Journal of Robotics, Networking and Artificial Life
Vol. 5(3); December (2018), pp. 199–203
DOI: 10.2991/jrnal.2018.5.3.12; ISSN 2405-9021 print; 2352-6386 online
https://www.atlantis-press.com/journals/jrnal

Research Article

Analysis of Team Relationship using Self-Organizing Map for
University Volleyball Players
Yasunori Takemura1,*, Kazuya Oda2, Michiyoshi Ono1
Department of Engineering, Nishinippon Institute of Technology, Miyako, Fukuoka 800-0344, Japan
Department of Life Science and System Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka 808-0896, Japan

1
2

ARTICLE INFOABSTRACT

Article History

In Japan, sports efforts are actively being carried out to host the 2020 Olympic Games. Especially in the field of sports science,researches on ergonomics, development of sports equipment and pattern recognition technology using artificial intelligence are
actively researched. In previous research, we developed a clustering algorithm for positioning adaptation and relationships inteam sports using self-organizing maps in university rugby players. However, I have not yet confirmed whether the developed
algorithm can be applied to other team sports. For this reason, we applied the same algorithm to a university volleyball player.
Then, as an algorithm, we verify whether it can be generally used for team sports.

Received 9 April 2018
Accepted 15 November 2018
Keywords
Sports scienceSOMmachine learning
clustering© 2018 The Authors. Published by Atlantis Press SARL.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
In Japan, sports efforts are actively being carried out to host the2020 Olympic Games. Especially in the field of sports science,researches on ergonomics, development of sports equipment
and pattern recognition technology using artificial intelligence
are actively researched. The budget of Japanese sports in 2016 is
32.4 billion yen, and considering that the budget for 10 years ago
was 19 billion yen, it is a doubling momentum.
By the way, victory or defeat in team sports depends on eachplayer’s individual technique, physical strength, and psychological condition. Similarly, it can be said that the suitability of anindividual to a certain position in the team affects the team’s
performance [1].
In the previous research, Barry and Cureton [1], Nick and Fleishman[2], Larson [3], McCloy and Young [4] and others clustered physicalfeatures and conducted factor analysis in investigating sports performance. In Japan, Tokunaga [5] studied the diagnosis criteria forathletic adaptation (i.e., suitability) in sports. These works showedthat physical features are one of the strongest factors determining
athletic adaptation. However, Matsuda and Singer [6] showed thatan athlete, no matter how good his/her physical features, is not athletically suited to team sports without having good motivation interms of setting goals and training. That is to say, for an individual
or team to be successful, a player needs to have both good physicalfeatures (e.g., techniques, balance, height, and weight) and good
psychological features. For example, Saijo et al. [7] presented thepsychological features of Japanese and New Zealand rugby players.
* Corresponding author. Email: takemura@nishitech.ac.jp

In this way, the suitability of a player in a certain position andthe relationships between different positions in team sports
are related to physical and psychological features. As it stands
now, a coach or selector decides the player suitability andrelationships between positions him/herself. However, does it
follow that good decisions are made? Previous research has notclarified athlete adaptation to positions and relationships
between positions considering both physical and psychologicalfeatures [8].
In the present study, we develop a clustering algorithm for positioning adaptation and relationships in team sports. We consider
the two main types of features, which are physical and psychological features, and introduce the concept of using an algorithm tocluster player features in team sports. In previous works, we developed clustering algorithm using self-organizing maps (SOM) forteam relationship map [8,9]. Previous work is adapted in university rugby players. However, I have not yet confirmed whether thedeveloped algorithm can be applied to another team sports. For
this reason, we applied the same algorithm to our volleyball players. Then, as an algorithm, we verify whether it can be generally
used for team sports.

2. RESEARCH PLAN AND METHOD
2.1. Selection of SportWe apply a team-sport clustering algorithm to volleyball.
Volleyball is selected because a volleyball team is one of themost necessary cooperative works and interaction between aplayer and teammates.

