FIFA World Cup 2018
In this project, I did an analysis and some visualizations on the FIFA18 dataset. The dataset is collected at this GitHub Repo. The goal is to predict the best possible international squad lineups for these 10 teams France, Germany, Spain, England, Brazil, Argentina, Belgium, Portugal, Uruguay, and Croatia at the 2018 World Cup this summer in Russia.
Published on April 23, 2018 by Udbhav Pangotra
pandas numpy plotly matplotlib
345 min READ
The FIFA World Cup, often simply called the World Cup, is an international association football competition contested by the senior men’s national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport’s global governing body. The championship has been awarded every four years since the inaugural tournament in 1930, except in 1942 and 1946 when it was not held because of the Second World War. The current champion is France, which won its second title at the 2018 tournament in Russia.
# Import libraries
# conda install plotly
import sys
!{sys.executable} -m pip install plotly==2.7.0
!{sys.executable} -m pip install geopy
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from plotly.offline import iplot, init_notebook_mode
from geopy.geocoders import Nominatim
import plotly.plotly as py
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1 - Data Preparation
1.1 Load Data
FIFA18 = pd.read_csv('CompleteDataset.csv', low_memory=False)
FIFA18.columns
Index(['Unnamed: 0', 'Name', 'Age', 'Photo', 'Nationality', 'Flag', 'Overall',
'Potential', 'Club', 'Club Logo', 'Value', 'Wage', 'Special',
'Acceleration', 'Aggression', 'Agility', 'Balance', 'Ball control',
'Composure', 'Crossing', 'Curve', 'Dribbling', 'Finishing',
'Free kick accuracy', 'GK diving', 'GK handling', 'GK kicking',
'GK positioning', 'GK reflexes', 'Heading accuracy', 'Interceptions',
'Jumping', 'Long passing', 'Long shots', 'Marking', 'Penalties',
'Positioning', 'Reactions', 'Short passing', 'Shot power',
'Sliding tackle', 'Sprint speed', 'Stamina', 'Standing tackle',
'Strength', 'Vision', 'Volleys', 'CAM', 'CB', 'CDM', 'CF', 'CM', 'ID',
'LAM', 'LB', 'LCB', 'LCM', 'LDM', 'LF', 'LM', 'LS', 'LW', 'LWB',
'Preferred Positions', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RF', 'RM',
'RS', 'RW', 'RWB', 'ST'],
dtype='object')
Let’s select the most interesting columns from loaded dataset:
interesting_columns = [
'Name',
'Age',
'Nationality',
'Overall',
'Potential',
'Club',
'Value',
'Wage',
'Preferred Positions'
]
FIFA18 = pd.DataFrame(FIFA18, columns=interesting_columns)
1.2 Summarize Data
FIFA18.head()
| Name | Age | Nationality | Overall | Potential | Club | Value | Wage | Preferred Positions | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Cristiano Ronaldo | 32 | Portugal | 94 | 94 | Real Madrid CF | €95.5M | €565K | ST LW |
| 1 | L. Messi | 30 | Argentina | 93 | 93 | FC Barcelona | €105M | €565K | RW |
| 2 | Neymar | 25 | Brazil | 92 | 94 | Paris Saint-Germain | €123M | €280K | LW |
| 3 | L. Suárez | 30 | Uruguay | 92 | 92 | FC Barcelona | €97M | €510K | ST |
| 4 | M. Neuer | 31 | Germany | 92 | 92 | FC Bayern Munich | €61M | €230K | GK |
FIFA18.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17981 entries, 0 to 17980
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Name 17981 non-null object
1 Age 17981 non-null int64
2 Nationality 17981 non-null object
3 Overall 17981 non-null int64
4 Potential 17981 non-null int64
5 Club 17733 non-null object
6 Value 17981 non-null object
7 Wage 17981 non-null object
8 Preferred Positions 17981 non-null object
dtypes: int64(3), object(6)
memory usage: 1.2+ MB
1.3 Preprocess Data
Right away I can see that values in columns: ‘Value’ and ‘Wage’ aren’t numeric but objects. Thus I’ll preprocess the data to make it usable. I will use short supporting function to convert values in those two columns into numbers. I will end up with two new columns ‘ValueNum’ and ‘WageNum’ that will contain numeric values.
