Paper : Game Analysis

Match Activity Characteristics in Second Division ofJapanese Professional Football: Three Years of Full-Season
Analyses Using Global Navigation Satellite System
Makoto Ayabe*, Satoki Murai**, Takashi Oyama*, Hideaki Kumahara*** ,Shunsuke Sunami**** and Satoshi Ishizaki******Faculty of Computer Science and Systems Engineering, Okayama Prefectural University

111 Kuboki, Soja, Okayama 719-1197 Japanayabe@ss.oka-pu.ac.jp**Graduate School of Computer Science and System Engineering, Okayama Prefectural University***Faculty of Nutritional Sciences, Nakamura Gakuen University****Faculty of Economics, Toyo University*****Faculty of Engineering, Shibaura Institute of Technology[Received June 14, 2021; Accepted November 24, 2021]

This investigation primarily aimed to quantify the movement profiles of soccer matches according
to playing positions and tactical formations in the Japanese professional football league using
the global navigation satellite system (GNSS). Over three years, 758 observations were obtained
from 125 official matches of the second division of the Japanese professional football league. Data
were integrated into tactical formations and playing positions. Match activity was assessed by thevalidating GNSS (OptimEye X4; Catapult Sports). Furthermore, the duration and distance ofactivities at different speed thresholds (>19.8, >24, and 19.8-24 km/h) were assessed. The durationand distance of activities at all speed thresholds significantly differed with respect to tacticalformations and playing positions (p<0.05). Significant interactions between tactical formations andplaying positions were identified when the total distance and distance at 19.8-24 km/h thresholdwere assessed (p<0.05). Additionally, the 3-5-2 tactical system and central defender playing position
significantly differed from the other systems and positions (p<0.05). A shorter distance in the central
defender playing position and a longer distance in the 3-5-2 tactical formation were significantly
different features in match movement characteristics in the Japanese professional football league.
Keywords: global positioning system, tracking, soccer, game analysis, sprint[Football Science Vol.18, 81-91, 2021]

1. Introduction
Recent technological advances have enabled theobjective measurement of match activity (Ohashi
et al., 2002). The use of video analysis systems
and the global navigation satellite system (GNSS)
is a conventional method for assessing match
activity and can consistently obtain positioning
information (Kawasaki et al., 2019; Varley et al.,2017). Evaluating the location information at a higher
sampling rate can enable the accurate assessmentof match activity characteristics, such as distance,
speed, and acceleration (Di Salvo et al., 2007).
Additionally, the number of continuous movements
can be evaluated by analyzing time-course changes inlocation information. Thereafter, activity profiles are
Football Science Vol.18, 81-91, 2021
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evaluated depending on their purposes by integrating
the aforementioned law data over a specific time
period, such as the entire match, first half, foxed
minute, and/or training session. Hence, these systems
are considered valuable for external load evaluation.
Furthermore, video systems provide soccer-specificinformation, including how the ball was being
handled (Kawasaki et al., 2019). On the other hand,
the GNSS mainly comprises wearable devices on
the player’s back, and its main advantage is that
the GNSS can consistently evaluate the externalload in both matches and training sessions. Several
investigations have consistently suggested that both
systems can be used and that the information thatthey provide through the analyzed variables can beinterchanged (Iseyama et al., 2015; Pons et al., 2019;81

Ayabe, M. et al.