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2.2. Describe the Physical Data andAnalysis Using Statistics
Physical data of members of the N university volleyball team wererecorded in 2016 December. The N university volleyball team comprised 22 students. There were three RTs (right), six LTs (left), seven
CNTs (center), two STs (setter position) and four LBRs. Physicalfeatures recorded were height (cm), weight (kg), grasping power(left hand, right hand) (kg), ante flexion (cm), toe touch (cm),jumping side-to-side (times), standing long jump (cm), 9 m dash(three round trips) (s), vertical jumping (cm), and back muscle
strength (kg). The data set thus had 11 physical dimensions. Data
showing statistically characteristic results could be shown below. It
shows the adaptation result of the position derived from the data.
Figure 1 shows the height data. The horizontal axis denotes individual positions. The vertical axis shows the height (cm).
As you can see from Figure 1, LT, RT and CNT show that players withrelatively high stature are selected, and ST and LBR can be said that
it is not necessary to be a player with a high height. This shows thatthe height of LT, RT and CNT is required as an attack such as attack.
Figure 2 shows the result of jumping side-to-side. The vertical axis
shows the how many times they can do (times). The horizontal
axis denotes individual positions. As you can see from the Figure 2,
it shows that only CNT shows a large value. In other words, it canbe seen by looking at this figure that the reflexes are necessary for
the CNT position. In this way, the suitability of the position can beunderstood by analyzing several items statistically, but in many items
there is not a big difference, it is impossible to decide suitability ofthe position unconditionally. Also, since the number of population isdifferent as statistical data, it is difficult to find the suitability of theposition only by comparing statistical data unconditionally.

2.3. Describe the Psychological Data andAnalysis Using Statistics
Psychological data of members of N university volleyball club wererecorded in 2016 December. The N volleyball club in 2017 comprised 22 students (There are same members as physical data.).Psychological features measured were those of the Diagnostic Inventoryof Psychological Competitive Ability for Athletes (DIPCA.3) [8].
DIPCA.3 measures 12 types of psychological ability in a 48-item questionnaire. DIPCA.3 is often used before mental training, because it
reveals athletic strengths and weaknesses. The DIPCA.3 check sheet
consists of 48 questions that measure psychological ability and fourquestions that measure reliability. These questions have already been
analyzed by good–poor analysis (G–P analysis), and the answers provide 12 criteria relating to five factors. The factors are motivation in
sport, mental stability and concentration, confidence, operation capability and cooperativeness. Additionally, we measure the reliability of
the answers by comparing answers to similar questions. To examine
these factors in detail, 12 criteria are described. Motivation in sportconsists of four criteria: endurance, fighting spirit, self-realization
motivation and motivation to win. Mental stability and concentrationconsist of three criteria: the ability to relax, capacity to concentrateand self-control. Confidence consists of two criteria: determinationand confidence. Operation capability consists of two criteria: predictive capability and judgment. Cooperativeness has only one criterion,
which is simply described as cooperativeness. DIPCA.3 provides a total
score (ability). Each criterion is scored, and the score of each factor isthe sum of scores for the related criteria. The total score is calculated
by summing the score for each factor. Table 1 gives the total scores,
which are classified into five levels. A classification of one indicates avery low overall mental ability and a classification of five indicates avery high ability. Each mass describes the total score. The methodology of DIPCA.3 is such that each person answers the 52 questions on ascale of 1–5 (1: I don’t think so, 2: I rarely think so, 3: I sometimes thinkso, 4: I often think so, 5: I always think so) within 10–15 min.
Figure 3 shows the evaluation result of DIPCA.3. The horizontal axis denotes each evaluation criteria. The vertical axis shows

Figure 1 | Heights of volleyball players (N university 2016 data)

Figure 2 | Jumping side-to-side result of volleyball players (N university
2016 data)

Table 1 | List of the variables used in a SOM algorithm
Parameter

DescriptionqxyTraining data
Input vector (physical features) [j = 1–19]
Input vector (psychological features) [j′ = 1, …. 12](12 criterion scores)
Index denoting class (i = 1, /…, l)Reference vector
Index expressing unit (k = 1, …, K)
Distance between input and reference vector
Best matching unit
Neighbor function
Euclidean distance between a and b
Learning rate
Neighbor radiusTime constant

i
w
k
E
k*
f
d(a, b)yst

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are calculated. The upper suffix k is the number of units and i is theindex of the data set [Eq. (2)].
Eik = w k −q k2(2)