# Supporting function for converting string values into numbers
def str2number(amount):
if amount[-1] == 'M':
return float(amount[1:-1])*1000000
elif amount[-1] == 'K':
return float(amount[1:-1])*1000
else:
return float(amount[1:])
FIFA18['ValueNum'] = FIFA18['Value'].apply(lambda x: str2number(x))
FIFA18['WageNum'] = FIFA18['Wage'].apply(lambda x: str2number(x))
To make things simpler, I select the first position from list as preferred and save it in ‘Position’ column.
FIFA18['Position'] = FIFA18['Preferred Positions'].str.split().str[0]
2 - Data Visualization
2.1 Age
plt.figure(figsize=(16,8))
sns.set_style("whitegrid")
plt.title('Grouping players by Age', fontsize=30, fontweight='bold', y=1.05,)
plt.xlabel('Number of players', fontsize=25)
plt.ylabel('Players Age', fontsize=25)
sns.countplot(x="Age", data=FIFA18, palette="hls");
plt.show()
2.2 Overall
plt.figure(figsize=(16,8))
sns.set_style("whitegrid")
plt.title('Grouping players by Overall', fontsize=30, fontweight='bold', y=1.05,)
plt.xlabel('Number of players', fontsize=25)
plt.ylabel('Players Age', fontsize=25)
sns.countplot(x="Overall", data=FIFA18, palette="hls");
plt.show()
2.3 Preferred Position
plt.figure(figsize=(16,8))
sns.set_style("whitegrid")
plt.title('Grouping players by Preferred Position', fontsize=30, fontweight='bold', y=1.05,)
plt.xlabel('Number of players', fontsize=25)
plt.ylabel('Players Age', fontsize=25)
sns.countplot(x="Position", data=FIFA18, palette="hls");
plt.show()
2.4 Nationality
In case the plot.ly image doesn’t show:
# Grouping the data by countries
valcon = FIFA18.groupby("Nationality").size().reset_index(name="Count")
# Plotting the choropleth map
init_notebook_mode()
plotmap = [ dict(
type = 'choropleth',
locations = valcon["Nationality"],
locationmode = 'country names',
z = valcon["Count"],
text = valcon["Nationality"],
autocolorscale = True,
reversescale = False,
marker = dict(
line = dict (
color = 'rgb(180,180,180)',
width = 0.5
) ),
colorbar = dict(
title = "Amount of Players"),
) ]
layout = dict(
# title = "Nationalities of FIFA 18 Players",
geo = dict(
# showframe = False,
# showcoastlines = False,
projection = dict(
type = 'natural earth'
)
)
)
fig = dict( data=plotmap, layout=layout )
iplot(fig)
FIFA18["Nationality"].value_counts().head(25)
England 1630
Germany 1140
Spain 1019
France 978
Argentina 965
Brazil 812
Italy 799
Colombia 592
Japan 469
Netherlands 429
Republic of Ireland 417
United States 381
Chile 375
Sweden 368
Portugal 367
Mexico 360
Denmark 346
Poland 337
Norway 333
Korea Republic 330
Saudi Arabia 329
Russia 306
Scotland 300
Turkey 291
Belgium 272
Name: Nationality, dtype: int64
I can see that the players are very centralized in Europe. To be precise, England, Germany, Spain, and France.