Taberner et al., 2019). This valuable information
enables us to better understand specific soccer match
activity characteristics.
Several investigations have already reported thematch activity profiles of elite players, including
those in the UEFA Champions League, European
league, Premier League (England), Serie A (Italy),
Bundesliga (Germany) (Andrzejewski et al., 2017;
Konefal et al., 2020), La Liga (Spain) (de Souza et al.,2021), and FIFA World Cup (Tuo et al., 2019). Among
26 confederations, 449 observations were obtained
from players during four seasons of the English
Premier and Championships Leagues using theProzone system (Di Salvo et al., 2013), and a total of
4,393 individual match observations from 350 soccer
players in the German Bundesliga were analyzed(Andrzejewski et al., 2017). Differences in running
performance among continental confederations wereanalyzed based on one confederation, and 508 match
observations were generated from 559 players in 59
matches at the 2018 FIFA World Cup in Russia (Tuoet al., 2019). These investigations showed the variety
of match movement patterns among soccer players,
including the total distance covered during the entire
match, the distance covered during high-intensity
running, the acceleration and deceleration patterns,
and the relationship of movement patterns withplaying positions and tactical formations (Baptista
et al., 2018; Bush et al., 2015; Rampinini et al., 2007;
Tierney et al., 2016).
With respect to Japanese professional soccer,
soccer tracking information has been studied sincethe 1970s or even earlier (Ohashi, 2020). Remarkably,
the total distance covered by the Japanese team in
the international match of Japan versus Korea for
the Asian qualifying competition for the Mexican
Olympic Games was measured using a curvimeter(Okano et al., 1976). The first conference of Medicine
and Science in Soccer held in 1980 presented theresults of a game analysis in which the distancecovered during the entire match differed according
to age categories, which ranged from children
to elite players, as well as between the Japanesenational team players and Argentinian players
who participated in the world youth championship(Ohashi et al., 1980). The total distance covered byfour elite soccer players, including two Japanesenational team players, was 9,303-11,601 m during
the entire match (Ohashi et al., 1988). Soccer matchcharacteristics, which are considered to be associated82with technological advances, have been objectively
analyzed among Japanese players (Ohashi, 2020).
Following the initiation of the Japanese professional
football league, small decreases in the total distancet raveled du r ing the later stages of the matchand frequent changes in speed were reported as
specifications for world-class foreign players (Miyagi
et al., 1999; Ohashi et al., 2002). Furthermore, theJapanese professional football league has publishedthe distance and number of sprints for each player inthe official J1 League match immediately after thematch (J-League, 2021). Owing to the aforementioned
environmental improvements and the spread of
GNSS devices, most professional soccer teams are
now able to obtain objectively evaluated match
activity characteristics. However, to the best of ourknowledge, no study has systematically analyzed the
movement patterns of Japanese professional soccer
games thus far. We hypothesized that the objectively
measu red t rack i ng i nfor mat ion sig n if ica ntly
differed with respect to match results, ranking of
the opposite team, location, playing positions andtactical formations among Japanese football players.
As match activity information is interchangeablein validating procedures, the data analysis of theJapanese professional football league may provide
useful evidence to compare the activities between
Japanese players and those from top-performing
countries.
Therefore, this study primarily aimed to quantify
the distance covered during the entire matches of theJapanese professional football league by objectively
measuring movement patterns and to examine theassociations of these patterns with playing positions
and tactical formations. This investigation focuseson the distance and duration of activities as thebasic index of match movement characteristics, as it
aims to demonstrate fundamental quantitative data
related to the movement characteristics of Japanese
professional soccer matches. While several data on
the GNSS, including acceleration, sprinting, andmetabolic power, have enabled researchers to develop
comparative data (Ayabe et al., 2020; Baptista etal., 2018; Higashino et al., 2018; Varley et al., 2017),
analysis of second variables from law data, such as
location, distance, and time, can be considered as thenext research issue.

Football Science Vol.18, 81-91, 2021
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GNSS-Based Soccer Match Analysis in the J2 League

2. Methods
Match activity data were collected from 125
matches of the second division of the Japanesesoccer league over three seasons between 2018 and2020. Throughout these three seasons, the actual
total number of matches played was 126; however,
one of these games was excluded from the analysis
because it was interrupted due to bad weather andwas divided across two days. According to previous
investigations (Di Salvo et al., 2007; Di Salvo et al.,2013), the selected data were limited to informationon outfield players who completed entire matches.
A total of 816 observations were obtained, and 58
observations (7.1%) were subsequently excluded due
to data errors in wearable devices. Consequently,
758 observations were included in the final analysis(Table 1). There were 20 players in 2018, 20 players
in 2019, and 18 players in 2020. The actual number ofplayers over the three-year period was 31; 12 players
were analyzed in only one of the three seasons, 11were analyzed in two of the three seasons, and 8 wereanalyzed in all three seasons. The players’ physicalcharacteristics, including their age, height, and bodyweight, were acquired from officially published datafrom the J-League (J-League, 2021). Body mass index
was calculated as body weight divided by heightsquared. This study was conducted in accordancewith the principles embodied in the Declaration of
Helsinki, and all procedures were approved by the
ethics committee of Okayama Prefectural University.
According to previous investigations (Di Salvo etal., 2007; Rampinini et al., 2007), the players wereclassified into the following seven positional roles(Figure 1): central defenders (CD), fullbacks (FB),
wingbacks (WB), central midfielders (CM), wide
midfielders (WM), attacking midfielders (AM), andforwards (FW). Tactical systems were determined by
the official match report and were divided as follows:
3-6-1 (3 CD, 2 CM, 2 WB, 2 AM, 1 FW), 3-5-2 (3CD, 2 CM, 2 WB, 1 AM, 2 FW), 4-4-2 (2 CD, 2 FB,
2 CM, 2 WM, 2 FW), and 4-5-1 (2 CD, 2 FB, 2 CM,
2 WM, 1 AM, 1 FW). The numbers of observations
for each playing position and tactical formation arepresented in Table 1. Match results were obtained
from the official match report and were divided toloss, draw, or win for the analysis. Among all 125
matches, there were 46 wins, 49 losses, and 30 draws.
Furthermore, observations were divided according tomatch location—that is, whether a team was playing
at its own ground (i.e., home) or at its opponent’s
ground (i.e., away). According to a specific team, 62
matches were played in the “home” stadium, whereas
63 matches were played in the “away” stadium.

