Paper : Game Analysis

Factor and Causal Structures of Counter Attack Skill
in Soccer
Hiroki Matsuoka*, Kozue Ando* and Takahiko Nishijima**Faculty of Health and Sport Sciences, University of Tsukuba

1-1-1 Tennodai, Tsukuba, Ibaraki 305-8574 Japanmatsuoka.hiroki.ga@u.tsukuba.ac.jp[Received February 2, 2022; Accepted June 28, 2022]

This study aimed to investigate the factor and causal structures of counter attack skills in soccer.
The research procedure comprised three steps: 1) qualitative analysis to construct a qualitative
causal structure and criterion-referenced measurement items of counter attack skills, 2)
confirmatory factor analysis of counter attack skills in soccer, and 3) causal structure analysis using
structural equation modelling (SEM). Counter attacks from 275 plays were collected through video
files. Qualitative analysis led to the construction of the causal structure of counter attack skills insoccer, consisting of penetrative dribble, lay off, penetrative pass, pushing down the opponent’s
DFL (Defence line), and disrupting the opponent’s defence, along with 17 criterion-referenced
measurement items for these skills. The model fit indices of the confirmatory factor structure andthe causal structure model were found to be acceptable, thus establishing the validity of the models.
The causal structure model revealed significant causal relationships (p < 0.05) between penetrative
dribble skill and pushing down the opponent’s DFL skill (0.555), lay off skill and penetrative pass
skill (0.588), and penetrative pass skill and disrupting the opponent’s defence skill (0.653). The
findings thus reveal the factor structure and the causal relationships among counter attack skills insoccer.
Keywords: f ootball analytics, hand notational analysis, counter play, structural equation modelling[Football Science Vol.19, 114-123, 2022]

1. Introduction
Game tactics in soccer has been developing
remarkably. As such, soccer teams are playing at
a faster pace in a more compact space. Hence, thenumber of goals from counter attacks has been
increasing. Wallace and Norton (2014) analysed thefinals of 12 World Cup tournaments that took placefrom 1966 to 2010. They found that in 44 years,
ball speed increased by 15%, whereas the numberof passes per minute increased by 35%. Barnes etal. (2014) analysed the 2006-2007 to 2012-2013
seasons of the English Premier League. They foundthat the sprint distance increased by approximately
35%, while the frequency of sprint increased byapproximately 85% over seven years. In addition, thenumber of passes, successful passes, and short passes
increased in the 2012-2013 season. The technicalreport of the 2018 World Cup in Russia claimed
the following findings: (1) the average distance
between the defensive line of the defensive teamand the frontline players was 26 m, (2) the defensive
114

formation was compact, and (3) majority of the goals
were scored from fast attacking play (FIFA, 2018).
The results of these studies indicate that soccer
game tactics have developed, and that the number ofcounter attacks, which are attacks that happen before
the preparation of the opposing team's defence, isincreasing.
Soccer game performance was measured in gameperformance analysis research, which indicatedthe relationships between counter attacks and goal
scoring or its opportunity. Tenga et al. (2010a, 2010b)
analysed the relationship between offensive outcomes
and tactical play in the 2004 Norwegian Professional
League using binomial logistic regression analysis.
They found that penetrative passes and counterattacks against an imbalanced defence were related
to goal scoring (Tenga et al., 2010a) and scorebox
entry (Tenga et al., 2010b). Lago-Ballesteros et al.
(2012) analysed the relationship between scorebox
penetration and tactical play in the Spanish First
Division in 2009-2010 and found that long passes
and counter attacks were effective against scorebox
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Causal Structure of Counter Attack Skill in Soccer

