Kim Gabe

Kim Gabe

Knowledge without application is meaningless. True learning lies in educating and empowering others.

스포츠데이터를 EDA하며 간단한 분석을 해보았습니다. 우리팀엔 어떤 선수들이 필요할까?!

스포츠 데이터 분석 : 유럽 축구 분석

부제 - Arsenal의 부활을 위해 필요한 선수는 누구?!

Introduction

season_result

지난 2018년 4월 20일 Arsenal F.C를 20여년간 지도했던 위대한 명장 Arsène Wenger이 성적 부진을 이유로 감독직에서 해임되었다.

이후 성적 반등을 위해 UEFA 유로파리그 3연패라는 대업을 이룬 Unai Emery 감독을 선임후 1년간의 적응기를 거친뒤 2019-2020시즌 구단의 지원하에 베테랑

및 고가의 몸값을 가진 선수를 성공적으로 영입하며 기대에 부응해줄 것을 고대했다.

하지만 그의 성적은 한때 리그 10위권 밖으로 내려가는등

EPL이라는 영국 프로축구리그의 Top4로 불리던 옛 영광을 재현하지 못하였고 결국 리그8위라는 처참한 성적을 내고 말았다.
asdfadf

이후 팀의 리빌딩을 위해 Arsenal F.C출신의 레전드 선수 Mikel Arteta를 감독으로 임명하였으나

그 또한 이번 시즌 (2020-2021)을 8위로 마감하며 구단과 팬들의 기대에 부응하지 못하였다.

이러한 구단의 부진에는 두 감독의 부족한 전술운영 능력등에 대한 영향도 있겠지만, Unai Emery 감독이 2019-2020시즌에 1000억이라는 거금으로 영입한 Nicolas Pépé나 베테랑 David Luiz의 부진, 그리고 유망주로서 영입한 선수들이 성장세를 보이지 못한 점 등이 팬들에게 많은 비판을 받고 있다.

당시에 영입한 선수들로 인한 팀의 부진 등은 현재도 지속되고 있기에 “당시에 어떤 선수를 방출하고 영입했어야 좋았을까?” 라는 팬심을 담아

FIFA 에서 제공하는 2018-2019의 선수데이터를 통해 Arsenal F.C를 분석한 뒤 대체로 영입했으면 좋았을 선수들을 선정해 보겠다.

데이터 수집

a. 데이터 불러오기

1
2
3
4
5
6
7
8
9
10
11
12

# 분석에 필요한 모듈 불러오기

import pandas as pd
import os

# 컬럼 전체 확인 가능하도록 출력 범위 설정
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

# 불필요한 경고 표시 생략
import warnings
warnings.filterwarnings(action = 'ignore')

1

os.listdir()

['.ipynb_checkpoints',
 'FIFA_data(origin).csv',
 'FIFA_data.csv',
 '[EDA] 아스널은 누구를 영입했어야 했을까 (EDA 로 스포츠 데이터 분석하기).ipynb']

1
2

# 분석할 데이터 로드
data = pd.read_csv('FIFA_data.csv')

1
2

# 데이터 확인
data.head()
Unnamed: 0 ID Name Age Nationality Overall Potential Club Value Wage Preferred Foot International Reputation Weak Foot Skill Moves Position Jersey Number Joined Contract Valid Until Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
0 0 158023 L. Messi 31 Argentina 94.0 94.0 FC Barcelona €110.5M €565K Left 5.0 4.0 4.0 RF 10.0 Jul 1, 2004 2021 5'7 159lbs 88+2 88+2 88+2 92+2 93+2 93+2 93+2 92+2 93+2 93+2 93+2 91+2 84+2 84+2 84+2 91+2 64+2 61+2 61+2 61+2 64+2 59+2 47+2 47+2 47+2 59+2 84.0 95.0 70.0 90.0 86.0 97.0 93.0 94.0 87.0 96.0 91.0 86.0 91.0 95.0 95.0 85.0 68.0 72.0 59.0 94.0 48.0 22.0 94.0 94.0 75.0 96.0 33.0 28.0 26.0 6.0 11.0 15.0 14.0 8.0 €226.5M
1 1 20801 Cristiano Ronaldo 33 Portugal 94.0 94.0 Juventus €77M €405K Right 5.0 4.0 5.0 ST 7.0 Jul 10, 2018 2022 6'2 183lbs 91+3 91+3 91+3 89+3 90+3 90+3 90+3 89+3 88+3 88+3 88+3 88+3 81+3 81+3 81+3 88+3 65+3 61+3 61+3 61+3 65+3 61+3 53+3 53+3 53+3 61+3 84.0 94.0 89.0 81.0 87.0 88.0 81.0 76.0 77.0 94.0 89.0 91.0 87.0 96.0 70.0 95.0 95.0 88.0 79.0 93.0 63.0 29.0 95.0 82.0 85.0 95.0 28.0 31.0 23.0 7.0 11.0 15.0 14.0 11.0 €127.1M
2 2 190871 Neymar Jr 26 Brazil 92.0 93.0 Paris Saint-Germain €118.5M €290K Right 5.0 5.0 5.0 LW 10.0 Aug 3, 2017 2022 5'9 150lbs 84+3 84+3 84+3 89+3 89+3 89+3 89+3 89+3 89+3 89+3 89+3 88+3 81+3 81+3 81+3 88+3 65+3 60+3 60+3 60+3 65+3 60+3 47+3 47+3 47+3 60+3 79.0 87.0 62.0 84.0 84.0 96.0 88.0 87.0 78.0 95.0 94.0 90.0 96.0 94.0 84.0 80.0 61.0 81.0 49.0 82.0 56.0 36.0 89.0 87.0 81.0 94.0 27.0 24.0 33.0 9.0 9.0 15.0 15.0 11.0 €228.1M
3 3 193080 De Gea 27 Spain 91.0 93.0 Manchester United €72M €260K Right 4.0 3.0 1.0 GK 1.0 Jul 1, 2011 2020 6'4 168lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 17.0 13.0 21.0 50.0 13.0 18.0 21.0 19.0 51.0 42.0 57.0 58.0 60.0 90.0 43.0 31.0 67.0 43.0 64.0 12.0 38.0 30.0 12.0 68.0 40.0 68.0 15.0 21.0 13.0 90.0 85.0 87.0 88.0 94.0 €138.6M
4 4 192985 K. De Bruyne 27 Belgium 91.0 92.0 Manchester City €102M €355K Right 4.0 5.0 4.0 RCM 7.0 Aug 30, 2015 2023 5'11 154lbs 82+3 82+3 82+3 87+3 87+3 87+3 87+3 87+3 88+3 88+3 88+3 88+3 87+3 87+3 87+3 88+3 77+3 77+3 77+3 77+3 77+3 73+3 66+3 66+3 66+3 73+3 93.0 82.0 55.0 92.0 82.0 86.0 85.0 83.0 91.0 91.0 78.0 76.0 79.0 91.0 77.0 91.0 63.0 90.0 75.0 91.0 76.0 61.0 87.0 94.0 79.0 88.0 68.0 58.0 51.0 15.0 13.0 5.0 10.0 13.0 €196.4M

b. 데이터 확인 및 분석 계획 설립

번호 컬럼 명 컬럼 의미 번호 컬럼 명 컬럼 의미
1 ID 고유의 번호 11 Skill Moves 개인기
2 Name 이름 12 Position 포지션
3 Age 나이 13 Jersey Number 등번호
4 Overall 현재 능력치 14 Joined 소속 팀 입단 날짜
5 Potential 잠재 능력치 15 Contract Valid Until 계약 기간
6 Club 소속 팀 16 Height 키 (피트)
7 Value 예상 이적료 (유로) 17 Weight 몸무게 (파운드)
8 Wage 주급 (유로) 18~26 LS ~ RB 포지션 별 능력치
9 Preferred Foot 잘 사용하는 발 27~79 Crossing ~ GKReflexes 세부 능력치
10 Weak Foot 잘 사용하지 않는 발 80 Release Clause 바이아웃

해당 컬럼들을 이용해서 어떻게 주제에 맞게 분석을 할지 고민

분석 순서

  • Arsenal F.C.의 선수들을 분석

  • Arsenal F.C.의 지역 라이벌 Chelsea F.C. 선수와 능력치 비교

  • 라이벌팀 대비 부족한 포지션 2개 선정

  • 다른팀의 선수 중 Arsenal F.C.의 재정, 현실가능성, 영입방침을 고려하여 2명의 선수를 영입

본격적 데이터 분석에 앞선 기초 전처리

Value, Wage 등 수치값이지만 화폐단위등과 같이 입력되어 str 형태인 데이터들을 숫자로 변환

분석 데이터가 2020년 데이터 이므로 유로환율을 2020년 평균으로 환산

2020년 유로 평균 환율 = 1,368.16

1M = 1,000,000

1K = 1,000




1
2
3
4
5
6
7
8
9
10
11

# 화폐관련 데이터의 각 단위에 맞게 숫자로 변경

# M = 000,000 으로 대체
# K = 000  으로 대체

data['Value'] = data['Value'].str.replace('M','000000')
data['Value'] = data['Value'].str.replace('K','000')
data['Wage'] = data['Wage'].str.replace('M','000000')
data['Wage'] = data['Wage'].str.replace('K','000')
data['Release Clause'] = data['Release Clause'].str.replace('M','000000')
data['Release Clause'] = data['Release Clause'].str.replace('K','000')

1
2

print(data[['Value', 'Wage','Release Clause']].info())
data[['Value', 'Wage','Release Clause']]

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18207 entries, 0 to 18206
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Value           18207 non-null  object
 1   Wage            18207 non-null  object
 2   Release Clause  16643 non-null  object
dtypes: object(3)
memory usage: 426.9+ KB
None
Value Wage Release Clause
0 €110.5000000 €565000 €226.5000000
1 €77000000 €405000 €127.1000000
2 €118.5000000 €290000 €228.1000000
3 €72000000 €260000 €138.6000000
4 €102000000 €355000 €196.4000000
... ... ... ...
18202 €60000 €1000 €143000
18203 €60000 €1000 €113000
18204 €60000 €1000 €165000
18205 €60000 €1000 €143000
18206 €60000 €1000 €165000

18207 rows × 3 columns





숫자 앞의 € 를 제외 하는 작업

인덱싱을 활용하여 1번째 자리에 있는 값부터 그 뒤의 값을 전부 slicing

여러번 반복하면 slicing한 상태에서 또 slincing을 하기 때문에 여러번 반복하지 않도록 주의

1
2
3
4
5
6
7
8

data['Value'] = data['Value'].str.slice(1,)

data['Wage'] = data['Wage'].str.slice(1,)

data['Release Clause'] = data['Release Clause'].str.slice(1,)

# 결과 확인
data[['Value', 'Wage','Release Clause']]
Value Wage Release Clause
0 110.5000000 565000 226.5000000
1 77000000 405000 127.1000000
2 118.5000000 290000 228.1000000
3 72000000 260000 138.6000000
4 102000000 355000 196.4000000
... ... ... ...
18202 60000 1000 143000
18203 60000 1000 113000
18204 60000 1000 165000
18205 60000 1000 143000
18206 60000 1000 165000

18207 rows × 3 columns




  • M, K 등으로 소수점 이 들어가있던 자료의 소수점을 제거
1
2
3
4
5
6
7
8
9

data['Value'] = data['Value'].str.replace('.','')

data['Wage'] = data['Wage'].str.replace('.','')

data['Release Clause'] = data['Release Clause'].str.replace('.','')

# 결과 확인
print(data[['Value', 'Wage','Release Clause']].info())
data[['Value', 'Wage','Release Clause']]

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18207 entries, 0 to 18206
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Value           18207 non-null  object
 1   Wage            18207 non-null  object
 2   Release Clause  16643 non-null  object
dtypes: object(3)
memory usage: 426.9+ KB
None
Value Wage Release Clause
0 1105000000 565000 2265000000
1 77000000 405000 1271000000
2 1185000000 290000 2281000000
3 72000000 260000 1386000000
4 102000000 355000 1964000000
... ... ... ...
18202 60000 1000 143000
18203 60000 1000 113000
18204 60000 1000 165000
18205 60000 1000 143000
18206 60000 1000 165000

18207 rows × 3 columns


  • 문자형으로된 숫자값을 숫자형으로 변환
1
2
3
4
5
6
7
8

data['Value'] = data['Value'].astype(float)

data['Wage'] = data['Wage'].astype(float)

data['Release Clause'] = data['Release Clause'].astype(float)

# 결과 확인
print(data[['Value', 'Wage','Release Clause']].info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18207 entries, 0 to 18206
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Value           18207 non-null  float64
 1   Wage            18207 non-null  float64
 2   Release Clause  16643 non-null  float64
dtypes: float64(3)
memory usage: 426.9 KB
None

1

data[['Value', 'Wage','Release Clause']]
Value Wage Release Clause
0 1.105000e+09 565000.0 2.265000e+09
1 7.700000e+07 405000.0 1.271000e+09
2 1.185000e+09 290000.0 2.281000e+09
3 7.200000e+07 260000.0 1.386000e+09
4 1.020000e+08 355000.0 1.964000e+09
... ... ... ...
18202 6.000000e+04 1000.0 1.430000e+05
18203 6.000000e+04 1000.0 1.130000e+05
18204 6.000000e+04 1000.0 1.650000e+05
18205 6.000000e+04 1000.0 1.430000e+05
18206 6.000000e+04 1000.0 1.650000e+05

18207 rows × 3 columns




Arsenal F.C 분석 - Arsenal F.C는 어떤 선수들이 존재하는가?

a. EDA

1
2
3

# data 의 데이터는 모든 축구선수 데이터를 포함하고 있기 때문에 Arsernal F.C. 선수 데이터 분석을 위해 별도 테이블 생성

gunners = data[data['Club']== 'Arsenal']

1
2
3
4
5

# 추출된 결과 확인 (Arsenal 선수들만 추출된 것인지) - 2개 모두 가능


#gunners.Club.unique()
gunners['Club'].unique()

array(['Arsenal'], dtype=object)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

# 추출된 gunners 테이블의 세부 데이터를 print 하여 확인

# print() 함수에 'f'를 입력하면 "" 내에서 변수를 사용 할 수 있다.
# 실행할 함수는 {} 사이에 입력한다.

print(f"아스널 선수들의 총 인원 : {gunners.shape[0]}")  #print(f"총 인원 : {len(gunners)} ") 도 가능
print("\n")

print(f"아스널 선수들의 평균 연령 : {gunners['Age'].mean():.2f}") # "출력할 내용 : .숫자f" 로 소수점 조절 가능 
print("\n")

print(f"아스널 선수들의 포지션 종류 : {gunners['Position'].unique()}")
print("\n")

print(f"아스널 선수들의 평균 능력치 : {gunners['Overall'].mean():.2f}") # "출력할 내용 : .숫자f" 로 소수점 조절 가능 
print("\n")

print(f"아스널 선수들의 평균 잠재 능력치 : {gunners['Potential'].mean():.2f}") # "출력할 내용 : .숫자f" 로 소수점 조절 가능 