3.2. Competitive Process
In the competitive process, to find the best matching input vector q forreference vector w, the best matching unit (BMU) (k*) is selected in
Eq. (3). That is, the closest environment to q is selected in the w space.
ki* = arg k min Eik

Figure 3 | Psychological result using DIPCA.3 (N university 2017)

evaluation value. Each evaluation maximum value is 20 (point). The
color bars describe the each position (LT, RT, CNT, ST, and LBR).
Comparing the psychological feature quantities, we can see that
there is little difference between the positions. On the whole,
however, RT knows that athletes with very psychological characteristics are selected. Moreover, as a whole, the ability as fighting
sprit, self-realization and motivation to win is high in the team as awhole. Meanwhile, items such as confidence, deamination, endurance, and judgment need to be lowered in capability as whole teammembers.

3. PROPOSAL ALGORITHM USING SOM
The analysis of physical and psychological features indicates thatthe conventional statistical method cannot reveal player adaption topositions in team sports and relationships between positions [8,9].
We therefore propose a new method using SOMs. An SOM is a topologically correct feature map proposed by Kohonen and is well known
as an attractive tool for extracting the characteristics of data and classifying data into clusters through its self-organizing process. The
brains of many higher-order animals appear to achieve a topologicalrelationship through a stream of sensory inputs, and several algorithms have been suggested to duplicate this neural processing. The
SOM has an algorithm that is capable of establishing a feature map bylearning a random sequence of input samples. Kohonen’s algorithm
can be represented in a simple iterative form, thereby demonstrating its computational power. The SOM is an unsupervised learningalgorithm for generating a topological transformation from a high-­
dimensional data vector space to a low-dimensional (usual one- or
two-dimensional) space. Higher-dimensional maps are also possible,
but are not discussed here. The neurons are programmed for various input patterns or classes of input patterns through a competitive
learning process. The SOM algorithm includes four processes: anevaluation, competitive, cooperative, and adaptive process. Table 1
lists the variables used in an SOM algorithm. The training data aredefined by Eq. (1), with suffix i denoting the input data set. x and y
are normalized from −1 to 1 for input into the SOM algorithm.
q i = (x j , y j ′ )

(1)

(3)

3.3. Cooperative Process
The learning quantity for each module is calculated according to
Eq. (4). The learning distributional rate y ik expresses the quantity
to be distributed to the k-th unit with regard to the i-th data class.
Here, the neighborhood function f (.), like a Gaussian function,
decreases monotonically with an increase in the distance functiond(.), and the iteration of learning t is selected.
Let d(k, ki* ) denote the Euclidean distance between the k-th unit
and ki*, which is the BMU for the i-th data class. The amplitudeof the topological neighborhood decreases monotonically withincreasing distance d(k, ki* ).
d(k, ki* )2	 fi k = exp
2s 2

(4)

The parameter s is the effective width of the topological neighborhood, and is called the neighbor radius. The use of s allows the SOM
algorithm to converge more quickly than a rectangular topologicalneighborhood function [10,11]. The neighbor radius s [Eq. (5)] of
the topological neighborhood function decreases with time.
 −t 
s = s min + (s max − s min )exp  
t 

(5)

In a batch-type SOM, the learning rate of each unit is defined byky ik , which is normalized by the sum of the f i [Eq. (6)].
y ik =

f ik∑ i ′ f ik′(6)

3.3.1. Adaptive process
In the Kohonen’s SOM, all unit vectors are adjusted using Eq. (7) in
the adaptive process.
w(t + 1) = w(t ) +y ik (q i - w k )(7)

For the adaptive function, Eq. (8) is introduced instead of Eq. (7)since Eq. (8) is known to achieve faster convergence for a batchtype SOM.