penetration. Hughes and Lovell (2019) analysed theoffensive transition of 29 final tournament matches
in the Union of European Football Associations
Champions league 2014-2015 and reported that 20
m runs (≥ 20 m), dribbles, and long passes (> 25 m)
immediately after ball gain resulted in an increasein the number of goals and scoring opportunities.
These previous studies measured the effectiveness ofcounter attacks by counting event-related items, suchas long pass, 20-m run, and dribble. As a result, theeffects of counter attacks for goal scoring and scoring
opportunities were determined.
However, existing literature is limited to a
descriptive analysis of counter attacks. Furthermore,factors associated with soccer skill related to counterattacks have not been studied. Skill is a trainableability (Breivik, 2016), and to promote data-driven
coaching for counter attacks, it is necessary to identify
the skill factors involved in counter attacks and therelationships between these factors. Sport skills
are components that cannot be directly measured;
hence, they are estimated as common factors (latentvariables) from the correlation structure among
measurement items (observed variables) by applying
exploratory factor analysis and confirmatory factor
analysis. In previous studies, offensive and defensive
skills were estimated based on performance items insoccer games.
Suzuki and Nishijima (2002) determined the skill
structure and the causal structure of skills in theattacking phase of soccer by using structural equation
modelling (SEM). They found that the attacking skills
in soccer consisted of space-making, progression,and break skills. Moreover, they found that there
were causal relationships between space-making
skills to progression skills and progression skills to
break skills. In addition, Suzuki and Nishijima (2004)
analysed the defence skill structure and the causal
structure between skills in soccer. Defensive skills
comprised defensive phases (delaying attack, forcing
play in one direction, squeezing the working spaceof attackers) and defensive targets (defence against
an attacker with the ball, defence against attackers
without the ball, defence to attacking space) in a
multidimensional confirmatory factor structure model.
Matsuoka et al. (2020) revealed the factor structureof defence tactical skills, control defence skills, andconcentration defence skills by using defence play
items, which were constructed by tracking the dataof soccer games and by using exploratory factor andFootball Science Vol.19, 114-123, 2022
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confirmatory factor analyses.
However, previous studies exploring counterattacks only analysed the relationships between
game event items, which were measured from gameperformance and offensive outcomes. The skill
factors involved in counter attack play have notbeen examined. With the recent development of datatechnology and data science, data on soccer games
has been widely studied (Rein and Memmert, 2016;
Memmert et al., 2016). Considering the development
of data-driven coaching using game data analysis, it
is essential to elucidate the skill factors involved incounter attack plays in soccer games and the causal
structure between skills.
The purpose of this study was to investigate thefactors and the causal structure of the counter attack
skill in soccer.

2. Methods
2.1 Research procedure
The research procedure was divided into threeparts: (1) qualitative analysis for constructing aqualitative causal structure and criterion-referenced
measurement items of the counter attack skill; (2)confirmatory factor analysis of counter attack skills insoccer; and (3) causal structure analysis using SEM.

2.2. Samples
There were 275 counter attack plays collected frominternational matches and top football leagues during
the 2019-2020 season (Table 1). Match videos were
downloaded from the Wyscout platform (Wyscout
Spa, Italy).
Table 1 Sample
Competitions (season)

N of samples N of games

FIFA World Cup 2018

1

1

UEFA Champions League (2019-20)

89

23

UEFA Europe League (2019-20)

3

1

English Premiere League (2019-20)

19

8Spanish La Liga (2019-20)

52

German Bundesliga (2019-20)

158

83

Total

275

118

115

Matsuoka, H. et al.

The plays were collected from the start to theend of the possession. Plays that started with freekicks or corner kicks were excluded. The start of thepossession corresponded to the ball gain from the
opposing team, the goal kicks in the attacking team,and the throw-in. The end of play corresponded to theloss of ball possession by ball gain of the opposing
team, halting of the possession (out of play), andoffensive outcomes, such as shots and goals. The
possession was considered to be continued when theattack was not interrupted, even if a player of the
opposing team touched the ball during the possession.
The definition of counter attack proposed by Tenga
et al. (2009; 2010a; 2010b) was used. They definedcounter attack as “Counter attack: starts by winningthe ball in play and progresses by either (a) utilizingor attempting to utilize a degree of imbalance fromstart to the end, or (b) creating or attempting to createa degree of imbalance from start to the end by using
early (i.e. first or second, evaluated qualitatively)
penetrative pass or dribble. Utilizing a degree ofimbalance means seeking penetration in such a waythat a defending team fails to regain a high degreeof balance from start to the end of team possession.
Counter attacks progress relatively quickly.” In
addition to this definition, counter attacks from quicktransitions from goal kicks and throw-ins were also
included in the analysis in this study because there
was an opportunity to create imbalance. All collected
plays were checked by four soccer experts to ensurethat they met the definition.
Approval to conduct this study was obtained fromthe Ethics Committee of University of Tsukuba(Project No. 021-36).