Table 1 C haracteristics of formations and players taken from the global navigation satellite
system
Year
All

2018

2019

2020

Observation

Number of observations (n)Number of matches (n)Players per match (n)

758
125
7±2

262
41
7±1

278
42
7±1

218
42
6±1

Players

Number of players (n)Matches per player (n)Age (yr.)
Height (cm)Body weight (kg)Body mass index (kg/m2)

58
25±22
29±4
177.1±6.6
72.0±7.0
22.9±1

20
14±11
29±4
177.2±6.8
71.6±7.2
22.8±1

20
14±13
29±4
177.5±6.3
72.6±6.7
23±0.9

18
13±13
29±5
176.4±6.6
71.6±6.9
23±1.2

Playing Position

Central defenders (n)Fullbacks (n)Wingbacks (n)Wide midfielders (n)Central Midfielders (n)Attacking midfielders (n)Forwards (n)

257
123
64
74
161
30
49

106
65
64
39
49
30
13

79
58

72

3-5-2 (n)3-6-1 (n)4-4-2 (n)4-5-1 (n)

100
162
489
7

100
162
-

Tactical formation

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35
65

47

30

6

271
7

218
-

83

Ayabe, M. et al.

FW

FW
AM

WB

CD

CM
CD

FW

FW

AM

AM
WB

CM

FW

FW

CD

WB

WB
CM
CD

WM

CM

CM

WM

WM

CM
CD

CD

FB

CD

CD

FB

FB

WM
CM

CM

CD

CD

attacking

AM

(d)

(c)

(b)

(a)

FB

Figure 1 Tactical formations and playing positions across over three seasons
The illustrations the basic tactical formations and the playing positions for 3-6-1 (a), 3-5-2 (a), 4-4-2 (c), and 4-51(d).CD: Central defenders, FB: Fullbacks, WB: Wingbacks, CM: Central midfielders, WM: Wide midfielders, AM:
Attacking midfielders, FW; Forwards.

Finally, the opposite team was divided according to
the ranking of the final result; the opponents of 21
teams were categorized into seven teams as “highranking,” “middle-ranking,” and “low-ranking.”
Match play demands were determined by a global
position tracking system (Catapult OptimEye X4system; Catapult Sports, Melbourne, Australia).
All players placed this small device (96 mm × 52mm) on the back pocket of their undershirt during
the match. The data were downloaded to a portablecomputer after each match and were analyzed using
a specific data program (Openfield, Catapult Sports,
Melbourne, Australia). This device obtained location
information at 10 Hz of the sampling rate. The
validity of 10-Hz GNSS was reported as follows: the
10-Hz GNSS showed a sufficient level of accuracy forthe quantification of distance covered at high speeds
or time spent while intensively running (Johnston etal., 2014; Rampinini et al., 2015; Taberner et al., 2019)and has been used in previous investigations (Bishop
et al., 2020; Slater et al., 2018). Based on changes in
the location, the movement distance and speed wereobtained. The total distance covered and the distanceand duration at three different speed thresholds(19.8-24.0 km/h, >24.0 km/h, and >19.8 km/h) wereanalyzed. A speed of 19.9 km/h was defined as the
speed threshold for high-intensity running (Di Salvo
et al., 2007; Weston et al., 2011), whereas a speed of
24.0 km/h was considered as high-intensity sprinting(J-League, 2021; Konefal et al., 2020; Pons et al.,2019; Taberner et al., 2019).
Data are expressed as mean and standard deviations.
Significant differences in the duration and distanceaccording to season year, match results, opposite
team, formations, and positions were analyzed
84

using a one-way analysis of variance (ANOVA).
As differences within groups were statisticallysignificant, Scheffe’s multiple comparison was used
to compare each variable. To analyze the interactive
effects of playing formations and tactical systems on
the tracking information, significant interactions offormations and positions with duration and distancewere analyzed using a two-way ANOVA. When themain effects and/or interactions were significant,Scheffe’s multiple comparison was used to analyze
the differences between formations and positions. A
p-value <0.05 was considered statistically significant,and all statistical analyses were performed using
SPSS version 26.0 (IBM Corp., NY, USA).

