MT2021: Team Description Paper
Linsheng Guan, Qiuna Chen, Zengcheng Shi, Wenlong Lv, Shengbing Chen
College of Artificial Intelligence and Big Data,Hefei University,Anhui Province, P.R.China, 230601
hfuumt@163.com

Abstract. MT2021 is a team of 2D soccer simulation league which is consisted of the students
who are coming from Hefei University and all of them are with strong robot enthusiasm. Since
2012, the MT2021 team has participated in RoboCup China open tournament, since 2015, ourteam has participated in RoboCup World Cup every year and has achieved many good results.
This paper briefly describes the background of MT2021 and the main works of our team sincethe 2019 RoboCup World Cup. Through these works we have greatly improved the competive
ability of our team.
Keywords: RoboCup, Log data mining, Through pass, Formation strategy,
Field_evaluator

1Introduction

MT2021 comes from Hefei University, China, and has actively participated in various
RoboCup competitions, including China Open, Iran Open and Portugal Open. Since
2015, our team has performed well in the Robot World Cup, entering the final eight ofthe 2D simulation group for five consecutive sessions, and has been recognized bymany powerful teams and friends in the world. Our major achievements in recentyears include: Champion in the 2016 Portugal Open, 3rd Place in the 2018
Robocup2D World Cup, 5th Place in the 2019 Robocup2D World Cup, etc. Throughextensive training, testing, and log data mining, we found some deficiencies in theteam and optimized the team code in many ways. We hope that the improved code
will work well in this year's competition and improve the level of team play.
We hope to present our latest research results and team technology in Robocup2dworldcup competition in 2021, achieve better results, and be able to actively discuss,learn and communicate with other teams through the competition.

2MT2021 Description of the underlying

We use the underlying version is agent2d - 3.1.1, download address is: http:// en.sourceforge.jp/projects/rctools/. The underlying way to use the chain of actions, using
librcsc as the underlying library. We modified and optimized MT2021 on the basis of
MT2020.

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Python-based game log data mining

The rcg and rcl log files generated by the team during the game record the position,
speed and player positions of the ball during each period of the game. In order to bet ter analyze the offensive and defensive characteristics of ourselves and other teams,
we used Python to write scripts to mine the information we needed from the log files,
which was convenient for data analysis and targeted optimization of team code.
3.1

Pass analysis

Extract all pass-related information from rcg and rcl files and draw the pass-related
information with Matplotlib. We assume that if the current holder unum and side(sides are divided into left and right, left means the team that is attacking from left toright and right means the team that is attacking from right to left.) are the same as thenext holder unum and side in a certain period, the same player is considered to be carrying the ball. If the unum of the ball handler changes and the side remains the same
during the following period, the ball is considered to have been passed to one of histeammates. If the side changes, the pass is considered a failure. Figure 1 shows theschematic diagram of passing information in the game logs of MT and multiple
teams. The black dot represents the position of the ball on the field before passing theball. A red cross indicates a failed pass; A green cross indicates a successful pass. Table 1 shows the analysis of the pass success rate in 100 matches between MT and
multiple teams.

Fig. 1. Passing path analysis.

3

Table 1. Passing success rate analysis.
Analyze pass success rates from 100 games
team

success
rate

total
passesnumber of
successes

number offailures

MTvsFractals2019_MT

0.8255

19306

15938

3368

MTvsFractals2019_Fractals2019

0.803417333

13926

3407

MTvsHELIOS2019_MT
MTvsHELIOS2019_HELIOS2019
MTvsCYRUS2019_MT

0.8548

18668

15957

2711

0.8512

18439

15695

2744

0.9194

23160

21293

1867

MTvsCYRUS2019_CYRUS2019

0.9033

18024

16281

1743

MTvsYuShan2019_MT

0.8507

18039

15346

2693

MTvsYuShan2019_YuShan2019

0.8527

18631

15886

2745

From Figure 1 and Table 1, we can see the passing characteristics and passing success rate of the analyzed team, and then analyze the offensive and defensive playing
methods of the team.MT has made corresponding defensive strategies for different
teams' passing styles, and its defensive ability has been improved.
3.2

Analysis of the opponent's shoot action

Shoot is the most important action in a football game. It is the most important mea sure of a team's strength. Using data mining algorithm to shoot analysis can help us tounderstand the shooting characteristics and shooting ability. We extracted information
about shoot from a large number of MT and other teams' game log files, such as thecoordinate of our defensive player, the coordinate of the goalie, the coordinate of theshooting point, the coordinate of the goal point, the Angle of shooting, the partial velocity of the x coordinate when shooting, the partial velocity of the y coordinate whenshooting, the partial velocity of the x coordinate before the goal, and the partial velocity of the y coordinate before the goal. At present, we have excavated and analyzed
the successful shots of several teams, and obtained their customary range distance,shooting point coordinates, speed, Angle, and goal point coordinates. Figure 2 is a diagram of analyzing the shooting path of the opponent, in which the red cross represents the shooting point, the green cross represents the goal point, and the blue line
represents the shooting path. Figure 3 is a graph analyzing the length of shooting path,where the abscissa represents the range and the ordinate represents the number oftimes that happens. Figure 4 is a graph analyzing the shooting speed and goal speed.
The abscissa of the upper figure in Figure 4 represents the shooting speed, the abscissa of the lower figure in Figure 4 represents the speed before the goal goes in, ordinate represents the number of times that happens.

4

Fig.2. Shot path analysis.

Fig. 3. Shot distance analysis.

Fig. 4. Shooting speed and goal speed analysis.