2.3. Construction of the structure of the counterattack skills and measurement items
The measurement items for the soccer counterattack skills were constructed based on the procedurein the study by Matsuoka et al. (2020; 2021). The
qualitative causal structure, criterion-referenced
measurement items, and success criteria of thecounter attack skills were determined by experts
via the cause-and-effect analysis using the Delphi
method. Four soccer experts participated in this study.
Two were soccer players who had experience playing
with the Japanese national team and professional
clubs in Europe. One was a coach with more than
10 years of experience. The fourth was a graduate
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student who used to play soccer and who was engaged
in sports science research. Three of them hold JFA
Official class C coach license. They were experienced
in using qualitative analysis methods. Individualinterviews were conducted with the participants, andbased on the extracted information, the researchersorganised the data into a cause-and-effect (fishbone)
diagram. The researchers then reported back to thesoccer experts. The procedure was repeated until all
the soccer experts agreed on the results.
Table 2 shows the playing phases, skills,
measurement items, and success criteria for thecounter attacks in soccer. The penetration phaseconsisted of 11 items covering three skills: four items
for penetrative dribble, four items for lay off*1, andthree items for penetrative pass. The disorganisationof the opponent's defence phase consisted of six items
covering two skills: two items for pushing down the
opponent's defence line (DFL) skill and four items for
disrupting the opponent's DFL skill. Counter attacks
in soccer were measured using binary data, whichwere deemed either achieved (1) or unachieved (0),according to the success criteria of the measurement
items. The success criteria were clearly illustrated
in the pictures of the play. A single rater used thesesuccess criteria for measurement.

2. 4. Statistical analyses
A confirmatory factor structure model wasconstructed based on the qualitative causal structureof the counter attack skills, and a causal structure
model was constructed based on the confirmatory
factor structure. SEM was applied to analyse theconfirmatory factor structure and causal structure of
the counter attack skills based on the goodness-of-fit
indices and the significance of the path coefficients inthe measurement and structural models. Tetrachoric
correlation matrices were calculated based on theachievement binary data, and diagonal weighted leastsquares was used as the estimation method (Rosseel,2012). The statistical significance level was set at 5%(p < 0.05).
The goodness-of-fit indices included thecomparative fit index (CFI), Tucker–Lewis index(TLI), root mean square error of approximation(RMSEA), standardised root mean square errorof approximation (SRMR), χ 2 statistic (CHISQ),
and significance probability (p). They were used tocomprehensively determine the model fit. In general,
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Causal Structure of Counter Attack Skill in Soccer

Table 2 Measurement items and success criteria of the soccer counter attack skills

PhasesPenetration

SkillsPenetrative
dribbleNo Items
V01 Dribbling over 15m

Success Criteria
Carry the ball dribbling over 15m in the attacking
direction

V02 Dribbling into central Carry the ball dribbling in the central directionlane

Lay off

Penetrative pass

Disorganisation Pushing down theof theopponent’s DFL
opponent’s
defence
Disrupting the
opponent’s DFL

V03 Dribbling into open
space

Carry the ball to a position where the opponent
player cannot touch

V04 Dribbling cut

Carry the ball dribbling forward and cut in the
direction of the centre of the pitch.

V05 Receive pass in short Receive the ball closer than 2m from the passer
distance
V06 Direct pass

Pass the ball to a player with one touch

V07 Drop pass

Pass the ball to backwards for a teammate

V08 Target pass

Pass the ball to a target player who is with an
opposing defender on his backV09 Through-pass

Pass between the opponent's two defenders andbehind the opponent's DFL

V10 Forward pass

Pass the ball to a player in the attacking direction

V11 Gap pass

Pass the ball to a player who positions in the gap
between the opponent's FWL-MFL or in the gap
between the opponent's MFL-DFL
Make the position of the opponent's MFL movetoward the opponent's goal

V12 Pushing down MFL
V13 Pushing down DFL

Make the position of the opponent's DFL movetoward the opponent's goal

V14 Creating space inbehind of DFLCreate space behind of the opponents’ DFL bydrawing the opposing defender in the DFL to theball carrier.
A player receive the ball in behind of the opponent
DFL

V15 Breaking DFL
V16 Drawing DFL

Draw more than two opponent’s DFL players on theside with the ball to create an open space

V17 Disrupting DFL

Make the opponent DFL uneven (not flat)

Note: FWL stands for forward line, DFL stands for defense line, MFL stands for midfield line.

the CFI and TLI values are between 0.00 to 1.00.
Values ≥ 0.95 are considered to be a good model fit.
The value of TLI may exceed 1.0 (Kline, 2016). A
value of zero in the RMSEA and SRMR corresponds
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to a perfect model fit, while larger values indicate aworse model fit. A worse model fit includes a goodmodel fit (RMSEA ≤ 0.05) (Browne and Cudeck,
1993; Kline, 2016) and a poor model fit (> 1.00 in
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Matsuoka, H. et al.