3. Results
Table 2 shows the differences in total distance,
distance, and duration at thresholds of 19.8-24 km/h, >24 km/h, and >19.8 km/h according to season
year, match results, opposite team, location, tacticalfor mations, and playing positions. Across 758
observations obtained from 125 matches, the median
distance covered during each match was 9,871±803m/match and was significantly different by season
year, tactical formations, and playing positions(p<0.05). The total distance was signif icantly
shorter in 2020 than in 2018 and 2019 (p<0.001).
Furthermore, the distance and duration at thresholdsof 19.8-24 km/h, >24 km/h, and >19.8 km/h werealso significantly different with respect to tacticalformations and playing positions (p<0.05). However,the season year, match results, ranking of the opposite
team, and location (home/away) did not have any
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GNSS-Based Soccer Match Analysis in the J2 League

Table 2 D
 ifference in activity profile by year, match result, opposite team, stadium, tactical formations, and playing
positions in professional Japanese soccer matches
Mean
(SD)

P value (F value/t value)
Match results(Win/ Draw/Lose)p = 0.314(F = 1.162)

p = 0.473(F = 0.749)

Location(Home/Away)
p = 0.584(t = 0.548)

Total distance covered
(m)

9871
(803)

Season Year
(18/ 19/ 20)
p = 0.001(F = 7.335)

Distance at >19.8 km/h
(m)

565
(228)

p = 0.135(F = 2.006)

p = 0.632(F = 459)

p = 0.602(F = 0.597)

Distance at >24 km/h
(m)

145
(95)

p = 0.716(F = 0.334)

p = 0.495(F = 0.705)

Distance at 19.8-24 km/h
(m)

420
(157)

p = 0.056(F = 2.885 )Duration at >19.8 km/h
(sec)

91
(36)

Duration at >24 km/h
(sec)
Duration at 19.8-24 km/h
(sec)

Formations**

Position***p = 0 .036
(F = 2.863)

p < 0.001(F = 73.408)

p = 0.499(t = 0.676)

p < 0.001(F = 6.350)

p < 0.001(F = 102.004)

p = 0.642(F = 0.443)

p = 0.401(F = 0.840)

p = 0.013(F = 3.598)

p < 0.001(F = 72.921)

p = 0.770(F = 261)

p = 0.547(F = 604)

p = 0.817(F = 0.231

p < 0.001(F = 6.340)

p < 0.001(F = 92.983)

p = 0.232(F = 1.464)

p = 0.513(F = 0.667)

p = 0.515(F = 665)

p = 0.518(t = 0.646)

p < 0.001(F = 6.620)

p < 0.001(F = 99.374)

20
(13)

p = 0.775(F = 0.255)

p = 0.768(F = 0.264)

p = 0.498(F = 0.698)

p = 0.920(t = 0.100)

p = 0.010(F = 3.811)

p < 0.001(F = 71.147)

71
(27)

p = 0.075(F = 2.603)

p = 0.619(F = 481)

p = 0.514(F = 0.666)

p = 0.407(t = 0.830)

p < 0.001(F = 6.729)

p < 0.001(F = 90.572)

Opposite*

Bold indicates statistical significance. *The opposite team was classified into one of three groups (upper, middle,
or lower) based on its ranking in the J2 league at the end of each year. **Comparison of three tactical formations,
including 3-5-2, 3-6-1, and 4-4-2, as the data from the 4-5-1 formation were excluded from the analysis because of thesmall sample size. ***The position was classified into one of seven positions, comprising central defenders, fullbacks,
wingbacks, central midfielders, wide midfielders, attacking midfielders, or forwards.

significant effects on distance or duration.
Figure 2 shows the total distance covered during
the entire match according to tactical formations
and playing positions. The two-way ANOVA reveledsignificant interactions of tactical formations andplaying positions with the total distance covered
during the entire match (p<0.001). Irrespective of
playing formations, CD covered significantly shorter
distances than FB, WB, CM, and AM (p<0.05).
Furthermore, significant differences in tacticalformations within the same playing position wereobserved in CD; the CD in the 4-4-2 formation
covered significantly shorter distances than the CD
in the remaining two formations (p<0.05). After
including all tactical formations, the total distancecovered during each match significantly differed interms of playing positions (CD < FW < FB, WM,
CM, and AM; p<0.05). After including all playing
positions, the total distance covered during eachmatch significantly differed in terms of tacticalformations (4-4-2 and 3-6-1 < 3-5-2; p<0.05).
Figure 3 shows the distance covered during
the entire match at thresholds of 19.8-24.0 km/h,>24.0 km/h, and >19.8 km/h. The two-way ANOVA
revealed significant interactions of tactical formations
and playing positions with the distance covered
during the entire match at a threshold of 19.8-24.0
Football Science Vol.18, 81-91, 2021
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km/h (p=0.013). This analysis also showed that there
were no significant differences between the distancecovered at speeds of >24.0 km/h and >19.8 km/h.
There were significant main effects associated withplaying formations at all speed thresholds (p<0.001).
Furthermore, the significant effects associated withtactical formations were limited to the distancecovered at thresholds of 19.8-24.0 km/h and >19.8
km/h (p<0.001). Irrespective of playing formations,
CD covered significantly shorter distances thanFB, WB, CM, and AM (p<0.05). Furthermore,significant differences in tactical formations within
the same playing position were observed in CD;
the CD in the 4-4-2 formation covered significantly
shorter distances than the CD in the remaining two
formations (p<0.05). After including all tacticalformations, the total distance covered during eachmatch significantly differed with respect to playing
positions (CD < FW < FB, WM, CM, and AM;
p<0.05). After including all playing positions, thetotal distance covered during each match significantly
differed with respect to tactical formations (3-6-1 <4-4-2 and 3-6-1; p<0.05).
Figure 4 shows the duration covered during
the entire match at thresholds of 19.8-24.0 km/h,>24.0 km/h, and >19.8 km/h. The two-way ANOVA
revealed significant interactions of tactical formations
85