2.5 Value
Let’s see the 20 players with highest value:
sorted_players = FIFA18.sort_values(["ValueNum"], ascending=False).head(20)
players = sorted_players[["Name" ,"Age" ,"Nationality" ,"Club" ,"Position" ,"Value"]].values
from IPython.display import HTML, display
table_content = ''
for row in players:
HTML_row = '<tr>'
HTML_row += '<td>' + str(row[0]) + '</td>'
HTML_row += '<td>' + str(row[1]) + '</td>'
HTML_row += '<td>' + str(row[2]) + '</td>'
HTML_row += '<td>' + str(row[3]) + '</td>'
HTML_row += '<td>' + str(row[4]) + '</td>'
HTML_row += '<td>' + str(row[5]) + '</td>'
table_content += HTML_row + '</tr>'
display(HTML(
'<table><tr><th>Name</th><th>Age</th><th>Nationality</th><th>Club</th><th>Position</th><th>Value</th></tr>{}</table>'.format(table_content))
)
| Name | Age | Nationality | Club | Position | Value |
|---|---|---|---|---|---|
| Neymar | 25 | Brazil | Paris Saint-Germain | LW | €123M |
| L. Messi | 30 | Argentina | FC Barcelona | RW | €105M |
| L. Suárez | 30 | Uruguay | FC Barcelona | ST | €97M |
| Cristiano Ronaldo | 32 | Portugal | Real Madrid CF | ST | €95.5M |
| R. Lewandowski | 28 | Poland | FC Bayern Munich | ST | €92M |
| E. Hazard | 26 | Belgium | Chelsea | LW | €90.5M |
| K. De Bruyne | 26 | Belgium | Manchester City | RM | €83M |
| T. Kroos | 27 | Germany | Real Madrid CF | CDM | €79M |
| P. Dybala | 23 | Argentina | Juventus | ST | €79M |
| G. Higuaín | 29 | Argentina | Juventus | ST | €77M |
| A. Griezmann | 26 | France | Atlético Madrid | LW | €75M |
| Thiago | 26 | Spain | FC Bayern Munich | CDM | €70.5M |
| G. Bale | 27 | Wales | Real Madrid CF | RW | €69.5M |
| A. Sánchez | 28 | Chile | Arsenal | RM | €67.5M |
| S. Agüero | 29 | Argentina | Manchester City | ST | €66.5M |
| P. Pogba | 24 | France | Manchester United | CDM | €66.5M |
| C. Eriksen | 25 | Denmark | Tottenham Hotspur | LM | €65M |
| De Gea | 26 | Spain | Manchester United | GK | €64.5M |
| M. Verratti | 24 | Italy | Paris Saint-Germain | CDM | €64.5M |
| M. Neuer | 31 | Germany | FC Bayern Munich | GK | €61M |
Let’s make a scatter chart of the players’ Value with respect to their Age and Overall:
plt.figure(figsize=(16,8))
sns.set_style("whitegrid")
plt.title('Players Value according to their Age and Overall', fontsize=30, fontweight='bold', y=1.05,)
plt.xlabel('Age', fontsize=25)
plt.ylabel('Overall', fontsize=25)
age = FIFA18["Age"].values
overall = FIFA18["Overall"].values
value = FIFA18["ValueNum"].values
plt.scatter(age, overall, s = value/100000, edgecolors='black')
plt.show()
2.6 Wage
Let’s see the 20 players with highest wage:
sorted_players = FIFA18.sort_values(["WageNum"], ascending=False).head(20)
players = sorted_players[["Name" ,"Age" ,"Nationality" ,"Club" ,"Position" ,"Wage"]].values
from IPython.display import HTML, display
table_content = ''
for row in players:
HTML_row = '<tr>'
HTML_row += '<td>' + str(row[0]) + '</td>'
HTML_row += '<td>' + str(row[1]) + '</td>'
HTML_row += '<td>' + str(row[2]) + '</td>'
HTML_row += '<td>' + str(row[3]) + '</td>'
HTML_row += '<td>' + str(row[4]) + '</td>'
HTML_row += '<td>' + str(row[5]) + '</td>'
table_content += HTML_row + '</tr>'
display(HTML(