3.1. Evaluation Process	   w(t + 1) = åy i q i

In the evaluation process, qi is input into all units, and the squaremean errors (differences) Eik between qi and reference vector wk

The SOM algorithm generates feature maps in two dimensions following processes (1)–(4). Each lattice cell describes a unit.

k

i

(8)

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4. EXPERIMENTAL RESULT
To evaluate the performance of the SOM algorithm, individually, atfirst, only x input data sets (only physical data sets) are used sameas previous works [8]. Figure 4 shows the feature maps used for the2017 physical data sets. Each lattice cell describes a unit. If a unit is
the BMU for a data set, it is labeled as such as 1st line: number ofplayer and 2nd line: position. The color of the lattice indicates the
Euclidian distance to the neighboring unit (Figure 4) and Figures
5 and 6 are described vector values. For example, blue indicatesthat the neighboring unit is nearby; red indicates that it is far away
(Figures 5 and 6 are high score), and green indicates that it is at
mid-distance (Figures 5 and 6 are low score).
Figure 4 shows the feature map of physical data. For example, on
the left side of the feature map, attack role people is gathered. On
the other hand, right side is gathered to deffensive positon people
and ST position. Figure 5 shows the same feature maps, however,the color label describe about the height. Looking at this figure,high-height (red lebeled) players are gathered on the left side, andplayers with relatively low height are gathered on the right side.
That is, the same result as statistical data can be obtained also in
the feature map using SOM. Next, Figure 6 shows the results ofcoloring the results of jumps side-to-side. The jumps side-to-side
does not have a relatively large difference. However, it turns out that

Figure 4 | The feature maps which is inputted physical data sets

Figure 5 | The feature maps of physical data sets which is colored byheight data

the CNT person shows a relatively high numerical value (green). In
this way, similar results to statistics can be found in SOM feature
map. However, the suitability of the position within the team was
not able to obtain distinctive results even if examining other items.
Second, we input y data (psychology data sets). Figure 7 shows thefeature map which is inputted psychological data sets. Each latticecell describes a unit. If a unit is the BMU for a data set, it is labeled
as such as 1st line: number of player and position and 2nd line:
total score evaluation (1: low level to 5: high level using DIPCA.3
statistics result). In this figure, right up side is gathered to low-levelplayers. On the other hand, left bottom side is gathered to highlevel people. Therefore, players in the upper right corner of the map
are mentally immature players and need mental training. On thecontrary, many of the players belonging to the lower left are elected
regular members, and the director also knows that they consider
mental reach. In addition, because mental elements are still being
analyzed, we will explain the details in the conference.

5. CONCLUSION
In this paper, we developed team relationship clustering algorithm-­using SOM for volleyball team players. In previous works, weFigure 6 | The feature maps of physical data sets which is colored by jumpside to side

Figure 7 | The feature map of psychological data sets result

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analyzed team relationship clustering algorithm-using SOM for
university rugby players [8,9]. However, I have not yet confirmed
whether the developed algorithm can be applied to another teamsports. For this reason, we applied the same algorithm to our volleyball players.
In previous research [8,9], we succeeded in electing the positionof the rugby players using the physical data sets. From the psychological data sets, we were able to find out what kind of people theregular members are. In addition, if two data groups are used comprehensively, the suitability of positioning and the selection of regular members were possible. In this paper, we confirmed whether we
can obtain the same knowledge as statistical data by using physicaldata group according to the method. We also examined whether statistically similar results can be obtained for psychological data groupas well. In the future, we need to analyze the suitability of the position
and the relationship with the company. We also plan to analyze integrated physical data group and psychological data group.
In addition, as a future task, it is necessary to consider a method
for general use because there is a difference in results, comparedwith the result of the clustering in the rugby player performed in
the preceding research [8,9] and the result of the volleyball player.

ACKNOWLEDGMENT
This work was supported by a Grant-in-Aid for Young Scientists(B) 15K21570 from JSPS KAKENHI.

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Authors Introduction
Dr. Yasunori Takemura
He graduated Ph.D. in Kyushu Instituteof Technology, Japan in 2010 and now heis working as an Associate Professor at
Nishinippon Insitutute of Technology, inJapan. His research area is about machinelearning, data mining and robotics.

Mr. Michiyoshi Ono
He received his bachelor degree of engineering from Nishinippon Institute ofTechnology, Japan in 2018. He is currently
doing Master degree student of engineering at Kyushu Institute of Technology, inJapan.

Mr. Kazuya Oda
He received his bachelor degree of engineering from Nishinippon Institute ofTechnology, Japan in 2017. He is currently doing Master degree studentof engineering at Kyushu Institute ofTechnology, in Japan.