Ayabe, M. et al.

12,000a
11,000

a
b
☆

a
a

a

e

a
a
c
e

a

a

★
☆

a
b
c
d
e
f

Main effects (Formation) p < 0.001*
Main effects (Position) p < 0.001*
Interaction p < 0.001*
† ††

9,000

All-AllAll-451

All-442All-361All-352

FW-AllAM-AllCM-All

WM-AllFB-AllWB-All

CD-All

FW-451

WM-451FB-451

CM-451

CD-451

FW-442

WM-442FB-442

CD-442

FW-361

AM-361

WB-361

CM-442p < 0.001+

p < 0.001+

CD-361

FW-352

AM-352

WB-352

CD-3527,000

p < 0.001+

CM-3618,000

CM-352

Distance (m)10,000

a

a
b
e

a
e

a

a
a
b
b
d

Position - formation

Figure 2 C omparison of total distance covered during entire match among tactical formations and playing
positions
The X-axis shows the tactical formations and the playing positions, 352, 3-5-2 (100 observation in 2018); 361, 3-6-1(162 observation in 2018); 442, 4-4-2 (271 observation in 2019 and 218 observations in 2020); 451, 4-5-1 (7observationin 2019), All; Averaged over all formations, CD; Central defenders, FB; Fullbacks, WB; wingbacks,CM; Central
Midfielders, WM; Wide midfielders, AM; Attacking midfielders, FW; Forwards, All; Averaged over all positions. *Pvalue show the results of the 2 way – ANOVA by the formation and the position, the data from 4-5-1 formation was
excluded from the analysis due to the small sample size, +P value show the results of the one way – ANOVA by theformation. †, †† Significantly different compared with ”3-5-2” († p < 0.05, †† p < 0.01), a-f Significantly different comparedwithin same formation compared with a CD, b FB, c WB, d WM, e CM, f AM, (p < 0.05). ★ ☆ Significantly different
compared within same position compared with ★ 3-5-2 and ☆ 3-6-1 (p < 0.05).

and playing positions with the distance covered
during the entire match at the threshold of 19.824.0 km/h (p=0.011). The analysis also showed that
there were no significant differences in the distancecovered at thresholds of >24.0 km/h and >19.8 km/
h. There were significant main effects associatedwith playing formations at all three thresholds(p<0.001). Furthermore, the significant effects
associated with tactical formations were limited tothe distance covered at thresholds of 19.8-24.0 km/h
and >19.8 km/h (p<0.001). Significant differences inthe duration of high-intensity running were similar
to those of the entire match. Particularly, positionspecific differences were obtained in CD over threetactical formations regardless of speed thresholds(p<0.05). The average running duration at all playing
positions at thresholds of 19.8 km/h and >19.8 km/h significantly differed among the three tacticalformations (4-4-2 and 3-6-1 < 3-5-2, p<0.05).

4. Discussion
This investigation showed the objectively measured
match activity characteristics of soccer matches
86

among Japanese professional soccer players using
the GNSS. Match movement patterns, including the
distance covered during the entire match, duration of
the match, and high-intensity running were analyzed
in 758 observations from 125 matches over threecalendar seasons. Consequently, this investigation
found significant effects of playing formations andtactical systems on the distance covered during theentire match and its duration. Nevertheless, thisinvestigation was unable to find significant effects ofseason year, match results, ranking of the opposite
team, and location on the distance and duration
at all three thresholds, whereas the total distancecovered significantly differed over three seasons.
Because of coronavirus disease 2019 (COVID-19),
the 2020 season of the J2 League was different
from the past two years. Previous investigations
demonstrated that the total distance covered, highintensity distance, sprint distance, and number ofsprints were not significantly affected by the numberof matches per week, that is, one versus two (Dupont
et al., 2010). However, recent meta-analyses havefound that the effect of fixture congestion on the
total distance covered during the entire match was
insignificant (Julian et al., 2021). Therefore, these
Football Science Vol.18, 81-91, 2021
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GNSS-Based Soccer Match Analysis in the J2 League> 19.8 km/h