아스널 선수들의 총 인원 : 33


아스널 선수들의 평균 연령 : 24.61


아스널 선수들의 포지션 종류 : ['LS' 'CAM' 'ST' 'GK' 'RCB' 'RM' 'LCM' 'CB' 'LDM' 'RB' 'LB' 'LM' 'LW'
 'RDM' 'CDM' 'CM']


아스널 선수들의 평균 능력치 : 75.18


아스널 선수들의 평균 잠재 능력치 : 81.39

1

gunners
Unnamed: 0 ID Name Age Nationality Overall Potential Club Value Wage Preferred Foot International Reputation Weak Foot Skill Moves Position Jersey Number Joined Contract Valid Until Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
33 33 188567 P. Aubameyang 29 Gabon 88.0 88.0 Arsenal 59000000.0 265000.0 Right 3.0 4.0 4.0 LS 14.0 Jan 31, 2018 2021 6'2 176lbs 84+3 84+3 84+3 83+3 83+3 83+3 83+3 83+3 81+3 81+3 81+3 81+3 73+3 73+3 73+3 81+3 64+3 58+3 58+3 58+3 64+3 61+3 52+3 52+3 52+3 61+3 77.0 88.0 79.0 77.0 86.0 79.0 80.0 74.0 64.0 82.0 93.0 95.0 76.0 87.0 70.0 82.0 79.0 76.0 76.0 79.0 43.0 48.0 90.0 77.0 76.0 86.0 27.0 25.0 36.0 6.0 9.0 15.0 9.0 9.0 1.121000e+09
74 74 176635 M. Özil 29 Germany 86.0 86.0 Arsenal 435000000.0 190000.0 Left 4.0 2.0 4.0 CAM 10.0 Sep 2, 2013 2021 5'11 168lbs 75+3 75+3 75+3 82+3 81+3 81+3 81+3 82+3 84+3 84+3 84+3 82+3 78+3 78+3 78+3 82+3 57+3 56+3 56+3 56+3 57+3 52+3 40+3 40+3 40+3 52+3 83.0 73.0 54.0 89.0 80.0 84.0 84.0 77.0 82.0 90.0 72.0 69.0 79.0 84.0 70.0 70.0 48.0 69.0 57.0 75.0 48.0 24.0 83.0 91.0 67.0 82.0 24.0 16.0 19.0 6.0 14.0 10.0 6.0 14.0 8.270000e+08
87 87 193301 A. Lacazette 27 France 85.0 86.0 Arsenal 45000000.0 180000.0 Right 3.0 4.0 4.0 ST 9.0 Jul 5, 2017 2022 5'9 161lbs 82+3 82+3 82+3 81+3 83+3 83+3 83+3 81+3 81+3 81+3 81+3 79+3 74+3 74+3 74+3 79+3 62+3 60+3 60+3 60+3 62+3 59+3 54+3 54+3 54+3 59+3 64.0 88.0 73.0 80.0 81.0 85.0 74.0 71.0 59.0 83.0 81.0 84.0 85.0 85.0 85.0 83.0 76.0 79.0 73.0 75.0 70.0 42.0 86.0 76.0 84.0 85.0 29.0 42.0 30.0 11.0 6.0 9.0 5.0 6.0 8.890000e+08
131 131 192563 B. Leno 26 Germany 84.0 87.0 Arsenal 27000000.0 115000.0 Right 3.0 4.0 1.0 GK 12.0 Jul 1, 2018 2023 6'3 183lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 9.0 9.0 13.0 45.0 10.0 16.0 9.0 8.0 40.0 22.0 46.0 52.0 52.0 80.0 44.0 23.0 73.0 43.0 68.0 14.0 28.0 22.0 7.0 54.0 23.0 66.0 27.0 15.0 18.0 84.0 82.0 75.0 85.0 82.0 5.330000e+08
146 146 172879 Sokratis 30 Greece 84.0 84.0 Arsenal 23000000.0 140000.0 Right 3.0 2.0 2.0 RCB 19.0 Jul 2, 2018 2021 6'1 187lbs 62+3 62+3 62+3 56+3 58+3 58+3 58+3 56+3 56+3 56+3 56+3 57+3 59+3 59+3 59+3 57+3 70+3 71+3 71+3 71+3 70+3 73+3 81+3 81+3 81+3 73+3 47.0 44.0 85.0 60.0 46.0 58.0 29.0 35.0 51.0 58.0 64.0 73.0 60.0 81.0 64.0 77.0 86.0 69.0 86.0 48.0 88.0 77.0 49.0 45.0 24.0 82.0 85.0 87.0 85.0 6.0 8.0 5.0 12.0 5.0 4.370000e+08
199 199 192883 H. Mkhitaryan 29 Armenia 83.0 83.0 Arsenal 255000000.0 145000.0 Right 3.0 5.0 4.0 RM 7.0 Jan 22, 2018 2021 5'10 165lbs 76+3 76+3 76+3 81+3 80+3 80+3 80+3 81+3 81+3 81+3 81+3 80+3 78+3 78+3 78+3 80+3 70+3 68+3 68+3 68+3 70+3 67+3 59+3 59+3 59+3 67+3 77.0 74.0 53.0 80.0 81.0 84.0 83.0 64.0 71.0 86.0 84.0 78.0 87.0 79.0 85.0 85.0 69.0 83.0 62.0 73.0 69.0 64.0 82.0 84.0 72.0 76.0 41.0 53.0 55.0 5.0 11.0 14.0 5.0 7.0 4.850000e+08
235 235 223959 L. Torreira 22 Uruguay 82.0 87.0 Arsenal 25000000.0 98000.0 Right 2.0 3.0 3.0 LCM 14.0 Jul 12, 2018 2023 5'6 141lbs 68+2 68+2 68+2 74+2 73+2 73+2 73+2 74+2 75+2 75+2 75+2 75+2 78+2 78+2 78+2 75+2 80+2 81+2 81+2 81+2 80+2 79+2 77+2 77+2 77+2 79+2 74.0 50.0 55.0 83.0 65.0 78.0 66.0 76.0 77.0 81.0 81.0 74.0 83.0 79.0 93.0 78.0 52.0 85.0 66.0 74.0 85.0 87.0 65.0 74.0 48.0 78.0 84.0 84.0 75.0 5.0 5.0 13.0 7.0 9.0 4.940000e+08
281 281 192227 S. Mustafi 26 Germany 82.0 84.0 Arsenal 225000000.0 115000.0 Right 3.0 3.0 2.0 RCB 20.0 Aug 30, 2016 2021 6'0 181lbs 64+3 64+3 64+3 61+3 62+3 62+3 62+3 61+3 63+3 63+3 63+3 62+3 67+3 67+3 67+3 62+3 72+3 76+3 76+3 76+3 72+3 74+3 80+3 80+3 80+3 74+3 53.0 58.0 81.0 77.0 51.0 53.0 55.0 47.0 69.0 70.0 58.0 65.0 63.0 82.0 67.0 66.0 83.0 68.0 78.0 52.0 82.0 82.0 50.0 55.0 54.0 73.0 80.0 82.0 82.0 11.0 9.0 15.0 10.0 6.0 4.440000e+08
289 289 186561 A. Ramsey 27 Wales 82.0 83.0 Arsenal 25000000.0 130000.0 Right 3.0 3.0 3.0 CAM 10.0 Jun 13, 2008 2019 6'0 168lbs 75+3 75+3 75+3 77+3 78+3 78+3 78+3 77+3 79+3 79+3 79+3 78+3 80+3 80+3 80+3 78+3 75+3 75+3 75+3 75+3 75+3 72+3 69+3 69+3 69+3 72+3 75.0 74.0 58.0 84.0 79.0 81.0 70.0 66.0 80.0 82.0 64.0 69.0 73.0 81.0 74.0 81.0 66.0 89.0 67.0 74.0 73.0 69.0 84.0 79.0 75.0 81.0 66.0 70.0 67.0 6.0 11.0 5.0 10.0 8.0 4.940000e+08
311 311 165229 L. Koscielny 32 France 82.0 82.0 Arsenal 125000000.0 115000.0 Right 3.0 3.0 2.0 CB 6.0 Jul 7, 2010 2020 6'1 165lbs 57+3 57+3 57+3 58+3 58+3 58+3 58+3 58+3 60+3 60+3 60+3 60+3 65+3 65+3 65+3 60+3 71+3 75+3 75+3 75+3 71+3 74+3 80+3 80+3 80+3 74+3 52.0 32.0 80.0 76.0 35.0 62.0 22.0 49.0 69.0 66.0 64.0 66.0 65.0 76.0 61.0 54.0 78.0 62.0 75.0 47.0 86.0 84.0 41.0 56.0 51.0 75.0 80.0 84.0 83.0 13.0 11.0 9.0 11.0 7.0 2.380000e+08
317 317 48940 P. Čech 36 Czech Republic 82.0 82.0 Arsenal 3000000.0 60000.0 Left 4.0 3.0 1.0 GK 1.0 Jun 29, 2015 2019 6'5 198lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 19.0 12.0 19.0 35.0 17.0 12.0 13.0 19.0 33.0 22.0 35.0 38.0 46.0 77.0 34.0 21.0 50.0 32.0 63.0 11.0 17.0 23.0 13.0 53.0 23.0 63.0 12.0 13.0 12.0 78.0 82.0 70.0 83.0 79.0 5.700000e+07
364 364 199503 G. Xhaka 25 Switzerland 81.0 84.0 Arsenal 22000000.0 115000.0 Left 3.0 2.0 3.0 LDM 10.0 Jul 1, 2016 2021 6'1 181lbs 68+3 68+3 68+3 69+3 71+3 71+3 71+3 69+3 74+3 74+3 74+3 71+3 79+3 79+3 79+3 71+3 71+3 77+3 77+3 77+3 71+3 70+3 71+3 71+3 71+3 70+3 71.0 49.0 62.0 85.0 50.0 70.0 73.0 77.0 85.0 80.0 47.0 49.0 49.0 77.0 61.0 88.0 32.0 80.0 75.0 80.0 92.0 71.0 77.0 81.0 63.0 74.0 68.0 72.0 68.0 7.0 8.0 11.0 7.0 13.0 4.350000e+08
464 464 203747 Héctor Bellerín 23 Spain 80.0 85.0 Arsenal 165000000.0 85000.0 Right 3.0 3.0 3.0 RB 2.0 Jul 1, 2014 2023 5'10 163lbs 65+3 65+3 65+3 73+3 70+3 70+3 70+3 73+3 71+3 71+3 71+3 75+3 71+3 71+3 71+3 75+3 79+3 74+3 74+3 74+3 79+3 79+3 73+3 73+3 73+3 79+3 76.0 52.0 60.0 76.0 41.0 75.0 63.0 50.0 59.0 78.0 94.0 91.0 82.0 78.0 77.0 52.0 63.0 84.0 58.0 43.0 72.0 77.0 67.0 66.0 62.0 70.0 75.0 78.0 80.0 8.0 14.0 14.0 13.0 12.0 3.260000e+08
520 520 177604 Nacho Monreal 32 Spain 80.0 80.0 Arsenal 8000000.0 99000.0 Left 2.0 3.0 3.0 LB 18.0 Jan 31, 2013 2019 5'10 159lbs 68+2 68+2 68+2 70+2 70+2 70+2 70+2 70+2 70+2 70+2 70+2 71+2 72+2 72+2 72+2 71+2 77+2 76+2 76+2 76+2 77+2 77+2 77+2 77+2 77+2 77+2 76.0 55.0 72.0 77.0 58.0 73.0 74.0 52.0 61.0 77.0 72.0 67.0 70.0 82.0 73.0 74.0 74.0 74.0 65.0 61.0 77.0 81.0 66.0 66.0 61.0 76.0 80.0 83.0 81.0 7.0 5.0 9.0 3.0 5.0 1.520000e+08
606 606 207993 S. Kolašinac 25 Bosnia Herzegovina 79.0 82.0 Arsenal 13000000.0 89000.0 Left 2.0 2.0 3.0 LB 31.0 Jul 1, 2017 2022 6'0 187lbs 70+2 70+2 70+2 69+2 70+2 70+2 70+2 69+2 68+2 68+2 68+2 70+2 69+2 69+2 69+2 70+2 76+2 74+2 74+2 74+2 76+2 76+2 78+2 78+2 78+2 76+2 77.0 59.0 74.0 76.0 48.0 73.0 43.0 25.0 53.0 74.0 68.0 75.0 49.0 77.0 55.0 82.0 76.0 79.0 90.0 58.0 93.0 73.0 66.0 60.0 44.0 72.0 73.0 81.0 80.0 15.0 9.0 16.0 7.0 9.0 2.570000e+08
723 723 108080 S. Lichtsteiner 34 Switzerland 79.0 79.0 Arsenal 36000000.0 94000.0 Right 3.0 3.0 3.0 RB 2.0 Jul 1, 2018 2019 6'0 168lbs 67+3 67+3 67+3 70+3 69+3 69+3 69+3 70+3 69+3 69+3 69+3 71+3 72+3 72+3 72+3 71+3 77+3 76+3 76+3 76+3 77+3 77+3 77+3 77+3 77+3 77+3 78.0 45.0 70.0 77.0 62.0 76.0 68.0 66.0 70.0 76.0 65.0 69.0 73.0 79.0 77.0 74.0 73.0 76.0 70.0 68.0 82.0 82.0 66.0 62.0 64.0 80.0 78.0 80.0 80.0 4.0 3.0 2.0 3.0 2.0 6.800000e+07
789 789 213655 A. Iwobi 22 Nigeria 78.0 84.0 Arsenal 14000000.0 81000.0 Right 1.0 3.0 4.0 LM 17.0 Jul 1, 2013 2023 6'1 165lbs 69+2 69+2 69+2 76+2 75+2 75+2 75+2 76+2 77+2 77+2 77+2 76+2 70+2 70+2 70+2 76+2 57+2 54+2 54+2 54+2 57+2 51+2 42+2 42+2 42+2 51+2 67.0 64.0 42.0 81.0 48.0 84.0 50.0 40.0 63.0 84.0 80.0 77.0 76.0 75.0 75.0 67.0 47.0 74.0 70.0 54.0 46.0 24.0 75.0 80.0 59.0 72.0 30.0 26.0 23.0 14.0 7.0 12.0 14.0 11.0 2.940000e+08
933 933 186146 D. Welbeck 27 England 77.0 77.0 Arsenal 95000000.0 95000.0 Right 3.0 3.0 3.0 LW 21.0 Sep 2, 2014 2019 6'1 161lbs 77+3 77+3 77+3 76+3 76+3 76+3 76+3 76+3 75+3 75+3 75+3 75+3 69+3 69+3 69+3 75+3 60+3 59+3 59+3 59+3 60+3 58+3 55+3 55+3 55+3 58+3 69.0 75.0 78.0 75.0 76.0 78.0 61.0 62.0 64.0 78.0 76.0 83.0 76.0 78.0 62.0 78.0 80.0 74.0 75.0 69.0 75.0 32.0 79.0 71.0 70.0 76.0 47.0 38.0 33.0 9.0 10.0 6.0 12.0 16.0 1.810000e+08
959 959 213051 M. Elneny 25 Egypt 77.0 79.0 Arsenal 9000000.0 76000.0 Right 1.0 3.0 3.0 RDM 17.0 Jan 14, 2016 2022 5'11 163lbs 65+2 65+2 65+2 67+2 68+2 68+2 68+2 67+2 70+2 70+2 70+2 70+2 74+2 74+2 74+2 70+2 74+2 76+2 76+2 76+2 74+2 73+2 71+2 71+2 71+2 73+2 66.0 57.0 57.0 80.0 39.0 68.0 69.0 56.0 77.0 76.0 58.0 60.0 65.0 75.0 62.0 76.0 69.0 92.0 68.0 67.0 73.0 76.0 63.0 70.0 62.0 77.0 76.0 74.0 72.0 7.0 15.0 15.0 12.0 6.0 1.780000e+08
1495 1495 228295 R. Holding 22 England 76.0 83.0 Arsenal 95000000.0 64000.0 Right 1.0 3.0 2.0 CB 16.0 Jul 26, 2016 2022 6'2 165lbs 53+2 53+2 53+2 55+2 55+2 55+2 55+2 55+2 57+2 57+2 57+2 59+2 62+2 62+2 62+2 59+2 68+2 71+2 71+2 71+2 68+2 70+2 75+2 75+2 75+2 70+2 52.0 30.0 75.0 72.0 27.0 62.0 33.0 30.0 61.0 67.0 58.0 61.0 58.0 70.0 67.0 48.0 75.0 68.0 76.0 27.0 72.0 77.0 40.0 57.0 38.0 79.0 77.0 78.0 76.0 14.0 11.0 10.0 14.0 13.0 2.000000e+07
3392 3392 202811 E. Martínez 25 Argentina 72.0 77.0 Arsenal 3000000.0 40000.0 Right 1.0 3.0 1.0 GK 26.0 Jul 1, 2010 2020 6'4 187lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 22.0 16.0 26.0 27.0 23.0 13.0 22.0 20.0 46.0 26.0 59.0 57.0 49.0 64.0 49.0 18.0 60.0 14.0 70.0 16.0 37.0 13.0 12.0 50.0 42.0 53.0 11.0 20.0 20.0 73.0 69.0 75.0 70.0 74.0 5.800000e+07
4590 4590 236496 M. Guendouzi 19 France 71.0 84.0 Arsenal 4000000.0 20000.0 Right 1.0 3.0 3.0 CDM 29.0 Jul 11, 2018 2023 6'1 150lbs 62+2 62+2 62+2 65+2 64+2 64+2 64+2 65+2 67+2 67+2 67+2 66+2 69+2 69+2 69+2 66+2 67+2 69+2 69+2 69+2 67+2 66+2 66+2 66+2 66+2 66+2 67.0 56.0 64.0 74.0 49.0 69.0 68.0 55.0 75.0 68.0 66.0 60.0 64.0 67.0 65.0 69.0 66.0 69.0 63.0 65.0 73.0 71.0 51.0 73.0 49.0 73.0 62.0 69.0 64.0 6.0 13.0 6.0 13.0 11.0 1.000000e+07
4719 4719 225782 A. Maitland-Niles 20 England 71.0 84.0 Arsenal 45000000.0 38000.0 Right 1.0 3.0 3.0 CM 15.0 Oct 24, 2014 2019 5'10 157lbs 64+2 64+2 64+2 70+2 69+2 69+2 69+2 70+2 70+2 70+2 70+2 70+2 69+2 69+2 69+2 70+2 68+2 67+2 67+2 67+2 68+2 66+2 61+2 61+2 61+2 66+2 64.0 56.0 38.0 74.0 44.0 73.0 60.0 36.0 69.0 72.0 82.0 85.0 76.0 69.0 70.0 65.0 55.0 69.0 66.0 62.0 58.0 66.0 66.0 70.0 54.0 77.0 57.0 66.0 60.0 15.0 9.0 7.0 10.0 13.0 1.140000e+08
6136 6136 200841 C. Jenkinson 26 England 69.0 71.0 Arsenal 1000000.0 34000.0 Right 2.0 2.0 3.0 RB 25.0 Jun 8, 2011 2020 6'1 170lbs 61+2 61+2 61+2 63+2 62+2 62+2 62+2 63+2 62+2 62+2 62+2 65+2 64+2 64+2 64+2 65+2 68+2 67+2 67+2 67+2 68+2 68+2 68+2 68+2 68+2 68+2 71.0 41.0 62.0 66.0 60.0 67.0 60.0 52.0 64.0 68.0 58.0 73.0 60.0 69.0 66.0 72.0 63.0 64.0 69.0 41.0 71.0 66.0 67.0 62.0 49.0 62.0 69.0 73.0 72.0 12.0 15.0 15.0 11.0 12.0 2.000000e+06
6937 6937 242000 K. Mavropanos 20 Greece 68.0 83.0 Arsenal 18000000.0 21000.0 Right 1.0 2.0 2.0 CB 27.0 Jan 4, 2018 2022 6'4 194lbs 44+2 44+2 44+2 41+2 41+2 41+2 41+2 41+2 41+2 41+2 41+2 44+2 46+2 46+2 46+2 44+2 56+2 58+2 58+2 58+2 56+2 59+2 66+2 66+2 66+2 59+2 37.0 30.0 66.0 47.0 30.0 41.0 32.0 37.0 48.0 42.0 52.0 60.0 55.0 57.0 39.0 45.0 67.0 64.0 79.0 26.0 65.0 62.0 30.0 37.0 42.0 53.0 70.0 74.0 72.0 12.0 11.0 11.0 10.0 7.0 4.500000e+07
7069 7069 222576 Pleguezuelo 21 Spain 68.0 77.0 Arsenal 12000000.0 21000.0 Right 1.0 4.0 3.0 CB 53.0 Apr 11, 2014 2019 5'11 139lbs 52+2 52+2 52+2 57+2 55+2 55+2 55+2 57+2 57+2 57+2 57+2 59+2 59+2 59+2 59+2 59+2 65+2 65+2 65+2 65+2 65+2 66+2 67+2 67+2 67+2 66+2 53.0 32.0 62.0 71.0 37.0 66.0 40.0 44.0 59.0 70.0 71.0 70.0 73.0 59.0 69.0 38.0 75.0 61.0 65.0 35.0 66.0 72.0 41.0 40.0 43.0 59.0 65.0 68.0 70.0 14.0 15.0 8.0 11.0 9.0 2.500000e+07
10424 10424 216699 G. Zelalem 21 United States 65.0 75.0 Arsenal 875000.0 15000.0 Right 1.0 3.0 3.0 CM 60.0 Mar 18, 2014 2018 5'11 154lbs 56+2 56+2 56+2 61+2 60+2 60+2 60+2 61+2 64+2 64+2 64+2 62+2 63+2 63+2 63+2 62+2 54+2 56+2 56+2 56+2 54+2 53+2 49+2 49+2 49+2 53+2 51.0 49.0 49.0 73.0 55.0 64.0 65.0 54.0 71.0 70.0 65.0 56.0 65.0 59.0 73.0 51.0 53.0 60.0 58.0 45.0 40.0 38.0 55.0 70.0 56.0 65.0 41.0 48.0 49.0 10.0 14.0 12.0 13.0 10.0 1.800000e+07
11579 11579 240273 E. Smith Rowe 17 England 64.0 85.0 Arsenal 975000.0 10000.0 Right 1.0 3.0 4.0 LW 55.0 Jul 31, 2017 2019 6'0 141lbs 59+2 59+2 59+2 64+2 62+2 62+2 62+2 64+2 62+2 62+2 62+2 63+2 55+2 55+2 55+2 63+2 50+2 45+2 45+2 45+2 50+2 47+2 38+2 38+2 38+2 47+2 60.0 61.0 36.0 59.0 55.0 67.0 67.0 58.0 49.0 67.0 78.0 79.0 73.0 53.0 68.0 69.0 47.0 61.0 44.0 60.0 39.0 24.0 58.0 59.0 64.0 60.0 37.0 30.0 31.0 11.0 8.0 14.0 8.0 10.0 2.800000e+07
11765 11765 236988 E. Nketiah 19 England 64.0 84.0 Arsenal 975000.0 10000.0 Right 1.0 3.0 3.0 ST 49.0 Jul 1, 2015 2021 5'9 154lbs 63+2 63+2 63+2 62+2 63+2 63+2 63+2 62+2 61+2 61+2 61+2 60+2 53+2 53+2 53+2 60+2 42+2 39+2 39+2 39+2 42+2 40+2 34+2 34+2 34+2 40+2 33.0 66.0 51.0 54.0 56.0 65.0 43.0 38.0 46.0 62.0 87.0 82.0 76.0 66.0 72.0 60.0 73.0 54.0 56.0 59.0 43.0 16.0 65.0 52.0 57.0 60.0 14.0 19.0 15.0 12.0 10.0 11.0 9.0 5.0 2.800000e+07
12497 12497 237169 C. Bramall 22 England 63.0 70.0 Arsenal 425000.0 12000.0 Left 1.0 2.0 2.0 LB 39.0 Jan 9, 2017 2019 5'10 159lbs 54+2 54+2 54+2 60+2 58+2 58+2 58+2 60+2 58+2 58+2 58+2 61+2 57+2 57+2 57+2 61+2 62+2 58+2 58+2 58+2 62+2 61+2 57+2 57+2 57+2 61+2 58.0 36.0 45.0 60.0 37.0 63.0 48.0 44.0 58.0 62.0 88.0 88.0 74.0 55.0 67.0 55.0 64.0 64.0 56.0 58.0 59.0 59.0 53.0 50.0 34.0 52.0 56.0 57.0 59.0 11.0 8.0 7.0 14.0 12.0 8.930000e+05
13999 13999 237329 J. Willock 18 England 61.0 79.0 Arsenal 525000.0 7000.0 Right 1.0 3.0 3.0 CM 59.0 Jul 1, 2016 2022 6'0 157lbs 53+2 53+2 53+2 57+2 57+2 57+2 57+2 57+2 59+2 59+2 59+2 59+2 59+2 59+2 59+2 59+2 57+2 58+2 58+2 58+2 57+2 56+2 54+2 54+2 54+2 56+2 44.0 36.0 41.0 64.0 42.0 62.0 51.0 46.0 62.0 62.0 68.0 74.0 70.0 54.0 72.0 58.0 55.0 63.0 62.0 46.0 55.0 56.0 58.0 64.0 47.0 60.0 54.0 55.0 51.0 11.0 6.0 12.0 8.0 9.0 1.300000e+07
15157 15157 240334 Z. Medley 17 England 60.0 78.0 Arsenal 375000.0 5000.0 Right 1.0 3.0 2.0 CB 47.0 Aug 2, 2017 2019 6'4 165lbs 40+2 40+2 40+2 38+2 39+2 39+2 39+2 38+2 39+2 39+2 39+2 40+2 43+2 43+2 43+2 40+2 52+2 53+2 53+2 53+2 52+2 54+2 59+2 59+2 59+2 54+2 34.0 25.0 55.0 43.0 35.0 34.0 34.0 29.0 37.0 41.0 54.0 49.0 49.0 56.0 57.0 41.0 70.0 58.0 63.0 27.0 48.0 60.0 32.0 37.0 43.0 50.0 61.0 70.0 63.0 10.0 11.0 5.0 10.0 14.0 9.470000e+05
16216 16216 233442 J. Osei-Tutu 19 England 58.0 74.0 Arsenal 240000.0 4000.0 Right 1.0 3.0 2.0 RB 52.0 Oct 6, 2015 2021 5'10 159lbs 48+2 48+2 48+2 51+2 49+2 49+2 49+2 51+2 48+2 48+2 48+2 52+2 47+2 47+2 47+2 52+2 56+2 51+2 51+2 51+2 56+2 56+2 54+2 54+2 54+2 56+2 49.0 37.0 44.0 46.0 29.0 50.0 43.0 34.0 30.0 52.0 74.0 78.0 55.0 58.0 65.0 33.0 62.0 65.0 63.0 35.0 58.0 56.0 49.0 44.0 42.0 48.0 52.0 54.0 56.0 10.0 13.0 7.0 11.0 10.0 6.060000e+05 
1
2
3

