, Mauchly’s sphericity test result was not
significant (W=0.929, p=0.212). The main effectanalysis revealed that the within-participant factor
was significant at the three measurement times (F(2,86)=44.69, p=0.000). The multiple comparison tests h o w e d s i g n i f i c a n t d i ff e r e n c e s b e t w e e n a l lmeasurement times (p=0.000). For vOBLA, theMauchly’s sphericity test result was significant(W=0.752, p=0.003). The main effect analysis
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Table 2 Aerobic running capability of the participants (n=44)

U-13

U-14

U-15

Mean ± SD

Mean ± SD

Mean ± SDvVO2max (km/h)

16.5 ± 1.4

17.7 ± 1.4*

18.5 ± 1.3**

vOBLA (km/h)

14.4 ± 1.1

15.2 ± 1.1*

15.8 ± 1.1**

3200-m run time (s)812.6 ± 34.6

760.2 ± 41.9 *Variables・

745.4 ± 41.0 *** Significantly different from U-13.
** Significantly different from U-13 and U-14
vVO2max: running velocity corresponding to maximal oxygen uptake
vOBLA: running velocity corresponding to onset of blood lactate accumulation
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Chuman, K. et al.

revealed that the within-participants factor was
significant at the three measurement times (F(1.6,
68.9)=40.65, p=0.000). The multiple comparison tests h o w e d s i g n i f i c a n t d i ff e r e n c e s b e t w e e n a l lmeasurement times (p=0.000).
Figure 1 shows the standard solution of thesequential causal structure model of aerobic runningcapability development in Japanese male junior youth
soccer players from U-13 to U-15, using SEM. The
goodness of model fit index was GFI=0.900,
AGFI=0.804, NFI=0.921, CFI=1.000, RMSEA=0.004,
AIC=67.014, and CMIN=23.014 (df=23, p=0.460).
The path coefficient between the latent variables fromU-13 to U-14 was significant at 0.71, and from U-14
to U-15 was also significant at 0.93 (p<0.05).
Figure 2 shows the standard solution of the latent
growth curve model of aerobic running capability
development (3200-m run time and vVO2max) fromU-13 to U-15 of Japanese male soccer player using
the SEM . Th e goodness of fit indices w ere
NFI=0.971, CFI=1.000, RMSEA=0.000, AIC=44.508,
and CMIN=4.508 (df=7, p=0.720). The pathcoefficient between the intercepts from the 3200-m
run time to vVO2max was significant at -0.97, andbetween the slopes from the 3200-m run time to4vVO2max was also significant at -0.86 (p<0.05).
Figure 3 shows the standard solution of the latent
growth curve model of aerobic running capability
development (3200-m run time and vOBLA) fromU-13 to U-15 of Japanese male soccer player using
the SE M . T he goodness of fit indice s we r e
NFI=0.964, CFI=1.000, RMSEA=0.000, AIC=46.156,
and CMIN=6.156 (df=7, p=0.522). The pathcoefficient between the intercepts from the 3200-m
run time to vOBLA was significant at -0.84, andbetween the slopes from the 3200-m run time tovOBLA was also significant at -0.90 (p<0.05).
Correlation analysis showed that the Pearson’s
product-moment correlation coefficient between the3200-m run time and vVO2max was significant (r=0.751, p=0.000) and between the 3200-m run timeand vOBLA (r=-0.735, p=0.000).
Figure 4 shows the results of the regression
analysis. The 3200-m run time (s) was theindependent variable (x), and vVO2max (km/h) orvOBLA (km/h) were the dependent variables (y) for
the regression equation.
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e1
.45

e2
e3

.40

・
vVO2max
vOBLA
・
vVO2max

e4

vOBLA

e5

・
vVO2max

e6

vOBLA

-.83

-.84

U-15

Aerobic runningcapability

.93
-.75

-.78

U-14

Aerobic runningcapability

.71
-.84

-.77

U-13

Aerobic runningcapability.89

3200-m run

e7

3200-m run

e8

3200-m run

e9

d1
.90

d2
.78

GFI=.900, AGFI=.804, NFI=.921, CFI=1.000, RMSEA=.004,
AIC=67.014, CMIN=23.014, df=23, P=.460
Figure 1 S equential causal structure model of aerobic running capability development from U-13 to U-15 of Japanese malesoccer player using structural equation modelingvVO2max: running velocity corresponding to maximal oxygen uptake
vOBLA: running velocity corresponding to onset of blood lactate accumulation
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Regression Equation Estimating Aerobic Running Velocity from 3200-M Run Time