Figure 1 Cause-and-effect/fishbone diagram of the counter attack skill in soccer
Note: DFL stands for defense line, and MFL stands for midfield line.

SRMR) (Kline, 2016).
The statistical software R and the lavaan package
were used for SEM.

3. Results
3.1. Qualitative causal structure andmeasurement item
Figure 1 shows the cause-and-effect (fishbone)
diagram of the qualitative causal structure of thecounter attack skill in soccer. The counter attack skills
were consisted of penetration and disorganisationof the opponent's defence skills according to thephase of play. The penetration skills were consistedof penetrative dribble, lay off, and penetrative
pass, and these three skills were measured by 11
measurement items, such as v01 dribbling over 15m.
The disorganisation of the opponent's defence skill
was consisted of pushing down and disrupting the
opponent's DFL skill. These two skills were measured
by six measurement items, including V13 pushingdown DFL.

3.2. Confirmatory factor structure
Figure 2 shows the confirmatory factor structureof the soccer counter attack skill. The confirmatory
118

factor structure model was constructed based on thequalitative causal structure shown in the cause-andeffect/fishbone diagram. The factor structure wasconfirmed by the goodness-of-fit and significanceof the path coefficients. The goodness-of-fit indices
showed that the CFI was 0.998, the TLI was 0.997,
the RMSEA was 0.008, the SRMR was 0.099, and theCHISQ was 104.85 (df = 103, p = 0.43). The overallmodel fit was considered to be good.
All path coefficients from the latent variables(factors) to the measured items were statisticallysignificant (p < 0.05). The path coefficients fromthe penetrative dribble to the four items ranged
from 0.532 to 0.692, the path coefficients from lay
off to the four items ranged from 0.745 to 0.853,
and the path coefficients from penetrative pass tothe three items ranged from 0.472 to 0.761. For thedisorganisation of the opponent's defence skill, thepath coefficients from pushing down the opponent's
DFL to two items ranged from 0.713 to 0.804, while
the path coefficients from disrupting the opponent's
DFL to four items ranged from 0.301 to 0.827.
For the factor correlation, the correlation
coefficient was 0.493 between penetrative dribble andpushing down the opponent's DFL, 0.393 between
the lay off and penetrative pass, 0.457 between lay
off and disrupting the opponent's DFL, and 0.548
between the penetrative pass and disrupting theFootball Science Vol.19, 114-123, 2022
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Causal Structure of Counter Attack Skill in Soccer

Figure 2 The confirmatory factor structure of the counter attack skill in soccer
Note: DFL stands for defense line, MFL stands for midfield line.

opponent’s DFL. However, there was a negative
factor correlation coefficient between the penetrative
dribble and penetrative pass (-0.319), between thepenetrative dribble and lay off (-0.383), and between
the penetrative dribble and disrupting the opponent’s
DFL (-0.198). The factor correlation between
disrupting the opponent’s DFL and pushing down the
opponent’s DFL was -0.025.

3.3. Causal structure between skills
Figure 3 shows the causal structure of the soccer
counter attack skill, which is a causal structure modelof the skills following the phase of counter attacks
constructed based on a confirmatory factor structure.
The causal structure was confirmed by the goodnessof-fit and significance of the path coefficients. The
model fit indices showed the following results: CFI =0.979, TLI = 0.974, RMSEA = 0.024, SRMR = 0.108,
CHISQ = 126.830 (df = 110, p = 0.13). The overallmodel fit was deemed to be good.
The path coefficients between the penetrative
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dribble and pushing down the opponent’s DFL were0.555, 0.588 between the lay off and penetrative
pass, 0.265 between the penetrative pass and pushingdown the opponent’s DFL, and 0.653 between thepenetrative pass and disrupting the opponent’s DFL.
The path coefficient between the penetrative dribble
and lay was -0.433. The path coefficients from thelatent variables to the 17 observed variables ranged
from 0.285 to 0.848. All path coefficients were
statistically significant (p < 0.05).

4. Discussion
The purpose of this study was to analyse thefactors and causal structures of the counter attack
skills in soccer. Qualitative analysis was conductedto construct the causal structure and measurement
items of the counter attack skills. On the other hand,the confirmatory factor analysis via SEM confirmed
the construct validity of the 17 measurement items ofthe counter attack skills in soccer. A causal structure
model that assumes a causal relationship between
119Matsuoka, H. et al.