# 데이터 시각화를 위해 seabron 로드 

import seaborn as sns 

1
2
3

# Arsenal F.C의 나이대분포 확인

sns.countplot(gunners['Age'])

<AxesSubplot:xlabel='Age', ylabel='count'>


1
2
3

# Arsenal F.C의 포지션 분포 확인

sns.countplot(gunners['Position'])

<AxesSubplot:xlabel='Position', ylabel='count'>


1
2
3

# 포지션별 능력치 분포에 이상치가 있는지 확인 - boxplot 사용

sns.boxplot(data = gunners, x = 'Position', y = 'Overall')

<AxesSubplot:xlabel='Position', ylabel='Overall'>


1
2
3

# 포지션별 잠재력 분포에 이상치가 있는지 확인 - boxplot 사용

sns.boxplot(data = gunners, x = 'Position', y = 'Potential')

<AxesSubplot:xlabel='Position', ylabel='Potential'>

결측치 찾기

1

gunners.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 33 entries, 33 to 16216
Data columns (total 81 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                33 non-null     int64  
 1   ID                        33 non-null     int64  
 2   Name                      33 non-null     object 
 3   Age                       33 non-null     int64  
 4   Nationality               33 non-null     object 
 5   Overall                   33 non-null     float64
 6   Potential                 33 non-null     float64
 7   Club                      33 non-null     object 
 8   Value                     33 non-null     float64
 9   Wage                      33 non-null     float64
 10  Preferred Foot            33 non-null     object 
 11  International Reputation  33 non-null     float64
 12  Weak Foot                 33 non-null     float64
 13  Skill Moves               33 non-null     float64
 14  Position                  33 non-null     object 
 15  Jersey Number             33 non-null     float64
 16  Joined                    33 non-null     object 
 17  Contract Valid Until      33 non-null     object 
 18  Height                    33 non-null     object 
 19  Weight                    33 non-null     object 
 20  LS                        30 non-null     object 
 21  ST                        30 non-null     object 
 22  RS                        30 non-null     object 
 23  LW                        30 non-null     object 
 24  LF                        30 non-null     object 
 25  CF                        30 non-null     object 
 26  RF                        30 non-null     object 
 27  RW                        30 non-null     object 
 28  LAM                       30 non-null     object 
 29  CAM                       30 non-null     object 
 30  RAM                       30 non-null     object 
 31  LM                        30 non-null     object 
 32  LCM                       30 non-null     object 
 33  CM                        30 non-null     object 
 34  RCM                       30 non-null     object 
 35  RM                        30 non-null     object 
 36  LWB                       30 non-null     object 
 37  LDM                       30 non-null     object 
 38  CDM                       30 non-null     object 
 39  RDM                       30 non-null     object 
 40  RWB                       30 non-null     object 
 41  LB                        30 non-null     object 
 42  LCB                       30 non-null     object 
 43  CB                        30 non-null     object 
 44  RCB                       30 non-null     object 
 45  RB                        30 non-null     object 
 46  Crossing                  33 non-null     float64
 47  Finishing                 33 non-null     float64
 48  HeadingAccuracy           33 non-null     float64
 49  ShortPassing              33 non-null     float64
 50  Volleys                   33 non-null     float64
 51  Dribbling                 33 non-null     float64
 52  Curve                     33 non-null     float64
 53  FKAccuracy                33 non-null     float64
 54  LongPassing               33 non-null     float64
 55  BallControl               33 non-null     float64
 56  Acceleration              33 non-null     float64
 57  SprintSpeed               33 non-null     float64
 58  Agility                   33 non-null     float64
 59  Reactions                 33 non-null     float64
 60  Balance                   33 non-null     float64
 61  ShotPower                 33 non-null     float64
 62  Jumping                   33 non-null     float64
 63  Stamina                   33 non-null     float64
 64  Strength                  33 non-null     float64
 65  LongShots                 33 non-null     float64
 66  Aggression                33 non-null     float64
 67  Interceptions             33 non-null     float64
 68  Positioning               33 non-null     float64
 69  Vision                    33 non-null     float64
 70  Penalties                 33 non-null     float64
 71  Composure                 33 non-null     float64
 72  Marking                   33 non-null     float64
 73  StandingTackle            33 non-null     float64
 74  SlidingTackle             33 non-null     float64
 75  GKDiving                  33 non-null     float64
 76  GKHandling                33 non-null     float64
 77  GKKicking                 33 non-null     float64
 78  GKPositioning             33 non-null     float64
 79  GKReflexes                33 non-null     float64
 80  Release Clause            33 non-null     float64
dtypes: float64(43), int64(3), object(35)
memory usage: 22.4+ KB

