日本オペレーションズ・リサーチ学会
2021年 秋季研究発表会

1-D-5

Analysis of soccer player’s activity profiles using deep learning technique
申請中
01506960

Juntendo University
Juntendo University
Juntendo University
Juntendo University

1. Introduction
Recently, with the development of technology, themeasurement of physical data using wearable devices has
become a common in the field of team sport coaching.
Wearable devices are now widely used to understand thephysical demands of soccer matches (Krustrup, Mohr,Ellingsgaard, & Bangsbo, 2005). The devices contain
Global Positioning System (GPS) and inertial
measurement units (IMU) with accelerometers,gyroscopes, and magnetometers which can quantify themovements of non-running-based work such as
acceleration, deceleration and change of direction.
Activity profile (Suarez-Arrones et al., 2015; Varley,
Fairweather & Aughey, 2012) and time-motion analysis(Castellano, Blanco-Villasenor & Alvarez, 2011) are themajor research tools using wearable devices as they canmeasure a large number of players simultaneously andanalyze them in a short time. Although the data taken withwearable devices has been used to measure the items, notmany applications of machine learning methods such as
deep learning to analyzing the raw time-series data has
been reported so far in terms of soccer. The aim of thisstudy was to classify the activity profiles of soccer players
during a game based on the time-series data measured by
wearable devices utilizing a Fast Fourier Transform (FFT)
method. If detailed analysis using raw data can beconducted through this research, it will be possible toanalyze the movement patterns of players from the dataalone, which will enable analysis in a shorter time than theconventional method of analysis that requiresconsiderable time and effort and will contribute to thecoaching field.

2. Method
2.1 Subject and match data
Eighteen collegiate female soccer players (age: 20.3 ±1.29 years; height: 161.1 ± 5.65 cm; body mass: 55.6 ±6.24 kg) from the same college league team participatedin this study. Goalkeepers were excluded from this study.
Eight matches (weather: sunny or cloudy; temperature:
22.9 ± 4.45 ℃; humidity: 62.7 ±15.1 %) of the 33rd Kanto
University Women Football League 2nd Division in 2019were examined. The match times were a total of 90
minutes with 45-minute halves and a 15-minute interval
between the halves.

* MASUI Yuki
HIROTSU Nobuyoshi
SHIMASAKI Yu
YOSHIMURA Masafumi
2.2 Data measurement and analysis
Activity data from IMU were collected using wearable
devices (OptimEye S5 and G5, Catapult Sports, Australia)
operating at a sampling frequency of 100 Hz. By
combining the obtained information, we could detect theinclination and direction. Thus, it was possible to measure
not only the acceleration and deceleration frequencies but
also the right and left movement frequencies.
The players wore special harnesses that enabled thesedevices to be fitted to their upper backs. To extract andanalyze the data of only those players who competed in
the match, the match start time, the first half end time, thesecond half start time, and the full time were recorded
based on the ratio clock. Moreover, the time of a player
exiting and coming on as a substitute was regarded as hertime of leaving and entering the soccer field, respectively.
After recording, the data were uploaded into a PC andanalyzed using the software package (Openfield version
2.2.0, Catapult Sports, Australia). After analyzing with thespecific software, we extracted 100 Hz raw data andperformed a FFT of the data with Python to analyze themovement patterns of each player. The items to beanalyzed were the value of acceleration for the x-, y-, andz- axes. After analyzing the data, we compared the video
to the data to see what movements were measured. Using
the data transformed by FFT, we have been trying toidentify event or play according to each part of time series
data.
To identify the event or play we apply unsupervisedclustering method of deep learning, aiming to group theFFT data into classes entirely without labels. Here, we try
to cluster any kind of unlabelled paired data (X, X’) bytraining a network (Φ) to predict cluster identities.

3. Result and discussion
In order to evaluate the analysis of players duringmatches based on the above models, we needed to divide
100 Hz raw data measured over 90 minutes into 3 seconds
intervals. Figure 2 shows an example of the time-series
acceleration data and its FFT when the player cleared theball.
As shown in Figures 2, we can see the pattern of theamplitude of frequency between directions. We can also
see the difference of the patterns between plays appearedin the game.

1

In addition to Figures 2, we were able to analyze moretypes of the movement. For example, we were able toanalyze movements of contact with opposing players,
running speed changing to fast speed, driving a cross andthrow-in and so on.

OutputInput

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4. Conclusion and Further study
In this paper, we have used the FFT method to analyze
the movement patterns of each player from 100 Hz rawdata. As a result of analysis, this procedure could make itpossible to identify each movement pattern appeared in
the game without watching a video.
In this paper, we have just analyzed the situation of playfrom 100Hz raw data using FFT. In order to contribute tosoccer teams that use wearable devices to measure data, itwill be necessary to analyze data about every movement
during a soccer game in the future. As this study is still inprogress, we plan to present more in the conference.

Acknowledgement
151
Input
Layer

128

64 64 128
10Hidden
Output (Fully Connected)
Layer
Layer

Figure 1. Example of a simple deep network architectureThis work was supported by JSPS KAKENHI Grant
Number JP19K04911, Japanese Center for Research on
Women in Sport of Juntendo University, and Institute of
Health & Sports Science & Medicine, Juntendo
University. We acknowledge the players and coaching
staff of Juntendo Women’s Football Club for theircontribution to the study.

acceleration(m/s)

amplitude(V/rtHz)

References

Frequency(Hz)acceleration(m/s)

amplitude(V/rtHz)TimeStamp

Frequency(Hz)acceleration(m/s)

amplitude(V/rtHz)TimeStamp

TimeStamp

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Frequency(Hz)Figure 2. Acceleration of the player cleared the ball (Top: xaxes, Middle: y-axes, Bottom: z-axes)2