Jyo_sen2021 (Japan) 2D Soccer Simulation League Team
Description Paper
Minoru Mochizuki1, Takashi Kataoka, Riku Ueda, Ryoya Tatenuma, Ken Sugihara,
Masayoshi Nakamura
Akihabara Programming School, Japan
1mmocchi1@gmail.com

Abstract. This team description paper introduces the resent research theme ofthe team Jyo_sen2021. The team is now working on the analysis of soccer formations. We investigate which soccer formation is more efficient than the others,by comparing formations used by professional soccer teams and those used atancient battle fields in Samurai-era. In addition, we develop an automated test
program called cszp for efficient analysis.
Keywords: Formation analysis, Automatic test program, cszp: Easy soccer execution program.

1Introduction

Jyo_sen (Akihabara Programming School Soccer Club) consists of approximately 10
members from junior high school students to adults. Most of the members study deeplearning at AI programming courses, but there is a limit to improve the programmingskills without applied problem solving challenges such as artificially playing soccergames. Therefore, by starting a soccer club to participate in RoboCup 2D simulation,
we are improving programming skills and enjoy communication beyond generations.
Jyo_sen is founded in 2018 and participate in Japan Open 2019 held in Nagaoka for thefirst time. Our team ranked 5th out of 10 teams. Jyo_sen2019 described in this paper iscreated based on agent2d (release 3.1.1) made by H. Akiyama [1,2]. Jyo_sen2021,
which is scheduled to participate in RoboCup2021, will be created based on Gliders2d[3,4] with reference to the movement of each player in the team HillStone code[5].
The team name "Jyo_sen" comes from the traditional phrase "Jyozai-senjo (常在戦場)"
that means "You're always in the battle field". There was a legendary samurai, T. Kawai
who was a commander of last army troops of Edo Shogunate. He respects this phrase
and is known as the final samurai.

22Analyzing performances in various formations

We wanted to strengthen our team using the reinforcement learning described in theFuture Works section, but we just didn't have the skills to write reinforcement learning
programs yet. So, we decided to do something at the level we could do, and decided toinvestigate the performance impact of changing the formation.
At that time, the soccer formations used by professionals alone were boring, so wefound it interesting to include the formation of the Samurai period related to the team
name, "Jyozai-senjo (常在戦場)".
We analyzed the performance differences in soccer formations such as win rate andgoal difference. We create three types of formations based on actual soccer and one
type formation based on battles in the Samurai period.

2.1

Description of the formation

We created the following formations based on actual soccer; "4231.conf" based on the4-2-3-1 formation, "433.conf" based on the 4-3-3 formation, and "442.conf" based on
the 4-4-2. As a defensive formation, we created “df433.conf” based on the 4-3-3 formation [6].
The representative battle formation in Japan is the Takeda Hachijin formation. The formation is composed of eight types, Gyorin (魚鱗), Kakuyoku (鶴翼), Engetu (偃月),Houshi (鋒矢), Houen (方円), Chouda (長蛇), Kouyaku (衡軛) and Gankou (雁行)(Figure 1). This time, we created a Gyorin formation with reference to Gyorin (魚鱗)(Figure 2).
Gyorin, which literally means “fish scales”, is a formation in which the center protrudes
forward and both wings recede. Gyorin maintains the overall defense performance,
while keeping the mobility of each small unit, consisting of a certain number of people.
It is said in history that Shingen Takeda used the Gyorin formation to defeat Ieyasu
Tokugawa in the battle of Mikatahara. At the Rugby World Cup 2019, the platoon All
Blacks took in the pre-game performance resembled a platoon of Gyorin.

3(Source: Wikipedia)
https://ja.wikipedia.org/wiki/%E9%99%A3%E5%BD%A2
Fig. 1. Takeda Hachijin formation

Fig. 2. Gyorin formation

4

2.2

Test method and results

Jyo_sen2019 takes an offense position when an ally team member is closer to the ballthan an opponent team member by two or more cycles. Also, it takes a defense position
when the opponent team member is closer to the ball than one of its own team membersby two or more cycles. When neither of two applies, it takes a normal position.
In the offense positions and normal positions, four types of formations are set:"of4231.conf", "of433.conf", "of442.conf" and “Gyorin.conf”. In the defense position,“df433.conf” is commonly used. Jyo_sen2019 and agent2d (release 3.1.1) played 300
matches, and the following results were obtained (Table 1).
Table 1. Performance by formation
Formation

4-2-3-14-3-3
4-4-2
Gyorin

Won

204
200
152
176

Lost

45
53
7775

DrawWin rate

Jyo_sen2019

Agent2dGoal
Difference

51
47
71
49

0.82
0.79
0.660.70

3.12
3.28
2.16
2.72

1.58
1.85
1.60
1.83

1.54
1.43
0.56
0.89

The best win rate was observed with 4-2-3-1 formation, followed by 4-3-3, Gyorin, and4-4-2. It is thought that 4231 and 433 are strong because of the balance between offense
and defense. 442 is weakest because it is too defensive and scored poorly. Gyorin was
the third despite the fact it was one of the best formations in battle fields. Gyorin formation may not be very efficient in soccer because in a battle field, people and horses
move with their feet and approach the opponent's general, whereas in soccer the teamapproaches the opponent's goal mainly with passes.

3cszp

A dedicated program is required to run hundreds of tests. Therefore, we have developed
a program called cszp to perform tests easily (Figure 3). cszp was developed by a juniorhigh school member of the team .
The program is CUI based and supports two languages: Japanese and English. Whenloop mode is selected on the start screen and the number of games and server arguments
are input, the test starts, and data such as game time, team name, score, goal loss, andscore difference can be output in CSV file. It also supports sync mode [7].
The data in the CSV file can be analyzed using a browser during test execution (Figure4). You can also view the past log after the test by selecting server mode on the start
screen [8]. At the time of writing, cszp was 5.4.0, but we will continue to update it tomake it easier to use.
GitHub: https://github.com/kumitatepazuru/cszp

5Fig. 3. CUI Interface

Fig. 4. Analysis screen

64Future Works

We plan to learn AI by incorporating the following automation technologies and machine learning [9].
 Construction of automatic parameters tuning system
 Make some agent2d features (Dribble, etc.) a learning base, such as reinforcement
learning

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