1
2
3
4
5
6
7

# LS ~ RB 컬럼에 3개씩 결측치가 있음
# 어떤 데이터에서 누락이 발생하는지 확인

gunners[gunners.isnull()['LS']]


# 포지션이 GK인 선수는 다른 포지션들의 값이 비어있는 것이 원인
Unnamed: 0 ID Name Age Nationality Overall Potential Club Value Wage Preferred Foot International Reputation Weak Foot Skill Moves Position Jersey Number Joined Contract Valid Until Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
131 131 192563 B. Leno 26 Germany 84.0 87.0 Arsenal 27000000.0 115000.0 Right 3.0 4.0 1.0 GK 12.0 Jul 1, 2018 2023 6'3 183lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 9.0 9.0 13.0 45.0 10.0 16.0 9.0 8.0 40.0 22.0 46.0 52.0 52.0 80.0 44.0 23.0 73.0 43.0 68.0 14.0 28.0 22.0 7.0 54.0 23.0 66.0 27.0 15.0 18.0 84.0 82.0 75.0 85.0 82.0 533000000.0
317 317 48940 P. Čech 36 Czech Republic 82.0 82.0 Arsenal 3000000.0 60000.0 Left 4.0 3.0 1.0 GK 1.0 Jun 29, 2015 2019 6'5 198lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 19.0 12.0 19.0 35.0 17.0 12.0 13.0 19.0 33.0 22.0 35.0 38.0 46.0 77.0 34.0 21.0 50.0 32.0 63.0 11.0 17.0 23.0 13.0 53.0 23.0 63.0 12.0 13.0 12.0 78.0 82.0 70.0 83.0 79.0 57000000.0
3392 3392 202811 E. Martínez 25 Argentina 72.0 77.0 Arsenal 3000000.0 40000.0 Right 1.0 3.0 1.0 GK 26.0 Jul 1, 2010 2020 6'4 187lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 22.0 16.0 26.0 27.0 23.0 13.0 22.0 20.0 46.0 26.0 59.0 57.0 49.0 64.0 49.0 18.0 60.0 14.0 70.0 16.0 37.0 13.0 12.0 50.0 42.0 53.0 11.0 20.0 20.0 73.0 69.0 75.0 70.0 74.0 58000000.0 
1
2
3
4
5

# 포지션이 GK인 선수는 다른 포지션에 대한 능력치를 부여할 필요가 없기에 NaN으로 채워져 있는 것으로 가정
# NaN값을 측정할 수 없다는 의미의 -1로 교체
# 다른 데이터 분석시 -값이 존재한다면 -1로 쓰는 것은 의미가 없다. (현재 데이터는 모두 양수값이 기에 사용)

gunners = gunners.fillna(-1)

1
2
3
4

# 결칙치가 잘 대체되었는지 확인

#gunners[gunners['Position']=='GK'] 로 결측치 있던 부분만 재확인 
gunners.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 33 entries, 33 to 16216
Data columns (total 81 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                33 non-null     int64  
 1   ID                        33 non-null     int64  
 2   Name                      33 non-null     object 
 3   Age                       33 non-null     int64  
 4   Nationality               33 non-null     object 
 5   Overall                   33 non-null     float64
 6   Potential                 33 non-null     float64
 7   Club                      33 non-null     object 
 8   Value                     33 non-null     float64
 9   Wage                      33 non-null     float64
 10  Preferred Foot            33 non-null     object 
 11  International Reputation  33 non-null     float64
 12  Weak Foot                 33 non-null     float64
 13  Skill Moves               33 non-null     float64
 14  Position                  33 non-null     object 
 15  Jersey Number             33 non-null     float64
 16  Joined                    33 non-null     object 
 17  Contract Valid Until      33 non-null     object 
 18  Height                    33 non-null     object 
 19  Weight                    33 non-null     object 
 20  LS                        33 non-null     object 
 21  ST                        33 non-null     object 
 22  RS                        33 non-null     object 
 23  LW                        33 non-null     object 
 24  LF                        33 non-null     object 
 25  CF                        33 non-null     object 
 26  RF                        33 non-null     object 
 27  RW                        33 non-null     object 
 28  LAM                       33 non-null     object 
 29  CAM                       33 non-null     object 
 30  RAM                       33 non-null     object 
 31  LM                        33 non-null     object 
 32  LCM                       33 non-null     object 
 33  CM                        33 non-null     object 
 34  RCM                       33 non-null     object 
 35  RM                        33 non-null     object 
 36  LWB                       33 non-null     object 
 37  LDM                       33 non-null     object 
 38  CDM                       33 non-null     object 
 39  RDM                       33 non-null     object 
 40  RWB                       33 non-null     object 
 41  LB                        33 non-null     object 
 42  LCB                       33 non-null     object 
 43  CB                        33 non-null     object 
 44  RCB                       33 non-null     object 
 45  RB                        33 non-null     object 
 46  Crossing                  33 non-null     float64
 47  Finishing                 33 non-null     float64
 48  HeadingAccuracy           33 non-null     float64
 49  ShortPassing              33 non-null     float64
 50  Volleys                   33 non-null     float64
 51  Dribbling                 33 non-null     float64
 52  Curve                     33 non-null     float64
 53  FKAccuracy                33 non-null     float64
 54  LongPassing               33 non-null     float64
 55  BallControl               33 non-null     float64
 56  Acceleration              33 non-null     float64
 57  SprintSpeed               33 non-null     float64
 58  Agility                   33 non-null     float64
 59  Reactions                 33 non-null     float64
 60  Balance                   33 non-null     float64
 61  ShotPower                 33 non-null     float64
 62  Jumping                   33 non-null     float64
 63  Stamina                   33 non-null     float64
 64  Strength                  33 non-null     float64
 65  LongShots                 33 non-null     float64
 66  Aggression                33 non-null     float64
 67  Interceptions             33 non-null     float64
 68  Positioning               33 non-null     float64
 69  Vision                    33 non-null     float64
 70  Penalties                 33 non-null     float64
 71  Composure                 33 non-null     float64
 72  Marking                   33 non-null     float64
 73  StandingTackle            33 non-null     float64
 74  SlidingTackle             33 non-null     float64
 75  GKDiving                  33 non-null     float64
 76  GKHandling                33 non-null     float64
 77  GKKicking                 33 non-null     float64
 78  GKPositioning             33 non-null     float64
 79  GKReflexes                33 non-null     float64
 80  Release Clause            33 non-null     float64
dtypes: float64(43), int64(3), object(35)
memory usage: 22.4+ KB




Arsenal F.C는 어떤 포지션을 보강해야 할까?

a. 전처리

1
2
3
4

# Chelsea F.C.선수와 능력치 비교를 하기 위해 추가로 Chelsea 선수들의 데이터가 담긴 테이블 생성

blues = data[data['Club']== 'Chelsea']
blues.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 33 entries, 5 to 16806
Data columns (total 81 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                33 non-null     int64  
 1   ID                        33 non-null     int64  
 2   Name                      33 non-null     object 
 3   Age                       33 non-null     int64  
 4   Nationality               33 non-null     object 
 5   Overall                   33 non-null     float64
 6   Potential                 33 non-null     float64
 7   Club                      33 non-null     object 
 8   Value                     33 non-null     float64
 9   Wage                      33 non-null     float64
 10  Preferred Foot            33 non-null     object 
 11  International Reputation  33 non-null     float64
 12  Weak Foot                 33 non-null     float64
 13  Skill Moves               33 non-null     float64
 14  Position                  33 non-null     object 
 15  Jersey Number             33 non-null     float64
 16  Joined                    32 non-null     object 
 17  Contract Valid Until      33 non-null     object 
 18  Height                    33 non-null     object 
 19  Weight                    33 non-null     object 
 20  LS                        29 non-null     object 
 21  ST                        29 non-null     object 
 22  RS                        29 non-null     object 
 23  LW                        29 non-null     object 
 24  LF                        29 non-null     object 
 25  CF                        29 non-null     object 
 26  RF                        29 non-null     object 
 27  RW                        29 non-null     object 
 28  LAM                       29 non-null     object 
 29  CAM                       29 non-null     object 
 30  RAM                       29 non-null     object 
 31  LM                        29 non-null     object 
 32  LCM                       29 non-null     object 
 33  CM                        29 non-null     object 
 34  RCM                       29 non-null     object 
 35  RM                        29 non-null     object 
 36  LWB                       29 non-null     object 
 37  LDM                       29 non-null     object 
 38  CDM                       29 non-null     object 
 39  RDM                       29 non-null     object 
 40  RWB                       29 non-null     object 
 41  LB                        29 non-null     object 
 42  LCB                       29 non-null     object 
 43  CB                        29 non-null     object 
 44  RCB                       29 non-null     object 
 45  RB                        29 non-null     object 
 46  Crossing                  33 non-null     float64
 47  Finishing                 33 non-null     float64
 48  HeadingAccuracy           33 non-null     float64
 49  ShortPassing              33 non-null     float64
 50  Volleys                   33 non-null     float64
 51  Dribbling                 33 non-null     float64
 52  Curve                     33 non-null     float64
 53  FKAccuracy                33 non-null     float64
 54  LongPassing               33 non-null     float64
 55  BallControl               33 non-null     float64
 56  Acceleration              33 non-null     float64
 57  SprintSpeed               33 non-null     float64
 58  Agility                   33 non-null     float64
 59  Reactions                 33 non-null     float64
 60  Balance                   33 non-null     float64
 61  ShotPower                 33 non-null     float64
 62  Jumping                   33 non-null     float64
 63  Stamina                   33 non-null     float64
 64  Strength                  33 non-null     float64
 65  LongShots                 33 non-null     float64
 66  Aggression                33 non-null     float64
 67  Interceptions             33 non-null     float64
 68  Positioning               33 non-null     float64
 69  Vision                    33 non-null     float64
 70  Penalties                 33 non-null     float64
 71  Composure                 33 non-null     float64
 72  Marking                   33 non-null     float64
 73  StandingTackle            33 non-null     float64
 74  SlidingTackle             33 non-null     float64
 75  GKDiving                  33 non-null     float64
 76  GKHandling                33 non-null     float64
 77  GKKicking                 33 non-null     float64
 78  GKPositioning             33 non-null     float64
 79  GKReflexes                33 non-null     float64
 80  Release Clause            32 non-null     float64
dtypes: float64(43), int64(3), object(35)
memory usage: 21.1+ KB

1
2
3

# 결측치 확인

blues[blues.isnull()['LS']]
Unnamed: 0 ID Name Age Nationality Overall Potential Club Value Wage Preferred Foot International Reputation Weak Foot Skill Moves Position Jersey Number Joined Contract Valid Until Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
177 177 206585 Kepa 23 Spain 83.0 91.0 Chelsea 285000000.0 84000.0 Right 1.0 4.0 1.0 GK 13.0 Aug 8, 2018 2025 6'1 187lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 25.0 11.0 17.0 32.0 18.0 10.0 25.0 19.0 36.0 16.0 34.0 33.0 41.0 79.0 39.0 25.0 71.0 37.0 51.0 14.0 18.0 21.0 15.0 55.0 24.0 63.0 19.0 18.0 16.0 84.0 80.0 76.0 80.0 84.0 584000000.0
1287 1287 139062 W. Caballero 36 Argentina 76.0 76.0 Chelsea 1000000.0 39000.0 Right 2.0 3.0 1.0 GK 13.0 Jul 1, 2017 2019 6'1 179lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 12.0 11.0 22.0 33.0 13.0 12.0 23.0 11.0 48.0 30.0 43.0 46.0 39.0 73.0 55.0 23.0 70.0 37.0 69.0 14.0 30.0 19.0 7.0 47.0 16.0 57.0 14.0 11.0 12.0 75.0 72.0 71.0 77.0 76.0 19000000.0
3937 3937 101880 R. Green 38 England 72.0 72.0 Chelsea 210000.0 28000.0 Right 2.0 3.0 1.0 GK 31.0 Jul 26, 2018 2019 6'2 203lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 19.0 12.0 15.0 26.0 12.0 14.0 16.0 13.0 14.0 15.0 46.0 32.0 47.0 70.0 40.0 22.0 68.0 26.0 59.0 11.0 26.0 20.0 13.0 58.0 21.0 49.0 14.0 12.0 11.0 73.0 70.0 60.0 72.0 74.0 389000.0
16806 16806 238186 M. Bulka 18 Poland 56.0 74.0 Chelsea 150000.0 2000.0 Right 1.0 2.0 1.0 GK 59.0 Oct 4, 2016 2019 6'6 198lbs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 12.0 8.0 10.0 28.0 5.0 7.0 13.0 14.0 27.0 13.0 28.0 25.0 42.0 49.0 28.0 22.0 43.0 20.0 70.0 7.0 25.0 11.0 8.0 38.0 18.0 42.0 12.0 10.0 14.0 57.0 54.0 56.0 52.0 59.0 371000.0 
1
2
3
4