1,300
1,200

a
e

1,100

a

1,000

a
e

a

900
800

★

700

★

a
b
e

a

a

a
b
a
d

a
a

a
b

a

a
b
c
d

a
b

Main effects (Formation) p < 0.001*
Main effects (Position) p < 0.001*
Interaction p = 0.091*
a
c
d
††

††

a

600

★

500
400
300
200

p < 0.001+

100

p < 0.001+

p < 0.001+

500> 24.0 km/h

★
400

a
e

Distance (m)

300

a
e

a
e

a
e

a
e

a
b
e

a
e

a
e

a
b

a
e

a
e
a
e

a
b
c
d

★

200

Main effects (Formation) p = 0.230*
Main effects (Position) p < 0.001*
Interaction p = 0.868*

a
b

★
100

p < 0.001+

900

p < 0.001+

p < 0.001+

19.8 to 24.0 km/h

a
e

800

a

700

a
e

a

a

600

a

500

a

★
a
g

★

a

a
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a
b
★
e
a
d

a
b

a

a
c
d

a
b
e

Main effects (Formation) p < 0.001*
Main effects (Position) p < 0.001*
Interaction p = 0.013*
a
c
d
f

††

††

★
☆

400
300
200

All-AllAll-451

All-442All-361All-352

FW-AllAM-AllCM-All

WM-AllFB-AllWB-All

CD-All

FW-451

WM-451FB-451

CM-451

CD-451

FW-442

WM-442FB-442

CM-442

CD-442

FW-361

AM-361

WB-361

CD-361

FW-352

AM-352

WB-352

CD-352

CM-3520

p < 0.001+

p < 0.001+

p < 0.001+

CM-361100

Position - formation

Figure 3 C omparison of distance covered by high intensity run during entire match among tactical formations and playing
positions
The X-axis shows the tactical formations and the playing positions, 352, 3-5-2 (100 observation in 2018); 361, 3-6-1 (162
observation in 2018); 442, 4-4-2 (271 observation in 2019 and 218 observations in 2020); 451, 4-5-1 (7 observation in 2019) ,All; Averaged over all formations, CD; Central defenders, FB; Fullbacks, WB; wingbacks, CM; Central Midfielders, WM; Widemidfielders, AM; Attacking midfielders, FW; Forwards, All; Averaged over all positions. *P value show the results of the 2 way –ANOVA by the formation and the position, the data from 4-5-1 formation was excluded from the analysis due to the small sample
size, +P value show the results of the one way – ANOVA by the formation. †, †† Significantly different compared with ”3-5-2” († p < 0.05,†† p < 0.01) , a-f Significantly different compared within same formation compared with a CD, b FB, c WB, d WM, e CM, f AM, (p < 0.05). ★☆ Significantly different compared within same position compared with ★ 3-5-2 and ☆ 3-6-1 (p < 0.05) .

seasonal differences in the total distance were causedby the playing positions and tactical formations
rather than COVID-19-associated fixture congestion.
Subsequently, the results of the two-way ANOVA
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showed significant interactions of playing positions
and tactical formations with activity characteristics.
The results suggested that, regarding the total value
added to outfield players, the 3-5-2 tactical system
87

Ayabe, M. et al.
> 19.8 km/h

250

p < 0.001+

p < 0.001+
200

a
e

a
e

a

★

100

a
b
e

a ★
e
a
a

150

Main effects (Formation) p < 0.001*
Main effects (Position) p < 0.001*
Interaction p = 0.058*

p < 0.001+

a

a

a
b

a
d

a

a
b

a
b
e

a
b
c
d

a

a
c
d
f

††

††

★

50

80

> 24.0 km/h

70p < 0.001+

p < 0.001+

60

a
e

Duration (sec)

50

a
e

40

a
e

a
e

a
e

Main effects (Formation) p = 0.173*
Main effects (Position) p < 0.001*
Interaction p = 0.882*

p < 0.001+
a
b a
e e

a
e
a
e

★

30

★

20
10
0

200

19.8 to 24.0 km/h

180p < 0.001+

p < 0.001+
a
e

160
140

a

120

a
e ★

a

a

100

★

80

p < 0.001+

a

a

a

a

a
★
b
a
e
e
d

a
b

a
b

a

a
b
a e
c
d

Main effects (Formation) p < 0.001*
Main effects (Position) p < 0.001*
Interaction p = 0.011*
a
c
d
f