Figure 3 The causal structure of the counter attack skill in soccer
Note: DFL stands for defense line, MFL stands for midfield line.

the first factors can be used in the post-hoc analysis
when factor correlations are found between the first
factors of the confirmatory factor structure. Thus,
the sequential causal structure model among soccer
counter attack skills that follows the playing phase ofcounter attacks was analysed via SEM.
As shown in Figure 3, there are two types ofsequential causal relationships between the soccer
counter attack skills: (1) the penetrative pass and thelay off skills related to disrupting the opponent's DFL
and (2) the penetrative dribble, the penetrative pass,and the lay off related to the push of the opponent's
DFL down.
In the penetration phase, penetrative dribble andpenetrative pass skills are related to the push downof the opponent's DFL skill. In particular, the pathcoefficient of the penetrative dribble skill to the pushdown the opponent's DFL was as high as 0.555,
which indicates a moderate causal relationship. In
previous studies, Tenga et al. (2009: 2010a; 2010b)
stated that the counter attack was an offence play120

that created a degree of imbalance through an early
penetrative pass or dribble. Hughes and Lovell (2019)
reported that dribbling in the first or the second action
after ball gain increased goal scoring opportunities.
Dribbling forward immediately after ball gain before
the opponent team creates a defensive formationis crucial in increasing offensive chances and ismeasured as the highest path coefficient from thepenetrative dribble skill to "V01 15 m dribble."This suggests that the long dribble to approach the
opponent's goal before the opposing team is organised
to force the opponent team's DFL to move backto their own goal. Therefore, it is obvious that thepenetrative dribble skill toward the opponent's goal,such as dribbling more than 15 m, is related to theskill of pushing down the opponent's DFL.
Penetrative pass skill is related to the disruptionof the opponent's DFL skill. The skill related to thepenetrative pass is the lay off skill, and there is thesequential causal relationship between the former andthe latter. Disorganizing the opposing team's defensive
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Causal Structure of Counter Attack Skill in Soccer

shape is one of the key principles of soccer attacks(da Costa et al., 2009; Hewitt et al., 2016). Moreover,the importance of measuring the interaction between
the members of the opposing team has been suggested(Tenga et al, 2009; Tenga et al. 2010a; Tenga et al.
2010b; Lago-Ballesteros et al. 2012; Matsuoka et al.
2021a). With regards to the opponent’s interaction,
Tenga et al. (2010a; 2010b) reported a low effectof the counter attacks on the opponents’ balanced
defence. In addition, penetrative passes and long
passes (> 25 m) are important for the counter attacks(Tenga et al., 2010a; Tenga et al., 2010b; LagoBallesteros et al., 2012; Hughes and Lovell 2019). In
this study, the path coefficient from the penetrative
pass skill to V09 through pass was the highest, while
the path coefficient from the penetrative pass skill tothe disruption of the opponent's DFL skill was alsohigh. Thus, penetrative pass skill is crucial to create
an imbalance in the opponent’s defensive shapes andin disrupting the opponent's DFL skill.
Lay off skills are involved in the offensive player
positions between the MFL and the DFL of the
opposing team and in the skill factors related topenetrative pass skills. The item that measures the layoff skill in this study is the V05 Receive pass in shortdistance and V07 Drop pass, which involves passing
between the opposing team's DFL and MFL defensivelines. Suzuki et al. (2018) found that attacking
through the space between the opposing defender andmidfielder was effective and was considered to be alay-off skill that led to a play that broke the opposing
team's DFL on the next action. From these results,
it is clear that the sequential causal relationship ispresent between the lay off skill and penetrative pass
skill and between the penetrative pass skill and thedisruption of the opponent's DFL skill.
A significant negative correlation coefficient of-0.433 was found between the penetrative dribble andlay-off skills. In this study, all items were measured
using the binary data, while the causal structure
model was analysed using the tetrachoric correlation
matrix. The negative factor correlation coefficients
indicated that either the penetrative dribble skill or
the lay-off skill was related to one of the factors. In
other words, according to the principle of passing or
dribbling in soccer attacking tactics, either passing
or dribbling is selected in the counter attack play.
Although situations in which the involvement of
the skill of the penetration phase remains unknown,
the findings of this study are useful for data-driven
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coaching in soccer.
The causal structure analysis via SEM shows
the sequential causal relationship among the soccer
counter attack skills that follows the phase of thecounter attacks. There is the sequential causal
relationship between the penetrative dribble skills topushing down the opponent's DFL skill and between
the lay-off skill to pushing down the opponent's DFL
skill and disrupting the opponent's DFL skill throughthe penetrative pass skill.
The correlation coefficients between the error
variable were considered to be soccer skills thatwere not explained by the model in this study.
Measurement items, V13, V14, V15, and V16 in
Figures 2 and 3 are all measures of the opposing
DFL These can represent skills in pushing down anddisrupting the opposing DFL. Additionally, thesewere not accounted for in the model in this study. For
example, V13 Pushing down DFL measures “makethe position of the opponent's DFL move toward theopponent's goal”, while V15 Break DFL measures “aplayer receives the ball behind the opponent’s DFL”.
Thus, the correlation between the error variable in
the two items shows that there is a skill involved in aseries of plays involving pushing down the opposing
DFL and breaking through it.
Based on the correlations between the error
variables of the passing-related and dribbling-related
measures in Figure 2, it can be assumed that there
are skills that require both passing and dribbling,such as pass-to-dribble and dribble-pass. The modelin this study accounts for penetrative pass skill andpenetrative dribble skill. However, the model inthis study does not account for skills that deal withboth passing and dribbling, such as pass-to-dribble
and dribble-pass. Negative correlation coefficientswere obtained between the error variables for V09
Through-pass and V06 Direct pass and between V01
Dribbling over 15m and V11 Gap pass are shownin Figure 3. Since the variables in this study were
measured using the achievement binary data, thenegative correlation coefficients indicated that when
one of the measures was achieved, the other wasnot. The soccer skills which are not explained in themodel in this study should be investigated in futurestudies.
The sample size was one of the limitations of thisstudy. The sample size for this study was 275 counterattack plays collected from 118 games that occurredin the seven leagues of the FIFA World Cup, UEFA
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Matsuoka, H. et al.