# Arsenal과 마찬가지로 GK 포지션의 선수들에서 결측치가 발생 동일한 방식으로 -1을 입력하여 해결

blues = blues.fillna(-1)
blues.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 33 entries, 5 to 16806
Data columns (total 81 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                33 non-null     int64  
 1   ID                        33 non-null     int64  
 2   Name                      33 non-null     object 
 3   Age                       33 non-null     int64  
 4   Nationality               33 non-null     object 
 5   Overall                   33 non-null     float64
 6   Potential                 33 non-null     float64
 7   Club                      33 non-null     object 
 8   Value                     33 non-null     float64
 9   Wage                      33 non-null     float64
 10  Preferred Foot            33 non-null     object 
 11  International Reputation  33 non-null     float64
 12  Weak Foot                 33 non-null     float64
 13  Skill Moves               33 non-null     float64
 14  Position                  33 non-null     object 
 15  Jersey Number             33 non-null     float64
 16  Joined                    33 non-null     object 
 17  Contract Valid Until      33 non-null     object 
 18  Height                    33 non-null     object 
 19  Weight                    33 non-null     object 
 20  LS                        33 non-null     object 
 21  ST                        33 non-null     object 
 22  RS                        33 non-null     object 
 23  LW                        33 non-null     object 
 24  LF                        33 non-null     object 
 25  CF                        33 non-null     object 
 26  RF                        33 non-null     object 
 27  RW                        33 non-null     object 
 28  LAM                       33 non-null     object 
 29  CAM                       33 non-null     object 
 30  RAM                       33 non-null     object 
 31  LM                        33 non-null     object 
 32  LCM                       33 non-null     object 
 33  CM                        33 non-null     object 
 34  RCM                       33 non-null     object 
 35  RM                        33 non-null     object 
 36  LWB                       33 non-null     object 
 37  LDM                       33 non-null     object 
 38  CDM                       33 non-null     object 
 39  RDM                       33 non-null     object 
 40  RWB                       33 non-null     object 
 41  LB                        33 non-null     object 
 42  LCB                       33 non-null     object 
 43  CB                        33 non-null     object 
 44  RCB                       33 non-null     object 
 45  RB                        33 non-null     object 
 46  Crossing                  33 non-null     float64
 47  Finishing                 33 non-null     float64
 48  HeadingAccuracy           33 non-null     float64
 49  ShortPassing              33 non-null     float64
 50  Volleys                   33 non-null     float64
 51  Dribbling                 33 non-null     float64
 52  Curve                     33 non-null     float64
 53  FKAccuracy                33 non-null     float64
 54  LongPassing               33 non-null     float64
 55  BallControl               33 non-null     float64
 56  Acceleration              33 non-null     float64
 57  SprintSpeed               33 non-null     float64
 58  Agility                   33 non-null     float64
 59  Reactions                 33 non-null     float64
 60  Balance                   33 non-null     float64
 61  ShotPower                 33 non-null     float64
 62  Jumping                   33 non-null     float64
 63  Stamina                   33 non-null     float64
 64  Strength                  33 non-null     float64
 65  LongShots                 33 non-null     float64
 66  Aggression                33 non-null     float64
 67  Interceptions             33 non-null     float64
 68  Positioning               33 non-null     float64
 69  Vision                    33 non-null     float64
 70  Penalties                 33 non-null     float64
 71  Composure                 33 non-null     float64
 72  Marking                   33 non-null     float64
 73  StandingTackle            33 non-null     float64
 74  SlidingTackle             33 non-null     float64
 75  GKDiving                  33 non-null     float64
 76  GKHandling                33 non-null     float64
 77  GKKicking                 33 non-null     float64
 78  GKPositioning             33 non-null     float64
 79  GKReflexes                33 non-null     float64
 80  Release Clause            33 non-null     float64
dtypes: float64(43), int64(3), object(35)
memory usage: 21.1+ KB

1

data.Position.unique()

array(['RF', 'ST', 'LW', 'GK', 'RCM', 'LF', 'RS', 'RCB', 'LCM', 'CB',
       'LDM', 'CAM', 'CDM', 'LS', 'LCB', 'RM', 'LAM', 'LB', 'RDM', 'RW',
       'LM', 'CM', 'RB', 'RAM', 'CF', 'RWB', 'LWB', nan], dtype=object)

주전선수 비교를 위하여 Arsenal 과 Chelsea의 주전을 선정

  • 기본 전술포메이션은 4-4-2 (공격수 - 미드필더 - 수비수) 로 통일 (GK는 포메이션명에서 제외 - 굳이 쓰자면 4-4-2-1)

  • 즉, GK : 1명, CB : 4명 MF : 4명 ST : 2명을 선발

  • 선발의 기준은 현재능력치

  • GK 리스트 = GK

  • CB 리스트 = CB, LCB, RCB, RB, LB

  • MF 리스트 = RCM, LCM, RDM, LDM, CDM, CM, LM, RM, CAM

  • ST 리스트 = ST, LW, RW, LS, RS

1
2
3
4
5

# 각 포지션 별 리스트 작성
st_list = ['ST', 'LW', 'RW', 'LS','RS','LF','RF','CF']
mf_list = ['RCM', 'LCM', 'RDM','LDM', 'CDM', 'CM','LM', 'RM', 'CAM', 'LAM','RAM']
cb_list = ['CB', 'LCB', 'RCB', 'RB', 'LB', 'LWB','RWB']
gk_list = ['GK']

아스널 라인업 선발

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

# 각 포지션별 남은 자리 숫자를 입력
st_num = 2
mf_num = 4
cb_num = 4
gk_num = 1

lineup=[]
for index in gunners.index:
    # gk 포지션 선발
    if gunners['Position'][index] in gk_list:  # 포지션이 gk인 선수들 중에서
        if gk_num != 0:  # gk 포지션에 남은 자리가 0이 아니라면 (= 있다면)
            lineup.append(gunners['ID'][index])  # 해당 선수의 ID를 lineup에 입력하고
            gk_num -= 1  # gk_num (선수자리)의 값을 -1 해라

            # st 포지션 선발
    elif gunners['Position'][index] in st_list:  # 포지션이 st인 선수들 중에서
        if st_num != 0:  # st 포지션에 남은 자리가 0이 아니라면( = 있다면)
            lineup.append(gunners['ID'][index])  # 해당 선수의 ID를 lineup에 입력하고
            st_num -= 1  # st_num(선수 자리)의 값을 -1 해라
            gunners['Position'][index] = 'ST' #포지션은 ST 로 통일
    # mf포지션 선발
    elif gunners['Position'][index] in mf_list:  # 포지션이 mf인 선수들 중에서
        if mf_num != 0:  # mf 포지션에 남은 자리가 0이 아니라면( = 있다면)
            lineup.append(gunners['ID'][index])  # 해당 선수의 ID를 lineup에 입력하고
            mf_num -= 1  # mf_num(선수 자리)의 값을 -1 해라
            gunners['Position'][index] = 'MF' #포지션은 MF 로 통일

    # cb 포지션 선발
    else:
        if cb_num != 0:  # cb 포지션에 남은 자리가 0이 아니라면( = 있다면)
            lineup.append(gunners['ID'][index])  # 해당 선수의 ID를 lineup에 입력하고
            cb_num -= 1  # cb_num(선수 자리)의 값을 -1 해라
            gunners['Position'][index] = 'CB' #포지션은 CB 로 통일

1
2

gunners_lineup = gunners[gunners['ID'].isin(lineup)]
gunners_lineup
Unnamed: 0 ID Name Age Nationality Overall Potential Club Value Wage Preferred Foot International Reputation Weak Foot Skill Moves Position Jersey Number Joined Contract Valid Until Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
33 33 188567 P. Aubameyang 29 Gabon 88.0 88.0 Arsenal 59000000.0 265000.0 Right 3.0 4.0 4.0 ST 14.0 Jan 31, 2018 2021 6'2 176lbs 84+3 84+3 84+3 83+3 83+3 83+3 83+3 83+3 81+3 81+3 81+3 81+3 73+3 73+3 73+3 81+3 64+3 58+3 58+3 58+3 64+3 61+3 52+3 52+3 52+3 61+3 77.0 88.0 79.0 77.0 86.0 79.0 80.0 74.0 64.0 82.0 93.0 95.0 76.0 87.0 70.0 82.0 79.0 76.0 76.0 79.0 43.0 48.0 90.0 77.0 76.0 86.0 27.0 25.0 36.0 6.0 9.0 15.0 9.0 9.0 1.121000e+09
74 74 176635 M. Özil 29 Germany 86.0 86.0 Arsenal 435000000.0 190000.0 Left 4.0 2.0 4.0 MF 10.0 Sep 2, 2013 2021 5'11 168lbs 75+3 75+3 75+3 82+3 81+3 81+3 81+3 82+3 84+3 84+3 84+3 82+3 78+3 78+3 78+3 82+3 57+3 56+3 56+3 56+3 57+3 52+3 40+3 40+3 40+3 52+3 83.0 73.0 54.0 89.0 80.0 84.0 84.0 77.0 82.0 90.0 72.0 69.0 79.0 84.0 70.0 70.0 48.0 69.0 57.0 75.0 48.0 24.0 83.0 91.0 67.0 82.0 24.0 16.0 19.0 6.0 14.0 10.0 6.0 14.0 8.270000e+08
87 87 193301 A. Lacazette 27 France 85.0 86.0 Arsenal 45000000.0 180000.0 Right 3.0 4.0 4.0 ST 9.0 Jul 5, 2017 2022 5'9 161lbs 82+3 82+3 82+3 81+3 83+3 83+3 83+3 81+3 81+3 81+3 81+3 79+3 74+3 74+3 74+3 79+3 62+3 60+3 60+3 60+3 62+3 59+3 54+3 54+3 54+3 59+3 64.0 88.0 73.0 80.0 81.0 85.0 74.0 71.0 59.0 83.0 81.0 84.0 85.0 85.0 85.0 83.0 76.0 79.0 73.0 75.0 70.0 42.0 86.0 76.0 84.0 85.0 29.0 42.0 30.0 11.0 6.0 9.0 5.0 6.0 8.890000e+08
131 131 192563 B. Leno 26 Germany 84.0 87.0 Arsenal 27000000.0 115000.0 Right 3.0 4.0 1.0 GK 12.0 Jul 1, 2018 2023 6'3 183lbs -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 9.0 9.0 13.0 45.0 10.0 16.0 9.0 8.0 40.0 22.0 46.0 52.0 52.0 80.0 44.0 23.0 73.0 43.0 68.0 14.0 28.0 22.0 7.0 54.0 23.0 66.0 27.0 15.0 18.0 84.0 82.0 75.0 85.0 82.0 5.330000e+08
146 146 172879 Sokratis 30 Greece 84.0 84.0 Arsenal 23000000.0 140000.0 Right 3.0 2.0 2.0 CB 19.0 Jul 2, 2018 2021 6'1 187lbs 62+3 62+3 62+3 56+3 58+3 58+3 58+3 56+3 56+3 56+3 56+3 57+3 59+3 59+3 59+3 57+3 70+3 71+3 71+3 71+3 70+3 73+3 81+3 81+3 81+3 73+3 47.0 44.0 85.0 60.0 46.0 58.0 29.0 35.0 51.0 58.0 64.0 73.0 60.0 81.0 64.0 77.0 86.0 69.0 86.0 48.0 88.0 77.0 49.0 45.0 24.0 82.0 85.0 87.0 85.0 6.0 8.0 5.0 12.0 5.0 4.370000e+08
199 199 192883 H. Mkhitaryan 29 Armenia 83.0 83.0 Arsenal 255000000.0 145000.0 Right 3.0 5.0 4.0 MF 7.0 Jan 22, 2018 2021 5'10 165lbs 76+3 76+3 76+3 81+3 80+3 80+3 80+3 81+3 81+3 81+3 81+3 80+3 78+3 78+3 78+3 80+3 70+3 68+3 68+3 68+3 70+3 67+3 59+3 59+3 59+3 67+3 77.0 74.0 53.0 80.0 81.0 84.0 83.0 64.0 71.0 86.0 84.0 78.0 87.0 79.0 85.0 85.0 69.0 83.0 62.0 73.0 69.0 64.0 82.0 84.0 72.0 76.0 41.0 53.0 55.0 5.0 11.0 14.0 5.0 7.0 4.850000e+08
235 235 223959 L. Torreira 22 Uruguay 82.0 87.0 Arsenal 25000000.0 98000.0 Right 2.0 3.0 3.0 MF 14.0 Jul 12, 2018 2023 5'6 141lbs 68+2 68+2 68+2 74+2 73+2 73+2 73+2 74+2 75+2 75+2 75+2 75+2 78+2 78+2 78+2 75+2 80+2 81+2 81+2 81+2 80+2 79+2 77+2 77+2 77+2 79+2 74.0 50.0 55.0 83.0 65.0 78.0 66.0 76.0 77.0 81.0 81.0 74.0 83.0 79.0 93.0 78.0 52.0 85.0 66.0 74.0 85.0 87.0 65.0 74.0 48.0 78.0 84.0 84.0 75.0 5.0 5.0 13.0 7.0 9.0 4.940000e+08
281 281 192227 S. Mustafi 26 Germany 82.0 84.0 Arsenal 225000000.0 115000.0 Right 3.0 3.0 2.0 CB 20.0 Aug 30, 2016 2021 6'0 181lbs 64+3 64+3 64+3 61+3 62+3 62+3 62+3 61+3 63+3 63+3 63+3 62+3 67+3 67+3 67+3 62+3 72+3 76+3 76+3 76+3 72+3 74+3 80+3 80+3 80+3 74+3 53.0 58.0 81.0 77.0 51.0 53.0 55.0 47.0 69.0 70.0 58.0 65.0 63.0 82.0 67.0 66.0 83.0 68.0 78.0 52.0 82.0 82.0 50.0 55.0 54.0 73.0 80.0 82.0 82.0 11.0 9.0 15.0 10.0 6.0 4.440000e+08
289 289 186561 A. Ramsey 27 Wales 82.0 83.0 Arsenal 25000000.0 130000.0 Right 3.0 3.0 3.0 MF 10.0 Jun 13, 2008 2019 6'0 168lbs 75+3 75+3 75+3 77+3 78+3 78+3 78+3 77+3 79+3 79+3 79+3 78+3 80+3 80+3 80+3 78+3 75+3 75+3 75+3 75+3 75+3 72+3 69+3 69+3 69+3 72+3 75.0 74.0 58.0 84.0 79.0 81.0 70.0 66.0 80.0 82.0 64.0 69.0 73.0 81.0 74.0 81.0 66.0 89.0 67.0 74.0 73.0 69.0 84.0 79.0 75.0 81.0 66.0 70.0 67.0 6.0 11.0 5.0 10.0 8.0 4.940000e+08
311 311 165229 L. Koscielny 32 France 82.0 82.0 Arsenal 125000000.0 115000.0 Right 3.0 3.0 2.0 CB 6.0 Jul 7, 2010 2020 6'1 165lbs 57+3 57+3 57+3 58+3 58+3 58+3 58+3 58+3 60+3 60+3 60+3 60+3 65+3 65+3 65+3 60+3 71+3 75+3 75+3 75+3 71+3 74+3 80+3 80+3 80+3 74+3 52.0 32.0 80.0 76.0 35.0 62.0 22.0 49.0 69.0 66.0 64.0 66.0 65.0 76.0 61.0 54.0 78.0 62.0 75.0 47.0 86.0 84.0 41.0 56.0 51.0 75.0 80.0 84.0 83.0 13.0 11.0 9.0 11.0 7.0 2.380000e+08
464 464 203747 Héctor Bellerín 23 Spain 80.0 85.0 Arsenal 165000000.0 85000.0 Right 3.0 3.0 3.0 CB 2.0 Jul 1, 2014 2023 5'10 163lbs 65+3 65+3 65+3 73+3 70+3 70+3 70+3 73+3 71+3 71+3 71+3 75+3 71+3 71+3 71+3 75+3 79+3 74+3 74+3 74+3 79+3 79+3 73+3 73+3 73+3 79+3 76.0 52.0 60.0 76.0 41.0 75.0 63.0 50.0 59.0 78.0 94.0 91.0 82.0 78.0 77.0 52.0 63.0 84.0 58.0 43.0 72.0 77.0 67.0 66.0 62.0 70.0 75.0 78.0 80.0 8.0 14.0 14.0 13.0 12.0 3.260000e+08

선발된 선수들의 포지션을 4가지 유형으로 분류 한다.