d1
e1

U-13
3200-m run

.83
.74

e2

U-14
3200-m run

.71
.00
.47

e3

U-15
3200-m run

ICEPT3200-m run

-.97

.77
ICEPT
・
vVO2maxe4

U-14
・
vVO2maxe5

U-15
・
vVO2max

e6

.81
.85

.08
.37
SLOPE
3200-m run

U-13
・vVO2max

-.86

.00
SLOPE
・
vVO2max

.42
.69

.59
d2

NFI=.971, CFI=1.000, RMSEA=.000, AIC=44.508, CMIN=4.508, df=7, P=.720
Figure 2 L atent growth curve model of aerobic running capability development (3200-m run time and vVO2max) from U-13
to U-15 of Japanese male soccer player using structural equation modeling4

vVO2max: running velocity corresponding to maximal oxygen uptake
4

d1
e1

U-13
3200-m run

.82
.75

e2

U-14
3200-m run

.72
.00
.45

e3

U-15
3200-m run

ICEPT3200-m run

-.84

.87
ICEPT
vOBLA

e4

U-14
vOBLA

e5

U-15
vOBLA

e6

.91
.90

.34
.21
SLOPE
3200-m run

U-13
vOBLA

-.90

.00
SLOPE
vOBLA

.61
.99

.56
d2

NFI=.964, CFI=1.000, RMSEA=.000, AIC=46.156, CMIN=6.156, df=7, P=.522
Figure 3 L atent growth curve model of aerobic running capability development (3200-m run time and vOBLA) from U-13 to
U-15 of Japanese male soccer player using structural equation modelingvOBLA: running velocity corresponding to onset of blood lactate accumulation

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Chuman, K. et al.

4. Discussions
This study aimed to test the sequential causal
structure and latent growth curve models of aerobic
running capability and to develop regression
equations to estimate vVO2max or vOBLA from a
3200-m run time using longitudinal data fromJapanese male junior youth soccer players. The main
result of this study was that the following regression
equations were developed to estimate vVO2max orvOBLA from the 3200-m run time in Japanese malejunior youth soccer players (Figure 4).
vVO2max (km/h) =-0.02461×3200-m run time (s)+36.60 (r² = 0.564, n = 132)
vOBLA (km/h) =-0.01833×3200-m run time (s)+29.30 (r² = 0.541, n = 132)
The present study examined the development of
aerobic running capability using longitudinal datafrom participants and found that the 3200-m run time
significantly shortened from U-13 to U-14 and from
U-14 to U-15, and vVO2max and vOBLA significantly
increased from U-13 to U-14 and from U-14 to U-15
(Table 2). We did not find any previous reports
examining age-related changes in 3200-m run time,vVO2max, and vOBLA using 2-year longitudinal data
in male junior youth soccer players; therefore, theresults of this study cannot be compared. Using crosssectional data, Chuman et al. (2015) examined agerelated changes in the 3200-m run time in male junior
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youth soccer players and reported that the 3200-m run
time was shortened from U-13 to U-15. Chuman(2016, 2021) examined age-related changes invVO2max and vOBLA in young male soccer players
using cross-sectional data and reported that vVO2max
and vOBLA significantly increased from U-13 toU-15. This suggests that 3200-m run time, vVO2max,
and vOBLA in male junior youth soccer players show
development from U-13 to U-15 and that theirdevelopment is similar.
Analysis of the sequential causal structure modelof aerobic running capability from U-13 to U-15 in
the participants of this study showed that the
goodness-of-fit indices of the model were very good,with significantly higher values for path coefficients
between the latent variables (Figure 1). These results
indicate that the aerobic running capability affects
from U-13 to U-14 and from U-14 to U-15. To date,no reports have examined the causal structure of
aerobic running capability across age groups using
longitudinal data from male junior youth soccer
players; therefore, the results of this study cannot becompared. However, as mentioned above, in thepresent study, the 3200-m run time, vVO2max, andvOBLA showed significant development from U-13
to U-15, and their development was similar (Table 2).
Therefore, the sequential causal structure of aerobic
running capability in Japanese male junior youth
soccer players is likely affected by U-13 to U-14 and
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19

22
21
19
18
17
16
15

16
15
14
13

14

y=-0.02461x+36.60

13
12
0

Professionalsoccer players(Chuman, 2016)17

vOBLA (km/h)

・ max (km/h)
vVO
2

18

Professionalsoccer players(Chuman, 2021)

20

r²=0.564
600
0

660

720

780

y=-0.01833x+29.30

12
840

900

960

11
0

3200-m run time (s)r²=0.541
0
600

660

720

780

840

900

960

3200-m run time (s)Figure 4 R elationship between 3200-m run time and vVO2max or vOBLA in Japanese male soccer players from U-13 to U-15
4vVO2max: running velocity corresponding to maximal oxygen uptake
vOBLA: running velocity corresponding to onset of blood lactate accumulation
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Regression Equation Estimating Aerobic Running Velocity from 3200-M Run Time