5

Based on the analysis of the shooting data of each team, we get the shooting characteristics of them. In terms of range, CYRUS2019 has many long-range shots outside the big penalty area compared with other teams, and its average range is thelargest. In terms of shooting speed, Fractals2019 has the fastest average shot and HELIOS2019 had the highest shooting frequency. And timely to the MT goalie and defense players in the penalty area to make targeted adjustment, including the goalie anddefense players running, marking, intercept, pouncing and other actions, from the current test results, the defensive effect compared with the previous better.
3.3

Analysis of opponent running position characteristics

We extracted the opponent's position on the field from the match log file. The movement path of the opponent in the match is plotted, as shown in Figure 5, where the redline represents the full-court running path of No. 9 player of other teams. Through therunning path diagram, we can directly see the running situation of the players on thefield. It can help us to understand and analyze the characteristics of opposing players'
offensive and defensive running positions, and adjust the MT's offensive and defensive strategy rate accordingly. After we revised and debugged the code in a timely
manner, MT's offensive and defensive movement is much more targeted than before.

Fig. 5. Analysis of opponent movement.
3.4

Stamina analysis

Every player on the pitch has a ceiling on his or her stamina. Every player uses a lotof stamina, so he or she needs to use his or her stamina in a more reasonable way. For
example, the stamina of the midfield player is much larger than that of other players.
When approaching the end of the first and second halves, it is easy to appear physical
overdraft and become zero. In particular, MT's defensive midfield number 6 in the433 formation was prone to the energy pool (That's the total amount of stamina thatcan be invoked) turning to zero towards the end of the first and second halves. We extracted the energy value information of MT and other teams from the log files of a6large number of matches. After processing, as shown in Table 2, it analyzed the energy pool situation of player No. 6 when the 5999th cycle in 100 matches between
MT and other teams. From Table 2, It can be seen that YuShan2019's No. 6 has thelowest average energy pool. HELIOS2019 has the highest average energy pool, andthe energy distribution is reasonable. We have adjusted the position code of No. 6 ofMT specifically to reduce unnecessary physical energy consumption and allocate
physical energy more reasonably. Other players have also made adjustments. Afterour tests, we found that players were able to use their energy more rationally than be fore, and that individual players had less unnecessary energy expenditure.
Table 2. Stamina analysis.
Analyze number 6 player's stamina(5999 cycle) from 100 games
team
average total stamina total number
MTvsFractals2019_MT
41857.69 4185769.28
100

4

MTvsFractals2019_Fractals2019

356.3335632.73

100

MTvsHELIOS2019_MT

2898.04

289803.77

100

MTvsHELIOS2019_HELIOS2019

19374.73

1937473.23100

MTvsCYRUS2019_MT

5682.06

568206.49

100

MTvsCYRUS2019_CYRUS2019849.94

84993.9

100

MTvsYuShan2019_MT

1319.39

131938.95

100

MTvsYuShan2019_YuShan2019

271.7527175.34

100

Further improvements to the through pass

Through pass is a common technique used by the attacking team to penetrate the opposing team's defensive formation. The essence of this is to find open positions in thedefensive formation and play through passes, as well as diagonal runs. The ball isusually played by cutting through the opposition's defence and creating an excellent
scoring opportunity for the defending team. Through pass, as the name suggests, after
the team players get the ball (usually the midfielder) to observe the opponent's defense space, or the direction of the forward running to give timely delivery, and such apass like a sharp knife straight into the opponent's hinterland, offensive, need to bevery precise, very timely. We tried to further optimize the MT's through pass, further
dividing the through pass into long through pass (passing distance greater than 20m)
and short through pass (passing distance less than 20m). The long through pass isused on the counter attack to move the ball efficiently and quickly behind the opponent over long distances. A short through pass used to get the ball behind an opponent's back in the penalty area and break down their defence. Through repeated attempts to optimize the algorithm, modify the code, and conduct a lot of testing, we
further optimized the MT's through pass, and the MT's offensive ability became a little bit better than before. Figure 6 and Figure 7 show a successful MT's long throughpass and short through pass.

7Fig. 6. Example drawing of a long through pass.

Fig. 7. Example diagram of a short through pass.

5Formation strategy rate

As we all know, the formation of a team is very important to a team. We added a new343 formation and used the formation editing tool Fedit 2 0.0.0 to modify 343, 4231
and 442, with modify the move code as appropriate to try these formations in thegame. After testing, we found that the new formation strategy had some effect on MT.

6Field_evaluator Evaluator optimization

Our assessment of passing and dribbling has been further refined to allow for bettercross low. We made the following adjustments to field_evaluator: before, when theforward holds the ball on the wing and meets an opponent who is not very threaten-

8ing, the ball will be passed back to the midfielder, adjust forward to have a slightly
higher priority dribble than with the pass, try to continue to dribble and cross low.
And adjust the priority of the pass to the side receiver to be slightly higher than themiddle receiver (when the receiver is out of the penalty area). At the same time, thepriority of the through pass should be increased, so that more through passes can besent to cooperate with the cross low. After testing, it is found that the frequency andquality of cross low are better than before.

7

Summary and Prospect

Since the establishment of MT team, we have been trying to optimize the algorithm,
enrich the playing methods and adjust the attack and defense strategy rate, so as tomake the team actions more efficient and the overall cooperation more intelligent.
We've tried a lot of things, some good, some bad, and there are many more challenges
ahead, and we're going to continue to invest a lot of energy in RoboCup2D.We lookforward to communicating and cooperating with excellent teams from all over theworld to make progress together, and wish RoboCup2D better and better.

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