첼시 라인업 선발

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

# 각 포지션별 남은 자리 숫자를 입력
st_num = 2
mf_num = 4
cb_num = 4
gk_num = 1

lineup=[]
for index in blues.index:
    # gk 포지션 선발
    if blues['Position'][index] in gk_list:  # 포지션이 gk인 선수들 중에서
        if gk_num != 0:  # gk 포지션에 남은 자리가 0이 아니라면 (= 있다면)
            lineup.append(blues['ID'][index])  # 해당 선수의 ID를 lineup에 입력하고
            gk_num -= 1  # gk_num (선수자리)의 값을 -1 해라

            # st 포지션 선발
    elif blues['Position'][index] in st_list:  # 포지션이 st인 선수들 중에서
        if st_num != 0:  # st 포지션에 남은 자리가 0이 아니라면( = 있다면)
            lineup.append(blues['ID'][index])  # 해당 선수의 ID를 lineup에 입력하고
            st_num -= 1  # st_num(선수 자리)의 값을 -1 해라
            blues['Position'][index] = 'ST' #포지션은 ST 로 통일
    # mf포지션 선발
    elif blues['Position'][index] in mf_list:  # 포지션이 mf인 선수들 중에서
        if mf_num != 0:  # mf 포지션에 남은 자리가 0이 아니라면( = 있다면)
            lineup.append(blues['ID'][index])  # 해당 선수의 ID를 lineup에 입력하고
            mf_num -= 1  # mf_num(선수 자리)의 값을 -1 해라
            blues['Position'][index] = 'MF' #포지션은 MF 로 통일

    # cb 포지션 선발
    else:
        if cb_num != 0:  # cb 포지션에 남은 자리가 0이 아니라면( = 있다면)
            lineup.append(blues['ID'][index])  # 해당 선수의 ID를 lineup에 입력하고
            cb_num -= 1  # cb_num(선수 자리)의 값을 -1 해라
            blues['Position'][index] = 'CB' #포지션은 CB 로 통일

1
2

blues_lineup = blues[blues['ID'].isin(lineup)]
blues_lineup
Unnamed: 0 ID Name Age Nationality Overall Potential Club Value Wage Preferred Foot International Reputation Weak Foot Skill Moves Position Jersey Number Joined Contract Valid Until Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
5 5 183277 E. Hazard 27 Belgium 91.0 91.0 Chelsea 93000000.0 340000.0 Right 4.0 4.0 4.0 ST 10.0 Jul 1, 2012 2020 5'8 163lbs 83+3 83+3 83+3 89+3 88+3 88+3 88+3 89+3 89+3 89+3 89+3 89+3 82+3 82+3 82+3 89+3 66+3 63+3 63+3 63+3 66+3 60+3 49+3 49+3 49+3 60+3 81.0 84.0 61.0 89.0 80.0 95.0 83.0 79.0 83.0 94.0 94.0 88.0 95.0 90.0 94.0 82.0 56.0 83.0 66.0 80.0 54.0 41.0 87.0 89.0 86.0 91.0 34.0 27.0 22.0 11.0 12.0 6.0 8.0 8.0 1.721000e+09
14 14 215914 N. Kanté 27 France 89.0 90.0 Chelsea 63000000.0 225000.0 Right 3.0 3.0 2.0 MF 13.0 Jul 16, 2016 2023 5'6 159lbs 72+3 72+3 72+3 77+3 77+3 77+3 77+3 77+3 79+3 79+3 79+3 79+3 82+3 82+3 82+3 79+3 85+3 87+3 87+3 87+3 85+3 84+3 83+3 83+3 83+3 84+3 68.0 65.0 54.0 86.0 56.0 79.0 49.0 49.0 81.0 80.0 82.0 78.0 82.0 93.0 92.0 71.0 77.0 96.0 76.0 69.0 90.0 92.0 71.0 79.0 54.0 85.0 90.0 91.0 85.0 15.0 12.0 10.0 7.0 10.0 1.213000e+09
69 69 184432 Azpilicueta 28 Spain 86.0 86.0 Chelsea 35000000.0 175000.0 Right 3.0 3.0 2.0 CB 14.0 Aug 24, 2012 2022 5'10 168lbs 67+3 67+3 67+3 71+3 70+3 70+3 70+3 71+3 72+3 72+3 72+3 74+3 77+3 77+3 77+3 74+3 83+3 83+3 83+3 83+3 83+3 84+3 83+3 83+3 83+3 84+3 81.0 46.0 76.0 81.0 51.0 69.0 68.0 58.0 80.0 77.0 76.0 76.0 72.0 88.0 73.0 68.0 76.0 87.0 71.0 60.0 82.0 89.0 62.0 74.0 66.0 78.0 88.0 90.0 86.0 13.0 9.0 10.0 6.0 5.0 6.480000e+08
121 121 205498 Jorginho 26 Italy 84.0 87.0 Chelsea 38000000.0 155000.0 Right 2.0 3.0 3.0 MF 8.0 Jul 16, 2018 2023 5'11 148lbs 70+2 70+2 70+2 77+2 76+2 76+2 76+2 77+2 80+2 80+2 80+2 79+2 82+2 82+2 82+2 79+2 77+2 79+2 79+2 79+2 77+2 75+2 72+2 72+2 72+2 75+2 75.0 57.0 56.0 89.0 71.0 82.0 75.0 76.0 87.0 85.0 75.0 69.0 80.0 83.0 73.0 71.0 58.0 79.0 66.0 62.0 79.0 78.0 72.0 87.0 84.0 79.0 63.0 78.0 70.0 14.0 7.0 13.0 14.0 8.0 7.320000e+08
140 140 180403 Willian 29 Brazil 84.0 84.0 Chelsea 305000000.0 175000.0 Right 3.0 4.0 4.0 ST 22.0 Aug 28, 2013 2020 5'9 172lbs 74+3 74+3 74+3 82+3 81+3 81+3 81+3 82+3 82+3 82+3 82+3 82+3 79+3 79+3 79+3 82+3 71+3 67+3 67+3 67+3 71+3 66+3 54+3 54+3 54+3 66+3 76.0 77.0 31.0 80.0 59.0 88.0 82.0 83.0 77.0 84.0 91.0 86.0 89.0 82.0 79.0 79.0 41.0 85.0 60.0 81.0 44.0 55.0 80.0 83.0 70.0 79.0 43.0 57.0 51.0 14.0 12.0 13.0 7.0 13.0 5.640000e+08
176 176 207410 M. Kovačić 24 Croatia 83.0 89.0 Chelsea 35000000.0 165000.0 Right 3.0 4.0 4.0 MF 17.0 -1 Jun 30, 2019 5'9 172lbs 71+3 71+3 71+3 79+3 78+3 78+3 78+3 79+3 81+3 81+3 81+3 80+3 82+3 82+3 82+3 80+3 76+3 78+3 78+3 78+3 76+3 74+3 69+3 69+3 69+3 74+3 73.0 62.0 42.0 87.0 76.0 88.0 78.0 68.0 82.0 89.0 84.0 74.0 81.0 70.0 85.0 79.0 63.0 82.0 65.0 75.0 77.0 78.0 69.0 88.0 59.0 72.0 69.0 75.0 63.0 9.0 5.0 6.0 7.0 7.0 -1.000000e+00
177 177 206585 Kepa 23 Spain 83.0 91.0 Chelsea 285000000.0 84000.0 Right 1.0 4.0 1.0 GK 13.0 Aug 8, 2018 2025 6'1 187lbs -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 25.0 11.0 17.0 32.0 18.0 10.0 25.0 19.0 36.0 16.0 34.0 33.0 41.0 79.0 39.0 25.0 71.0 37.0 51.0 14.0 18.0 21.0 15.0 55.0 24.0 63.0 19.0 18.0 16.0 84.0 80.0 76.0 80.0 84.0 5.840000e+08
200 200 192638 Marcos Alonso 27 Spain 83.0 84.0 Chelsea 24000000.0 130000.0 Left 2.0 3.0 3.0 CB 3.0 Aug 31, 2016 2023 6'2 192lbs 78+2 78+2 78+2 77+2 78+2 78+2 78+2 77+2 77+2 77+2 77+2 79+2 79+2 79+2 79+2 79+2 81+2 80+2 80+2 80+2 81+2 81+2 79+2 79+2 79+2 81+2 85.0 68.0 83.0 84.0 70.0 79.0 85.0 86.0 73.0 83.0 69.0 76.0 64.0 84.0 58.0 84.0 71.0 90.0 76.0 77.0 75.0 79.0 79.0 69.0 64.0 76.0 79.0 82.0 79.0 9.0 7.0 13.0 9.0 12.0 4.620000e+08
209 209 179944 David Luiz 31 Brazil 83.0 83.0 Chelsea 175000000.0 130000.0 Right 4.0 3.0 3.0 CB 30.0 Aug 31, 2016 2019 6'2 190lbs 68+3 68+3 68+3 67+3 67+3 67+3 67+3 67+3 69+3 69+3 69+3 68+3 73+3 73+3 73+3 68+3 74+3 78+3 78+3 78+3 74+3 75+3 80+3 80+3 80+3 75+3 68.0 55.0 81.0 77.0 63.0 66.0 70.0 76.0 77.0 79.0 59.0 62.0 69.0 75.0 56.0 79.0 78.0 66.0 79.0 73.0 85.0 85.0 54.0 69.0 75.0 77.0 79.0 83.0 82.0 11.0 12.0 10.0 7.0 14.0 3.240000e+08
219 219 162895 Cesc Fàbregas 31 Spain 83.0 83.0 Chelsea 22000000.0 140000.0 Right 4.0 3.0 3.0 MF 4.0 Jul 1, 2014 2019 5'10 170lbs 74+3 74+3 74+3 76+3 77+3 77+3 77+3 76+3 80+3 80+3 80+3 77+3 79+3 79+3 79+3 77+3 68+3 70+3 70+3 70+3 68+3 66+3 62+3 62+3 62+3 66+3 85.0 72.0 74.0 90.0 81.0 79.0 78.0 81.0 89.0 85.0 56.0 54.0 62.0 81.0 75.0 77.0 63.0 56.0 61.0 70.0 45.0 50.0 72.0 91.0 78.0 92.0 64.0 59.0 58.0 6.0 10.0 8.0 15.0 15.0 4.070000e+08
257 257 205452 A. Rüdiger 25 Germany 82.0 84.0 Chelsea 225000000.0 110000.0 Right 2.0 3.0 2.0 CB 16.0 Jul 9, 2017 2022 6'3 187lbs 58+2 58+2 58+2 59+2 58+2 58+2 58+2 59+2 60+2 60+2 60+2 62+2 65+2 65+2 65+2 62+2 74+2 76+2 76+2 76+2 74+2 76+2 81+2 81+2 81+2 76+2 68.0 27.0 80.0 75.0 34.0 59.0 35.0 56.0 72.0 69.0 67.0 78.0 59.0 82.0 50.0 70.0 73.0 60.0 82.0 41.0 88.0 80.0 33.0 57.0 40.0 72.0 82.0 85.0 82.0 14.0 15.0 10.0 12.0 8.0 4.330000e+08 
1
2
3
4
5