††

††

★

60
40

All-AllAll-451

All-442All-361All-352

FW-AllAM-AllCM-All

WM-AllFB-AllWB-All

CD-All

FW-451

WM-451FB-451

CM-451

CD-451

FW-442

WM-442FB-442

CM-442

CD-442

FW-361

AM-361

WB-361

CD-361

CM-361

FW-352

AM-352

WB-352

CD-3520CM-35220

Position - formation

Figure 4 C omparison of duration for high intensity run during entire match among tactical formations and playing
positions
The X-axis shows the tactical formations and the playing positions, 352, 3-5-2 (100 observation in 2018); 361, 3-6-1(162 observation in 2018); 442, 4-4-2 (271 observation in 2019 and 218 observations in 2020); 451, 4-5-1 (7 observationin 2019), All; Averaged over all formations, CD; Central defenders, FB; Fullbacks, WB; wingbacks, CM; Central
Midfielders, WM; Wide midfielders, AM; Attacking midfielders, FW; Forwards, All; Averaged over all positions. *P value
show the results of the 2 way – ANOVA by the formation and the position, the data from 4-5-1 formation was excluded
from the analysis due to the small sample size, +P value show the results of the one way – ANOVA by the formation. †,†† Significantly different compared with ”3-5-2” († p < 0.05, †† p < 0.01) , a-f Significantly different compared within sameformation compared with a CD, b FB, c WB, d WM, e CM, f AM, (p < 0.05). ★☆ Significantly different compared withinsame position compared with ★ 3-5-2 and ☆ 3-6-1 (p < 0.05) .

may demand larger amounts of running than those in
the 4-4-2 and 3-6-1 systems. Additionally, it may beapparent that the CD covered a shorter distance andduration than those in other playing positions during
the soccer match regardless of tactical formations.
88

These results were supported by previous findings,
and a shorter distance in CD and a longer distance inthe 3-5-2 system have already been reported (Baptista
et al., 2018; Rampinini et al., 2007; Tierney et al.,2016). The investigation of elite European players
Football Science Vol.18, 81-91, 2021
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GNSS-Based Soccer Match Analysis in the J2 League

showed that the total distance, high-intensity running
distance, and very-high-intensity running distancewere significantly shorter in CD than in other playing
positions (Rampinini et al., 2007). Similar results
were obtained in elite Norwegian soccer players;
elite Norwegian CD and CM had significantly lower
work rates in sprints, decelerations, and accelerations
than FB, WM, and central FW (Baptista et al., 2018).
Moreover, a 3-5-2 formation was reported to elicit
a higher total distance, running speed, and highmetabolic load distance than all other formations,
including the 4- 4-2, 4-3-3, 3- 4-3, and 4-2-3-1
formations in elite English male footballers (Tierney
et al., 2016). Thus, it is highly suspected that a shorter
distance in CD and a longer distance in the 3-5-2
formation can be considered as features of significant
differences in match movement characteristics in theJapanese professional football league.
An original aspect of this investigation is that
we quantified the variability of match movement
characteristics in the Japanese professional footballleague based on objectively measured parameters in
a comparative sample size. As previously mentioned,
regarding elite Japanese football players and/or theJapanese professional football league, the match
movement characteristics of one very talented player
in the Japanese national team have been reportedly
analyzed; furthermore, currently the movement
distance and number of sprint runs can be obtained
immediately after any match in the J1 League(J-League, 2021; Miyagi et al., 1999; Ohashi, 2020;
Ohashi et al., 2002; Ohashi et al., 1980; Ohashi
et al., 1988; Okano et al., 1976). The strengths ofthis investigation were as follows. First, the match
movement characteristics were objectively measured
using a reliable 10-Hz GNSS device. The GNSS
system is one of the most useful and validatingassessments of movement characteristics in soccer
matches (Rampinini et al., 2015; Varley et al., 2017)and provides data that are interchangeable with thoseobtained through video analysis systems (Buchheit
et al., 2014; Pons et al., 2019; Taberner et al., 2019).
Second, the data sample size, which included 758
observations from 125 official matches over threecalendar seasons, provides additional strength to thegeneralizability of the study findings to football fields.
To the best of our knowledge, this study is the first to
include foreign elite/semi-elite players as well as elite
Japanese professional football players in the analysis
of match movement patter ns using the GNSS.
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Furthermore, the present investigation is the first to
analyze patterns using all official league matches
throughout the season. The 758 observations from 125
matches in this investigation are possibly comparable
to those of previous GNSS-based investigations of
the European national league (Baptista et al., 2018;
Hoppe et al., 2017; Martín-García et al., 2018). We
accordingly believe that this investigation provides
novel evidence on quantitative match movement
characteristics of Japanese professional soccer
players.
Remarkably, the speed threshold was inconsistently
associated with differences in playing positions andtactical formations. This investigation revealed thatthe distance covered during an entire match wassignificantly more associated with a low threshold(<24.0 km/h) than with a high threshold (>19.8 km/h
or 24.0 km/h). This result is unexpected as previous
investigations have shown significant differences inthe distance covered between high- and low-speed
running (Baptista et al., 2018; Rampinini et al., 2007;
Tierney et al., 2016). Furthermore, there was noconsensus on the number of sprints associated withspeed thresholds and interruption treatments (Varley
et al., 2017). Although the Japanese professional
football league defined the sprint as the movement
at the speed completely maintained at 24.0 km/h or
faster for 1 second or longer (J-League, 2021), thespeed threshold was ranged from 20.0 to 25.0 km/h, the time interval is also inconsistent from 0.5 to
1 second, furthermore, some investigation allowing
the slower movement less than speed thresholdsome investigation allowing the slower movement
less than speed threshold (Bush et al., 2015; Di
Salvo et al., 2010; Di Salvo et al., 2013; Rampinini
et al., 2007; Weston et al., 2011). Thus, there is noconsensus on the appropriate speed threshold toobtain movement patterns associated with playing
positions and tactical formations. Furthermore,
researchers should focus on the interchangeability
based on movement index definition. Nevertheless,this investigation is insufficient to understand thematch activity of Japanese professional football
players because European studies have analyzed ahuge number of observations by integrating teams
and seasons based on video analysis systems, suchas the 972 observations among seven confederationsin the German Bundesliga (Konefal et al., 2020),
the 26,449 observations in the English Premier andChampionships Leagues (Di Salvo et al., 2013), and89