'<table><tr><th>Name</th><th>Age</th><th>Nationality</th><th>Club</th><th>Position</th><th>Wage</th></tr>{}</table>'.format(table_content))
)
| Name | Age | Nationality | Club | Position | Wage |
|---|---|---|---|---|---|
| Cristiano Ronaldo | 32 | Portugal | Real Madrid CF | ST | €565K |
| L. Messi | 30 | Argentina | FC Barcelona | RW | €565K |
| L. Suárez | 30 | Uruguay | FC Barcelona | ST | €510K |
| G. Bale | 27 | Wales | Real Madrid CF | RW | €370K |
| R. Lewandowski | 28 | Poland | FC Bayern Munich | ST | €355K |
| L. Modrić | 31 | Croatia | Real Madrid CF | CDM | €340K |
| T. Kroos | 27 | Germany | Real Madrid CF | CDM | €340K |
| S. Agüero | 29 | Argentina | Manchester City | ST | €325K |
| Sergio Ramos | 31 | Spain | Real Madrid CF | CB | €310K |
| E. Hazard | 26 | Belgium | Chelsea | LW | €295K |
| K. Benzema | 29 | France | Real Madrid CF | ST | €295K |
| K. De Bruyne | 26 | Belgium | Manchester City | RM | €285K |
| Neymar | 25 | Brazil | Paris Saint-Germain | LW | €280K |
| I. Rakitić | 29 | Croatia | FC Barcelona | CM | €275K |
| G. Higuaín | 29 | Argentina | Juventus | ST | €275K |
| A. Sánchez | 28 | Chile | Arsenal | RM | €265K |
| M. Özil | 28 | Germany | Arsenal | RW | €265K |
| Iniesta | 33 | Spain | FC Barcelona | LM | €260K |
| Marcelo | 29 | Brazil | Real Madrid CF | LB | €250K |
| J. Rodríguez | 25 | Colombia | FC Bayern Munich | RM | €250K |
Let’s make a scatter chart of the players’ Wage with respect to their Age and Overall:
plt.figure(figsize=(16,8))
sns.set_style("whitegrid")
plt.title('Players Wage according to their Age and Overall', fontsize=30, fontweight='bold', y=1.05,)
plt.xlabel('Age', fontsize=25)
plt.ylabel('Overall', fontsize=25)
age = FIFA18["Age"].values
overall = FIFA18["Overall"].values
value = FIFA18["WageNum"].values
plt.scatter(age, overall, s = value/500, edgecolors='black', color="red")
plt.show()
3 - Best Squad Analysis
For simplicity of this analysis, I only pull in data I am interested in:
FIFA18 = FIFA18[['Name', 'Age', 'Nationality', 'Overall', 'Potential', 'Club', 'Position', 'Value', 'Wage']]
FIFA18.head(10)
| Name | Age | Nationality | Overall | Potential | Club | Position | Value | Wage | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Cristiano Ronaldo | 32 | Portugal | 94 | 94 | Real Madrid CF | ST | €95.5M | €565K |
| 1 | L. Messi | 30 | Argentina | 93 | 93 | FC Barcelona | RW | €105M | €565K |
| 2 | Neymar | 25 | Brazil | 92 | 94 | Paris Saint-Germain | LW | €123M | €280K |
| 3 | L. Suárez | 30 | Uruguay | 92 | 92 | FC Barcelona | ST | €97M | €510K |
| 4 | M. Neuer | 31 | Germany | 92 | 92 | FC Bayern Munich | GK | €61M | €230K |
| 5 | R. Lewandowski | 28 | Poland | 91 | 91 | FC Bayern Munich | ST | €92M | €355K |
| 6 | De Gea | 26 | Spain | 90 | 92 | Manchester United | GK | €64.5M | €215K |
| 7 | E. Hazard | 26 | Belgium | 90 | 91 | Chelsea | LW | €90.5M | €295K |
| 8 | T. Kroos | 27 | Germany | 90 | 90 | Real Madrid CF | CDM | €79M | €340K |
| 9 | G. Higuaín | 29 | Argentina | 90 | 90 | Juventus | ST | €77M | €275K |
3.1 Squad of Highest Overall Players
What’s the best squad according to FIFA 18 purely based on overall rating?