 # 두팀의 주전선수가 모인 데이터를 생성

london = pd.concat([gunners_lineup, blues_lineup])
print(len(london))
london

22
Unnamed: 0 ID Name Age Nationality Overall Potential Club Value Wage Preferred Foot International Reputation Weak Foot Skill Moves Position Jersey Number Joined Contract Valid Until Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
33 33 188567 P. Aubameyang 29 Gabon 88.0 88.0 Arsenal 59000000.0 265000.0 Right 3.0 4.0 4.0 ST 14.0 Jan 31, 2018 2021 6'2 176lbs 84+3 84+3 84+3 83+3 83+3 83+3 83+3 83+3 81+3 81+3 81+3 81+3 73+3 73+3 73+3 81+3 64+3 58+3 58+3 58+3 64+3 61+3 52+3 52+3 52+3 61+3 77.0 88.0 79.0 77.0 86.0 79.0 80.0 74.0 64.0 82.0 93.0 95.0 76.0 87.0 70.0 82.0 79.0 76.0 76.0 79.0 43.0 48.0 90.0 77.0 76.0 86.0 27.0 25.0 36.0 6.0 9.0 15.0 9.0 9.0 1.121000e+09
74 74 176635 M. Özil 29 Germany 86.0 86.0 Arsenal 435000000.0 190000.0 Left 4.0 2.0 4.0 MF 10.0 Sep 2, 2013 2021 5'11 168lbs 75+3 75+3 75+3 82+3 81+3 81+3 81+3 82+3 84+3 84+3 84+3 82+3 78+3 78+3 78+3 82+3 57+3 56+3 56+3 56+3 57+3 52+3 40+3 40+3 40+3 52+3 83.0 73.0 54.0 89.0 80.0 84.0 84.0 77.0 82.0 90.0 72.0 69.0 79.0 84.0 70.0 70.0 48.0 69.0 57.0 75.0 48.0 24.0 83.0 91.0 67.0 82.0 24.0 16.0 19.0 6.0 14.0 10.0 6.0 14.0 8.270000e+08
87 87 193301 A. Lacazette 27 France 85.0 86.0 Arsenal 45000000.0 180000.0 Right 3.0 4.0 4.0 ST 9.0 Jul 5, 2017 2022 5'9 161lbs 82+3 82+3 82+3 81+3 83+3 83+3 83+3 81+3 81+3 81+3 81+3 79+3 74+3 74+3 74+3 79+3 62+3 60+3 60+3 60+3 62+3 59+3 54+3 54+3 54+3 59+3 64.0 88.0 73.0 80.0 81.0 85.0 74.0 71.0 59.0 83.0 81.0 84.0 85.0 85.0 85.0 83.0 76.0 79.0 73.0 75.0 70.0 42.0 86.0 76.0 84.0 85.0 29.0 42.0 30.0 11.0 6.0 9.0 5.0 6.0 8.890000e+08
131 131 192563 B. Leno 26 Germany 84.0 87.0 Arsenal 27000000.0 115000.0 Right 3.0 4.0 1.0 GK 12.0 Jul 1, 2018 2023 6'3 183lbs -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 9.0 9.0 13.0 45.0 10.0 16.0 9.0 8.0 40.0 22.0 46.0 52.0 52.0 80.0 44.0 23.0 73.0 43.0 68.0 14.0 28.0 22.0 7.0 54.0 23.0 66.0 27.0 15.0 18.0 84.0 82.0 75.0 85.0 82.0 5.330000e+08
146 146 172879 Sokratis 30 Greece 84.0 84.0 Arsenal 23000000.0 140000.0 Right 3.0 2.0 2.0 CB 19.0 Jul 2, 2018 2021 6'1 187lbs 62+3 62+3 62+3 56+3 58+3 58+3 58+3 56+3 56+3 56+3 56+3 57+3 59+3 59+3 59+3 57+3 70+3 71+3 71+3 71+3 70+3 73+3 81+3 81+3 81+3 73+3 47.0 44.0 85.0 60.0 46.0 58.0 29.0 35.0 51.0 58.0 64.0 73.0 60.0 81.0 64.0 77.0 86.0 69.0 86.0 48.0 88.0 77.0 49.0 45.0 24.0 82.0 85.0 87.0 85.0 6.0 8.0 5.0 12.0 5.0 4.370000e+08
199 199 192883 H. Mkhitaryan 29 Armenia 83.0 83.0 Arsenal 255000000.0 145000.0 Right 3.0 5.0 4.0 MF 7.0 Jan 22, 2018 2021 5'10 165lbs 76+3 76+3 76+3 81+3 80+3 80+3 80+3 81+3 81+3 81+3 81+3 80+3 78+3 78+3 78+3 80+3 70+3 68+3 68+3 68+3 70+3 67+3 59+3 59+3 59+3 67+3 77.0 74.0 53.0 80.0 81.0 84.0 83.0 64.0 71.0 86.0 84.0 78.0 87.0 79.0 85.0 85.0 69.0 83.0 62.0 73.0 69.0 64.0 82.0 84.0 72.0 76.0 41.0 53.0 55.0 5.0 11.0 14.0 5.0 7.0 4.850000e+08
235 235 223959 L. Torreira 22 Uruguay 82.0 87.0 Arsenal 25000000.0 98000.0 Right 2.0 3.0 3.0 MF 14.0 Jul 12, 2018 2023 5'6 141lbs 68+2 68+2 68+2 74+2 73+2 73+2 73+2 74+2 75+2 75+2 75+2 75+2 78+2 78+2 78+2 75+2 80+2 81+2 81+2 81+2 80+2 79+2 77+2 77+2 77+2 79+2 74.0 50.0 55.0 83.0 65.0 78.0 66.0 76.0 77.0 81.0 81.0 74.0 83.0 79.0 93.0 78.0 52.0 85.0 66.0 74.0 85.0 87.0 65.0 74.0 48.0 78.0 84.0 84.0 75.0 5.0 5.0 13.0 7.0 9.0 4.940000e+08
281 281 192227 S. Mustafi 26 Germany 82.0 84.0 Arsenal 225000000.0 115000.0 Right 3.0 3.0 2.0 CB 20.0 Aug 30, 2016 2021 6'0 181lbs 64+3 64+3 64+3 61+3 62+3 62+3 62+3 61+3 63+3 63+3 63+3 62+3 67+3 67+3 67+3 62+3 72+3 76+3 76+3 76+3 72+3 74+3 80+3 80+3 80+3 74+3 53.0 58.0 81.0 77.0 51.0 53.0 55.0 47.0 69.0 70.0 58.0 65.0 63.0 82.0 67.0 66.0 83.0 68.0 78.0 52.0 82.0 82.0 50.0 55.0 54.0 73.0 80.0 82.0 82.0 11.0 9.0 15.0 10.0 6.0 4.440000e+08
289 289 186561 A. Ramsey 27 Wales 82.0 83.0 Arsenal 25000000.0 130000.0 Right 3.0 3.0 3.0 MF 10.0 Jun 13, 2008 2019 6'0 168lbs 75+3 75+3 75+3 77+3 78+3 78+3 78+3 77+3 79+3 79+3 79+3 78+3 80+3 80+3 80+3 78+3 75+3 75+3 75+3 75+3 75+3 72+3 69+3 69+3 69+3 72+3 75.0 74.0 58.0 84.0 79.0 81.0 70.0 66.0 80.0 82.0 64.0 69.0 73.0 81.0 74.0 81.0 66.0 89.0 67.0 74.0 73.0 69.0 84.0 79.0 75.0 81.0 66.0 70.0 67.0 6.0 11.0 5.0 10.0 8.0 4.940000e+08
311 311 165229 L. Koscielny 32 France 82.0 82.0 Arsenal 125000000.0 115000.0 Right 3.0 3.0 2.0 CB 6.0 Jul 7, 2010 2020 6'1 165lbs 57+3 57+3 57+3 58+3 58+3 58+3 58+3 58+3 60+3 60+3 60+3 60+3 65+3 65+3 65+3 60+3 71+3 75+3 75+3 75+3 71+3 74+3 80+3 80+3 80+3 74+3 52.0 32.0 80.0 76.0 35.0 62.0 22.0 49.0 69.0 66.0 64.0 66.0 65.0 76.0 61.0 54.0 78.0 62.0 75.0 47.0 86.0 84.0 41.0 56.0 51.0 75.0 80.0 84.0 83.0 13.0 11.0 9.0 11.0 7.0 2.380000e+08
464 464 203747 Héctor Bellerín 23 Spain 80.0 85.0 Arsenal 165000000.0 85000.0 Right 3.0 3.0 3.0 CB 2.0 Jul 1, 2014 2023 5'10 163lbs 65+3 65+3 65+3 73+3 70+3 70+3 70+3 73+3 71+3 71+3 71+3 75+3 71+3 71+3 71+3 75+3 79+3 74+3 74+3 74+3 79+3 79+3 73+3 73+3 73+3 79+3 76.0 52.0 60.0 76.0 41.0 75.0 63.0 50.0 59.0 78.0 94.0 91.0 82.0 78.0 77.0 52.0 63.0 84.0 58.0 43.0 72.0 77.0 67.0 66.0 62.0 70.0 75.0 78.0 80.0 8.0 14.0 14.0 13.0 12.0 3.260000e+08
5 5 183277 E. Hazard 27 Belgium 91.0 91.0 Chelsea 93000000.0 340000.0 Right 4.0 4.0 4.0 ST 10.0 Jul 1, 2012 2020 5'8 163lbs 83+3 83+3 83+3 89+3 88+3 88+3 88+3 89+3 89+3 89+3 89+3 89+3 82+3 82+3 82+3 89+3 66+3 63+3 63+3 63+3 66+3 60+3 49+3 49+3 49+3 60+3 81.0 84.0 61.0 89.0 80.0 95.0 83.0 79.0 83.0 94.0 94.0 88.0 95.0 90.0 94.0 82.0 56.0 83.0 66.0 80.0 54.0 41.0 87.0 89.0 86.0 91.0 34.0 27.0 22.0 11.0 12.0 6.0 8.0 8.0 1.721000e+09
14 14 215914 N. Kanté 27 France 89.0 90.0 Chelsea 63000000.0 225000.0 Right 3.0 3.0 2.0 MF 13.0 Jul 16, 2016 2023 5'6 159lbs 72+3 72+3 72+3 77+3 77+3 77+3 77+3 77+3 79+3 79+3 79+3 79+3 82+3 82+3 82+3 79+3 85+3 87+3 87+3 87+3 85+3 84+3 83+3 83+3 83+3 84+3 68.0 65.0 54.0 86.0 56.0 79.0 49.0 49.0 81.0 80.0 82.0 78.0 82.0 93.0 92.0 71.0 77.0 96.0 76.0 69.0 90.0 92.0 71.0 79.0 54.0 85.0 90.0 91.0 85.0 15.0 12.0 10.0 7.0 10.0 1.213000e+09
69 69 184432 Azpilicueta 28 Spain 86.0 86.0 Chelsea 35000000.0 175000.0 Right 3.0 3.0 2.0 CB 14.0 Aug 24, 2012 2022 5'10 168lbs 67+3 67+3 67+3 71+3 70+3 70+3 70+3 71+3 72+3 72+3 72+3 74+3 77+3 77+3 77+3 74+3 83+3 83+3 83+3 83+3 83+3 84+3 83+3 83+3 83+3 84+3 81.0 46.0 76.0 81.0 51.0 69.0 68.0 58.0 80.0 77.0 76.0 76.0 72.0 88.0 73.0 68.0 76.0 87.0 71.0 60.0 82.0 89.0 62.0 74.0 66.0 78.0 88.0 90.0 86.0 13.0 9.0 10.0 6.0 5.0 6.480000e+08
121 121 205498 Jorginho 26 Italy 84.0 87.0 Chelsea 38000000.0 155000.0 Right 2.0 3.0 3.0 MF 8.0 Jul 16, 2018 2023 5'11 148lbs 70+2 70+2 70+2 77+2 76+2 76+2 76+2 77+2 80+2 80+2 80+2 79+2 82+2 82+2 82+2 79+2 77+2 79+2 79+2 79+2 77+2 75+2 72+2 72+2 72+2 75+2 75.0 57.0 56.0 89.0 71.0 82.0 75.0 76.0 87.0 85.0 75.0 69.0 80.0 83.0 73.0 71.0 58.0 79.0 66.0 62.0 79.0 78.0 72.0 87.0 84.0 79.0 63.0 78.0 70.0 14.0 7.0 13.0 14.0 8.0 7.320000e+08
140 140 180403 Willian 29 Brazil 84.0 84.0 Chelsea 305000000.0 175000.0 Right 3.0 4.0 4.0 ST 22.0 Aug 28, 2013 2020 5'9 172lbs 74+3 74+3 74+3 82+3 81+3 81+3 81+3 82+3 82+3 82+3 82+3 82+3 79+3 79+3 79+3 82+3 71+3 67+3 67+3 67+3 71+3 66+3 54+3 54+3 54+3 66+3 76.0 77.0 31.0 80.0 59.0 88.0 82.0 83.0 77.0 84.0 91.0 86.0 89.0 82.0 79.0 79.0 41.0 85.0 60.0 81.0 44.0 55.0 80.0 83.0 70.0 79.0 43.0 57.0 51.0 14.0 12.0 13.0 7.0 13.0 5.640000e+08
176 176 207410 M. Kovačić 24 Croatia 83.0 89.0 Chelsea 35000000.0 165000.0 Right 3.0 4.0 4.0 MF 17.0 -1 Jun 30, 2019 5'9 172lbs 71+3 71+3 71+3 79+3 78+3 78+3 78+3 79+3 81+3 81+3 81+3 80+3 82+3 82+3 82+3 80+3 76+3 78+3 78+3 78+3 76+3 74+3 69+3 69+3 69+3 74+3 73.0 62.0 42.0 87.0 76.0 88.0 78.0 68.0 82.0 89.0 84.0 74.0 81.0 70.0 85.0 79.0 63.0 82.0 65.0 75.0 77.0 78.0 69.0 88.0 59.0 72.0 69.0 75.0 63.0 9.0 5.0 6.0 7.0 7.0 -1.000000e+00
177 177 206585 Kepa 23 Spain 83.0 91.0 Chelsea 285000000.0 84000.0 Right 1.0 4.0 1.0 GK 13.0 Aug 8, 2018 2025 6'1 187lbs -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 25.0 11.0 17.0 32.0 18.0 10.0 25.0 19.0 36.0 16.0 34.0 33.0 41.0 79.0 39.0 25.0 71.0 37.0 51.0 14.0 18.0 21.0 15.0 55.0 24.0 63.0 19.0 18.0 16.0 84.0 80.0 76.0 80.0 84.0 5.840000e+08
200 200 192638 Marcos Alonso 27 Spain 83.0 84.0 Chelsea 24000000.0 130000.0 Left 2.0 3.0 3.0 CB 3.0 Aug 31, 2016 2023 6'2 192lbs 78+2 78+2 78+2 77+2 78+2 78+2 78+2 77+2 77+2 77+2 77+2 79+2 79+2 79+2 79+2 79+2 81+2 80+2 80+2 80+2 81+2 81+2 79+2 79+2 79+2 81+2 85.0 68.0 83.0 84.0 70.0 79.0 85.0 86.0 73.0 83.0 69.0 76.0 64.0 84.0 58.0 84.0 71.0 90.0 76.0 77.0 75.0 79.0 79.0 69.0 64.0 76.0 79.0 82.0 79.0 9.0 7.0 13.0 9.0 12.0 4.620000e+08
209 209 179944 David Luiz 31 Brazil 83.0 83.0 Chelsea 175000000.0 130000.0 Right 4.0 3.0 3.0 CB 30.0 Aug 31, 2016 2019 6'2 190lbs 68+3 68+3 68+3 67+3 67+3 67+3 67+3 67+3 69+3 69+3 69+3 68+3 73+3 73+3 73+3 68+3 74+3 78+3 78+3 78+3 74+3 75+3 80+3 80+3 80+3 75+3 68.0 55.0 81.0 77.0 63.0 66.0 70.0 76.0 77.0 79.0 59.0 62.0 69.0 75.0 56.0 79.0 78.0 66.0 79.0 73.0 85.0 85.0 54.0 69.0 75.0 77.0 79.0 83.0 82.0 11.0 12.0 10.0 7.0 14.0 3.240000e+08
219 219 162895 Cesc Fàbregas 31 Spain 83.0 83.0 Chelsea 22000000.0 140000.0 Right 4.0 3.0 3.0 MF 4.0 Jul 1, 2014 2019 5'10 170lbs 74+3 74+3 74+3 76+3 77+3 77+3 77+3 76+3 80+3 80+3 80+3 77+3 79+3 79+3 79+3 77+3 68+3 70+3 70+3 70+3 68+3 66+3 62+3 62+3 62+3 66+3 85.0 72.0 74.0 90.0 81.0 79.0 78.0 81.0 89.0 85.0 56.0 54.0 62.0 81.0 75.0 77.0 63.0 56.0 61.0 70.0 45.0 50.0 72.0 91.0 78.0 92.0 64.0 59.0 58.0 6.0 10.0 8.0 15.0 15.0 4.070000e+08
257 257 205452 A. Rüdiger 25 Germany 82.0 84.0 Chelsea 225000000.0 110000.0 Right 2.0 3.0 2.0 CB 16.0 Jul 9, 2017 2022 6'3 187lbs 58+2 58+2 58+2 59+2 58+2 58+2 58+2 59+2 60+2 60+2 60+2 62+2 65+2 65+2 65+2 62+2 74+2 76+2 76+2 76+2 74+2 76+2 81+2 81+2 81+2 76+2 68.0 27.0 80.0 75.0 34.0 59.0 35.0 56.0 72.0 69.0 67.0 78.0 59.0 82.0 50.0 70.0 73.0 60.0 82.0 41.0 88.0 80.0 33.0 57.0 40.0 72.0 82.0 85.0 82.0 14.0 15.0 10.0 12.0 8.0 4.330000e+08

b. EDA

  • 주전선수 비교

포지션별 선수 능력치 비교

1
2
3

# 박스클폿으로 이상치 확인 및 두 팀의 선수 데이터 비교

sns.boxplot(data=london, x='Position', y='Overall', hue= 'Club')

<AxesSubplot:xlabel='Position', ylabel='Overall'>


1
2
3
4
5

# MF 부분 이상치 확인
london[london['Position'] == 'MF'][['Name','Age','Value','Overall','Position','Club']]

# 전체적인 데이터가 80대에 분포해있고 N. Kanté 선수가 다른 선수들에 비해 능력치가 높아서 인것으로 확인
# 특이사항은 아니므로 그대로 분석 진행
Name Age Value Overall Position Club
74 M. Özil 29 435000000.0 86.0 MF Arsenal
199 H. Mkhitaryan 29 255000000.0 83.0 MF Arsenal
235 L. Torreira 22 25000000.0 82.0 MF Arsenal
289 A. Ramsey 27 25000000.0 82.0 MF Arsenal
14 N. Kanté 27 63000000.0 89.0 MF Chelsea
121 Jorginho 26 38000000.0 84.0 MF Chelsea
176 M. Kovačić 24 35000000.0 83.0 MF Chelsea
219 Cesc Fàbregas 31 22000000.0 83.0 MF Chelsea 
1
2
3
4

london[london['Position'] == 'CB'][['Name','Age','Value','Overall','Position','Club']]