Ayabe, M. et al.

the 14,700 observations in the English Premier League(Bush et al., 2015). As the Japanese professional
football league published the performance of matches
in the J1 League, at least 3,000 observations (5outfield players playing for more than 90 min/match,
18 teams, and 34 matches/year) can be included in thedataset.
This study has several limitations. First, as thepresent investigation analyzed only one second
division team, the results of this investigation do
not represent the top level of Japanese professional
football players. Second, this study was observational.
Similar to numerous previous investigations, thetracking data during the official soccer match was
not reliable. Not only the opponent team but also
environmental factors and the players’ physiologicalconditions could not be controlled for research
purposes in actual official soccer matches that include
professional players. These two limitations arepotentially inevitable in observational investigationsof elite athletes. Therefore, future studies are expected
to analyze the next elite players by summarizing
various confounders. Based on the findings of thisstudy, the details of the match activity profiles ofprofessional Japanese soccer players may aid us inprogressively understanding the movement patternsof players. Finally, as previously mentioned when
discussing the objective of this research, the GNSS
provides us with not only the distance and durationof movements but also several secondly calculated
variables, including bout duration, acceleration,
deceleration, metabolic power, hear t rate, anddifferent sensor outputs. Additionally, all information
can be analyzed not only during the entire match
but also at specific time intervals. Therefore, theanalysis of soccer match activity based on positioning
information, irrespective of video analysis or GNSS,
has several undeveloped research issues, particularly
in Japanese professional football.
I n su m mar y, this investigation showed theobjectively measured match activity characteristics
of Japanese professional soccer players using the
GNSS in 758 observations from 125 matches overthree calendar seasons. The present investigationconsequently revealed the significant effects of playing
formations and tactical systems on the distance andduration of movements. The shorter distance in
the CD playing position and the longer distance inthe 3-5-2 tactical formation may be considered asfeatures of significant differences in match movement
90

characteristics in the Japanese professional footballleague. To understand the specificity of Japanesesoccer players, multiple confounders such as teamcategory, match information, and physiologicalconditions of players should be analyzed. Significant
findings can be obtained by extensively analyzing
location information using not only the GNSS but
also video analysis.
Acknowledgments

Part of this study was supported by KAKENHI(19K11493).
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Name:
Makoto Ayabe
Affiliation:
Faculty of Computer Science and Systems
Engineering, Okayama Prefectural University

Address:
111 Kuboki, Soja, Okayama 719-1197 Japan
Brief Biographical History:
Juntendo University (BS)Fukuoka University (MS)Hokkaido University (PhD)
Main Works:• Ayabe, M., Okita, Y., Higashino, Y., Morimura, K., Kumahara,
H., and Ishizaki, S. (2020). Cardiorespiratory responses torecreational small-sided walking soccer in community-dwelling
middle-aged to older adults with mild metabolic disorders.
Football Science, 17:11-15.
• Ayabe, M., Ishizaki, S., and Sakaue, K. (2020). Narrative
review: GPS-based Tracking system in football. The Journal ofJapanese Society of Science and Football, 15:13-22.
Membership in Learned Societies:• Japanese Society of Science and Football• Japanese Society of Physical Fitness and Sports Medicine• International Society for the Measurement of Physical Behavior

91