U-14 to U-15.
Analysis of the latent growth curve model of
aerobic running capability in this study’s participants
showed that the model’s goodness-of-fit index wasvery good, and the path coefficients between latentvariables were significantly higher (Figures 2 and 3).
These results indicate that a shorter 3200-m run time
affects the increases in vV O 2 max and vOBLA.
Participants with a greater shortening of the 3200-m
run time had greater increases in vV O 2max andvOBLA. There have been no previous reports
examining the relationship between shorter 3200-m
run times and increased vVO2max or vOBLA using
longitudinal data in male junior youth soccer players;
therefore, the results of this study are not comparable.
Chuman et al. (2015) reported a significant correlation
between 3200-m run time and vVO2max or vOBLA
using cross-sectional data from Japanese male junior
youth soccer players. These findings suggest that ashorter 3200-m run time may increase vVO2max orvOBLA in Japanese male junior youth soccer players.
As mentioned above, this study did not simply
examine the relationship between 3200-m run timeand vVO2max or vOBLA in male junior youth soccer
players and used two years of longitudinal data fromU-13 to U-15 to determine the relationship between
intra-individual variability in 3200-m run time andthat in vVO2max or vOBLA. These results suggestthat aerobic running capability, as measured by
vV O 2 max and vOBLA, should be improved toachieve high aerobic running performance in Japanesemale junior youth soccer players. However, it is noteasy to conduct ILT in the laboratory to measurevVO2max and vOBLA in male junior youth soccer
players because it takes a lot of time and costs(Chuman et al., 2015). Therefore, a regression
equation to estimate vVO2max and vOBLA from a
simple field test is required to implement efficientaerobic training in junior youth soccer players. The
coefficient of determination (r2) of the regression
equation developed in this study to estimate vVO2max
or vOBLA from a 3200-m run time was 0.564 or
0.541, respectively (Figure 4). No regression
equations have been developed to estimate vVO2max
or vOBLA from a 3200-m run time in Japanese malejunior youth soccer players. Lorenzen et al. (2009)
reported a regression equation to estimate vVO2max
from a 3200-m run time in elite Australian soccer
players; the coefficient of determination (r2 = 0.63) of
the regression equation was similar to that of the444

participants in this study. According to Hopkins et al.
(2009), a correlation coefficient (r) of 0.7 or greater
can be considered “Very large,” so a coefficient ofdetermination (r2) of 0.5 or greater indicates that the
regression equation is a high fit. From these facts, it
can be inferred that the regression equation developedin this study to estimate vVO2max or vOBLA from a
3200-m run time has a high degree of fit.
However, the participants in this study wereJapanese male junior youth soccer players at a highly
competitive level who belonged to a single club, and
it is unclear whether this regression equation can beapplied to players at a lower competitive level.
Whether the regression equation can be applied tomale junior youth soccer players at various
competitive levels remains to be clarified.
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5. Conclusion
The purpose of this study was to test the sequential
causal structure and latent growth curve models of
aerobic running capability development by applying
SEM to aerobic running training in Japanese malejunior youth soccer players and to develop regression
equations to estimate vV O 2 max or vOBLA, an
indicator of aerobic running capability, from a 3200m run time. In conclusion, two hypothetical models
were proved using longitudinal data for two years inJapanese male junior youth soccer players, andregression equations were developed to estimate
vVO2max or vOBLA from a 3200-m run time.
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Acknowledgements

We would like to thank Hiroki Matsuoka, Ph.D. of
R&D Center for Sport Innovation, University ofTsukuba, and Kozue Ando, Ph.D. of Institute of
Health and Sport Sciences, University of Tsukuba,with the statistical data analysis of this study.
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4

Name:
Kentaro Chuman44Affiliation:
JUBILO CO., LTD.

Address:
2500 Shingai, Iwata, Shizuoka 438-0025 Japan
Brief Biography:
2003-2005 Master’s Program in Health and Physical Education,
University of Tsukuba
2009-2012 Doctoral Program in Physical Education, Health andSport Sciences, University of Tsukuba
2004-2010 Physical coach, Jubilo Iwata Academy
2011-2020 Assistant director & Physical adviser, Jubilo Iwata
2016-2020 Physical coach, U-15 to U-23 Japan National Team2021-2023 Physical coach, Jubilo Iwata
Main Publications:• Chuman, K., Hoshikawa, Y., Iida, T., and Nishijima, T. (2014)
Maturity and intermittent endurance in male soccer players
during the adolescent growth spurt: A longitudinal study.
Football Science, 11: 39-47.
• C human, K., Horio, K., Kitatsuji, K., Suijo, K., and Aoki,
T. (2022) Relationship between CHU-Test results and highintensity running during soccer matches. Football Science, 19:
78-84.
Membership in Learned Societies:• Japanese Society of Science and Football• Japan Society of Sports Performance Research• National Strength and Conditioning Association

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