# 전체적인 데이터가 80대에 분포해있고 Azpilicueta 선수가 다른 선수들에 비해 능력치가 높아서 인것으로 확인
# 특이사항은 아니므로 그대로 분석 진행
Name Age Value Overall Position Club
146 Sokratis 30 23000000.0 84.0 CB Arsenal
281 S. Mustafi 26 225000000.0 82.0 CB Arsenal
311 L. Koscielny 32 125000000.0 82.0 CB Arsenal
464 Héctor Bellerín 23 165000000.0 80.0 CB Arsenal
69 Azpilicueta 28 35000000.0 86.0 CB Chelsea
200 Marcos Alonso 27 24000000.0 83.0 CB Chelsea
209 David Luiz 31 175000000.0 83.0 CB Chelsea
257 A. Rüdiger 25 225000000.0 82.0 CB Chelsea

두 팀을 비교해봤을때 아스널이 첼시에 비해 중원(미드필드)라인과 수비라인에서 다소 부족한 상태임을 알 수 있음

선수의 몸값 대비 능력치로 비교

1

sns.boxplot(data=london, x='Position', y='Value', hue= 'Club')

<AxesSubplot:xlabel='Position', ylabel='Value'>


1

london[london['Position'] == 'ST'][['Name','Age','Value','Overall','Position','Club']]
Name Age Value Overall Position Club
33 P. Aubameyang 29 59000000.0 88.0 ST Arsenal
87 A. Lacazette 27 45000000.0 85.0 ST Arsenal
5 E. Hazard 27 93000000.0 91.0 ST Chelsea
140 Willian 29 305000000.0 84.0 ST Chelsea 
1

london[london['Position'] == 'CB'][['Name','Age','Value','Overall','Position','Club']]
Name Age Value Overall Position Club
146 Sokratis 30 23000000.0 84.0 CB Arsenal
281 S. Mustafi 26 225000000.0 82.0 CB Arsenal
311 L. Koscielny 32 125000000.0 82.0 CB Arsenal
464 Héctor Bellerín 23 165000000.0 80.0 CB Arsenal
69 Azpilicueta 28 35000000.0 86.0 CB Chelsea
200 Marcos Alonso 27 24000000.0 83.0 CB Chelsea
209 David Luiz 31 175000000.0 83.0 CB Chelsea
257 A. Rüdiger 25 225000000.0 82.0 CB Chelsea

아스날선수와 첼시선수의 선발 선수명단을 비교해보면

  • 포지션 대비 능력로 보았을때

아스널의 선수들이 첼시 선수들에 비해 미드필더 부분과 수비부분에서 능력치가 열세임이 확인된다.

  • 선수가치 대비 능력치를 보았을때

아스널의 선수들이 첼시 선수들에 비해 공격수 부분은 낮은 몸값대비 비슷한 능력치를 보이고 있는 반면

미드 필더 부분은 아스널 선수들이 더 높은 몸값을 가진 것에 비해 첼시 선수들보다 더 낮은 능력치를 보이고 있다.

즉 포지션별로 선수들의 몸값과 역략을 고려하였을때 아스날 선수단이 첼시 선수단에 비해서 미드필더 부분과 수비부분에서 부족함을 보이고 있음을 알 수 있다.






4. Arsenal F.C는 어떤 선수를 영입해야 할까?

a. EDA

  • 필요 포지션의 어떤 선수를 대체해야할지 확인

  • 기준은 영입일, 능력치, 잠재력, 나이

방출 기준을 위한 나만의 공식 만들기

  • 선수의 미래가치 = (Overall + Potential*2) / Age

(가중치 2, 나이가 낮을 수록 가치가 높아지도록)

  • 현재 기량도 고려해야 하지만 추후에도 성장하며 지속적으로 활약해줘야 하기 때문에 Potential을 고려함
1

gunners_lineup['Point'] = (gunners_lineup['Overall'] + gunners_lineup['Potential']*2 / gunners_lineup['Age'])

문제가 되는 미드필더 & 수비 포지션의 선수들을 대상으로 계산한 미래가치를 확인

1
2
3

release_list_mf = gunners_lineup[gunners_lineup['Position']=='MF'][ ['Name','Overall','Potential','Age','Joined', 'Point']]
release_list_mf = release_list_mf.sort_values(by=["Point","Joined","Age"], ascending=[True, False, False])
release_list_mf
Name Overall Potential Age Joined Point
289 A. Ramsey 82.0 83.0 27 Jun 13, 2008 88.148148
199 H. Mkhitaryan 83.0 83.0 29 Jan 22, 2018 88.724138
235 L. Torreira 82.0 87.0 22 Jul 12, 2018 89.909091
74 M. Özil 86.0 86.0 29 Sep 2, 2013 91.931034 
1
2
3

release_list_cb = gunners_lineup[gunners_lineup['Position']=='CB'][ ['Name','Overall','Potential','Age','Joined', 'Point']]
release_list_cb = release_list_cb.sort_values(by=["Point","Joined","Age"], ascending=[True, False, False])
release_list_cb
Name Overall Potential Age Joined Point
311 L. Koscielny 82.0 82.0 32 Jul 7, 2010 87.125000
464 Héctor Bellerín 80.0 85.0 23 Jul 1, 2014 87.391304
281 S. Mustafi 82.0 84.0 26 Aug 30, 2016 88.461538
146 Sokratis 84.0 84.0 30 Jul 2, 2018 89.600000

잔류포인트, 합류일자, 나이로 보았을때 가장 위에 위치한 A. Ramsey, L. Koscielny 방출 후 MF, CB한명씩 영입해야 한다.

  • Arsenal F.C의 영입방칙은 최근 새로 부임한 아르테타 감독의 영입방침을 따른다.

[영입 방침]

  • 선수의 나이는 어릴수록 좋음

  • 잠재력 보다 현재 바로 주전으로 사용할 수 있는 선수

  • 포지션은 A. Ramsey와 Héctor Bellerín의 상세포지션인 CAM, CB를 따른다.

  • 라이벌팀이거나 빅클럽(리그 상위권에 속한 팀)의 선수는 영입하지 않는다.

1
2
3

# 위에서 사용한 Point 데이터를 market에서도 사용하기 위해 전체 선수데이터에도 동일 기준 적용

data['Point'] = (data['Overall'] + data['Potential']*2 / data['Age'])

1
2
3
4

# 전체 데이터에서 원하는 포지션이 있는 데이터만 추출하여 이적시장 데이터 형성 CAM, CB 포지션을 제외한 선수들을 제거
market = data[ (data['Position']=='CAM') | (data['Position']=='CB')]
market.drop(['Unnamed: 0'], axis = 1, inplace = True)
market.head()
ID Name Age Nationality Overall Potential Club Value Wage Preferred Foot International Reputation Weak Foot Skill Moves Position Jersey Number Joined Contract Valid Until Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression Interceptions Positioning Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause Point
12 182493 D. Godín 32 Uruguay 90.0 90.0 Atlético Madrid 44000000.0 125000.0 Right 3.0 3.0 2.0 CB 10.0 Aug 4, 2010 2019 6'2 172lbs 64+3 64+3 64+3 61+3 62+3 62+3 62+3 61+3 62+3 62+3 62+3 63+3 68+3 68+3 68+3 63+3 76+3 81+3 81+3 81+3 76+3 79+3 87+3 87+3 87+3 79+3 55.0 42.0 92.0 79.0 47.0 53.0 49.0 51.0 70.0 76.0 68.0 68.0 58.0 85.0 54.0 67.0 91.0 66.0 88.0 43.0 89.0 88.0 48.0 52.0 50.0 82.0 90.0 89.0 89.0 6.0 8.0 15.0 5.0 15.0 9.020000e+08 95.625000
17 194765 A. Griezmann 27 France 89.0 90.0 Atlético Madrid 78000000.0 145000.0 Left 4.0 3.0 4.0 CAM 7.0 Jul 28, 2014 2023 5'9 161lbs 86+3 86+3 86+3 87+3 87+3 87+3 87+3 87+3 86+3 86+3 86+3 86+3 80+3 80+3 80+3 86+3 70+3 67+3 67+3 67+3 70+3 67+3 61+3 61+3 61+3 67+3 82.0 90.0 84.0 83.0 87.0 88.0 84.0 78.0 76.0 90.0 88.0 85.0 90.0 90.0 80.0 80.0 90.0 83.0 62.0 82.0 69.0 35.0 91.0 83.0 79.0 87.0 59.0 47.0 48.0 14.0 8.0 14.0 13.0 14.0 1.658000e+09 95.666667
31 190460 C. Eriksen 26 Denmark 88.0 91.0 Tottenham Hotspur 735000000.0 205000.0 Right 3.0 5.0 4.0 CAM 10.0 Aug 30, 2013 2020 5'11 168lbs 79+3 79+3 79+3 84+3 84+3 84+3 84+3 84+3 86+3 86+3 86+3 85+3 85+3 85+3 85+3 85+3 71+3 71+3 71+3 71+3 71+3 66+3 57+3 57+3 57+3 66+3 88.0 80.0 52.0 91.0 77.0 84.0 86.0 87.0 88.0 91.0 75.0 73.0 79.0 88.0 81.0 84.0 50.0 92.0 58.0 89.0 46.0 56.0 83.0 91.0 67.0 88.0 59.0 57.0 22.0 9.0 14.0 7.0 7.0 6.0 1.415000e+09 95.000000
42 205600 S. Umtiti 24 France 87.0 92.0 FC Barcelona 57000000.0 205000.0 Left 3.0 3.0 2.0 CB 23.0 Jul 12, 2016 2023 6'0 165lbs 70+3 70+3 70+3 69+3 69+3 69+3 69+3 69+3 69+3 69+3 69+3 70+3 73+3 73+3 73+3 70+3 80+3 81+3 81+3 81+3 80+3 81+3 84+3 84+3 84+3 81+3 69.0 51.0 79.0 81.0 70.0 69.0 72.0 62.0 73.0 77.0 71.0 73.0 68.0 82.0 66.0 83.0 89.0 78.0 84.0 74.0 81.0 87.0 58.0 58.0 61.0 82.0 90.0 89.0 86.0 15.0 10.0 14.0 12.0 15.0 1.211000e+09 94.666667
61 201942 Roberto Firmino 26 Brazil 86.0 87.0 Liverpool 53000000.0 195000.0 Right 3.0 4.0 4.0 CAM 9.0 Jul 6, 2015 2023 5'11 168lbs 83+3 83+3 83+3 83+3 84+3 84+3 84+3 83+3 84+3 84+3 84+3 83+3 82+3 82+3 82+3 83+3 72+3 73+3 73+3 73+3 72+3 69+3 67+3 67+3 67+3 69+3 72.0 87.0 77.0 86.0 81.0 87.0 80.0 65.0 75.0 88.0 78.0 77.0 80.0 86.0 81.0 81.0 81.0 90.0 74.0 76.0 73.0 58.0 87.0 85.0 72.0 87.0 60.0 64.0 42.0 8.0 11.0 9.0 6.0 10.0 1.047000e+09 92.692308

시각화 하여 비교 후 영입 명단 작성

1

import matplotlib.pyplot as plt

plt.subplots(행, 열, figsize=(가로, 세로)) 심화

여러개의 그래프를 한번에 그리기 위하여 설정해 줍니다.

이것을 변수 ax에 담는다면 ax[1, 1]은 1열 1행에 그래프가 그려집니다.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

f, ax = plt.subplots(2,4, figsize = (35,10))

compare_list_mf = ['Age', 'Overall', 'Point', 'BallControl']
compare_list_cb = ['Age', 'Overall', 'Point', 'Interceptions']

for i in range(8):
    # CAM 선수데이터를 비교할 그래프 
    if i < 4 : # Age ~ BallControl 각각 1개씩 그래프에 들어가도록 하기 위함
        sns.barplot(x = compare_list_mf[i], y='Name', 
                    data= market[market['Position']=='CAM'][:15], ax=ax[i//4, i%4] )
        # [:15] 상위 15명 데이터만 추출
        # ax=ax[i//4, i%4] 4개의 그래프만 출력하기 위함
        
    
    # 위에서 지정한 4개의 컬럼까지 출력하면 아래 subplot으로 이동해서 그리게 하기 위함
    else :
        sns.barplot(x = compare_list_cb[i%4], y='Name', data= market[market['Position']=='CB'][:15], ax=ax[i//4, i%4])

plt.barplot(x, y, data, palette, ax) 심화

평균치 이상이면 음영에 차이를 주기 & 평균선 그어주기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

f, ax = plt.subplots(2, 4, figsize=(35, 10))


compare_list_mf = ['Age', 'Overall', 'Point', 'BallControl']
compare_list_cb = ['Age', 'Overall', 'Point', 'Interceptions']


for i in range(8):
    if i < 4:
        colors = ['firebrick' if x > market[market['Position'] == 'CAM'][:15][compare_list_mf[i]].mean()
                  else 'gray' for x in market[market['Position'] == 'CAM'][:15][compare_list_mf[i]]]

        sns.barplot(x=compare_list_mf[i], y='Name', data=market[market['Position']
                                                             == 'CAM'][:15], palette=colors, ax=ax[i//4, i % 4])

        ax[i//4, i % 4].axvline(market[market['Position'] == 'CAM']
                                [:15][compare_list_mf[i]].mean(), ls='--')

    else:
        sns.barplot(x=compare_list_cb[i % 4], y='Name',
                    data=market[market['Position'] == 'CB'][:15], ax=ax[i//4, i % 4])

        ax[i//4, i % 4].axvline(market[market['Position'] == 'CB']
                                [:15][compare_list_cb[i % 4]].mean(), ls='--')

결론

  • 포지션 별로 4개의 주요 지표를 비교해 보았을때 영입 list 는 다음과 같다.

[미드필더]

  • N.Fekir :Ramsey 선수보다 어리면서도, Overall이 높아 바로 기용이 가능하고 미래 가치인 Point도 평균치 이상으로 가장 적합하다.

[수비수]

  • S.de Vrij : L. Koscielny 선수보다 매우 어리고, Overall도 높아 바로 기용이 가능하고, 미래 가치인 Point도 평균치로 나이대비 준수한 편이며, 수비수의 주요 지표인 Interceptions 수치가 상위권으로 가장 적합하다.

태그:

카테고리:

업데이트:

댓글남기기

참고

“[Git] Github Action으로 내 README.md 자동화 하기”

9 분 소요

🚦 Summary 개인 포트폴리오로 하나의 레포지토리에 다양한 프로젝트를 관리하곤 합니다. 이때, README에 프로젝트를 간결하게 표로 정리하고 각 폴더의 링크를 제공하면 내 프로젝트들을 한눈에 보기 쉽게 제시할 수 있습니다. 하지만, 이 README의 표를 프로젝트...