Champions League, UEFA Europe League, English
Premiere League, Spanish La Liga, and German
Bundesliga, which are among the top leagues of
the UEFA. These leagues are presented in Table 1.
The findings in this study were generalised withinthese samples. However, they cannot be applied andgeneralised to all populations involved in soccer
games.

5. Conclusion
The purpose of this study was to investigate thefactor and the causal structure of the counter attack
skill in soccer. We found that the counter attack
skills in soccer consisted of penetrative dribble, layoff, penetrative pass, pushing down the opponent’s
DFL, and disrupting the opponent’s DFL skills.
Moreover, we found that there was the sequential
causal relationship between the counter attack skills
in soccer that followed the phase of play from thepenetrative dribble skill to the pushing down of the
opponent's DFL skill, from the lay-off skill to thepenetrative pass skill, from the penetrative pass skill
to the pushing down of the opponent's DFL skill, andfrom the penetrative pass skill to the disrupting the
opponent's DFL skills.
The limitations of this study include the sample size,
measurement items, and statistical analysis methods.
From the perspective of measurement science, followup research on the analysis of the counter attack skills
and measurement items is necessary as these develop
over time.
Note*1 A lay off is a play in which the same team player
in the space between opposing defensive players
who receives a short pass drops it with one touch to ateammate facing forward.
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Football Science Vol.19, 114-123, 2022
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Causal Structure of Counter Attack Skill in Soccer

Name:
Hiroki Matsuoka
Affiliation:
Faculty of Health and Sport Sciences,
University of Tsukuba

Address:
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8574 Japan
Brief Biographical:
2015-2017 Master’s Program in Health and Sport Sciences,
Graduate School of Comprehensive Human Sciences, Universityof Tsukuba
2017-2020 Doctoral Program in Physical Education, Health andSport Sciences, Graduate School of Comprehensive Human
Sciences, University of Tsukuba
2021-2022 Faculty of Health and Sport Sciences, University ofTsukuba
2022-present R&D Center for Sport Innovation, University ofTsukuba
Main Publications:• Matsuoka, H., Tahara, Y., Kozue, A., and Nishijima, T. (2021).
Development of criterion-referenced measurement itemsof defensive transition in soccer games from tracking data.
International Journal of Sport and Health Science, 19: 87-97.
• M a t s u ok a , H ., Koz u e , A ., a n d Ni s h iji m a , T. (20 21).
Identification of soccer defensive transition play using latentclass analysis. Football Science, 18: 60-71.
Membership in Learned Societies:• Japan Society of Physical Education, Health and Sport Sciences• J apanese Society of Science and Football

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