def get_best_squad(formation):
FIFA18_copy = FIFA18.copy()
store = []
# iterate through all positions in the input formation and get players with highest overall respective to the position
for i in formation:
store.append([
i,
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Name'].to_string(index = False),
FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].max(),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Age'].to_string(index = False),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Club'].to_string(index = False),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Value'].to_string(index = False),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Wage'].to_string(index = False)
])
FIFA18_copy.drop(FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax(),
inplace = True)
# return store with only necessary columns
return pd.DataFrame(np.array(store).reshape(11,7),
columns = ['Position', 'Player', 'Overall', 'Age', 'Club', 'Value', 'Wage']).to_string(index = False)
# 4-3-3
squad_433 = ['GK', 'RB', 'CB', 'CB', 'LB', 'CDM', 'CM', 'CAM', 'RW', 'ST', 'LW']
print ('4-3-3')
print (get_best_squad(squad_433))
4-3-3
Position Player Overall Age Club Value Wage
GK M. Neuer 92 31 FC Bayern Munich €61M €230K
RB Carvajal 84 25 Real Madrid CF €32M €195K
CB Sergio Ramos 90 31 Real Madrid CF €52M €310K
CB G. Chiellini 89 32 Juventus €38M €225K
LB Marcelo 87 29 Real Madrid CF €38M €250K
CDM T. Kroos 90 27 Real Madrid CF €79M €340K
CM N. Kanté 87 26 Chelsea €52.5M €190K
CAM Coutinho 86 25 Liverpool €56M €205K
RW L. Messi 93 30 FC Barcelona €105M €565K
ST Cristiano Ronaldo 94 32 Real Madrid CF €95.5M €565K
LW Neymar 92 25 Paris Saint-Germain €123M €280K
# 4-4-2
squad_442 = ['GK', 'RB', 'CB', 'CB', 'LB', 'RM', 'CM', 'CM', 'LM', 'ST', 'ST']
print ('4-4-2')
print (get_best_squad(squad_442))
4-4-2
Position Player Overall Age Club Value Wage
GK M. Neuer 92 31 FC Bayern Munich €61M €230K
RB Carvajal 84 25 Real Madrid CF €32M €195K
CB Sergio Ramos 90 31 Real Madrid CF €52M €310K
CB G. Chiellini 89 32 Juventus €38M €225K
LB Marcelo 87 29 Real Madrid CF €38M €250K
RM K. De Bruyne 89 26 Manchester City €83M €285K
CM N. Kanté 87 26 Chelsea €52.5M €190K
CM A. Vidal 87 30 FC Bayern Munich €37.5M €160K
LM C. Eriksen 87 25 Tottenham Hotspur €65M €165K
ST Cristiano Ronaldo 94 32 Real Madrid CF €95.5M €565K
ST L. Suárez 92 30 FC Barcelona €97M €510K
# 4-2-3-1
squad_4231 = ['GK', 'RB', 'CB', 'CB', 'LB', 'CDM', 'CDM', 'CAM', 'CAM', 'CAM', 'ST']
print ('4-2-3-1')
print (get_best_squad(squad_4231))
4-2-3-1
Position Player Overall Age Club Value Wage
GK M. Neuer 92 31 FC Bayern Munich €61M €230K
RB Carvajal 84 25 Real Madrid CF €32M €195K
CB Sergio Ramos 90 31 Real Madrid CF €52M €310K
CB G. Chiellini 89 32 Juventus €38M €225K
LB Marcelo 87 29 Real Madrid CF €38M €250K
CDM T. Kroos 90 27 Real Madrid CF €79M €340K
CDM L. Modrić 89 31 Real Madrid CF €57M €340K
CAM Coutinho 86 25 Liverpool €56M €205K
CAM R. Nainggolan 86 29 Roma €42.5M €130K
CAM Cesc Fàbregas 86 30 Chelsea €41M €210K
ST Cristiano Ronaldo 94 32 Real Madrid CF €95.5M €565K
Alright, now let’s move onto studying different squad’s impact on Nationality teams. First let’s modifiy above get_summary and get_best_squad functions for Nationality:
def get_best_squad_n(formation, nationality, measurement = 'Overall'):
FIFA18_copy = FIFA18.copy()
FIFA18_copy = FIFA18_copy[FIFA18_copy['Nationality'] == nationality]
store = []
for i in formation:
store.append([
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'].str.contains(i)][measurement].idxmax()]]['Position'].to_string(index = False),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'].str.contains(i)][measurement].idxmax()]]['Name'].to_string(index = False),
FIFA18_copy[FIFA18_copy['Position'].str.contains(i)][measurement].max(),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'].str.contains(i)][measurement].idxmax()]]['Age'].to_string(index = False),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'].str.contains(i)][measurement].idxmax()]]['Club'].to_string(index = False),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'].str.contains(i)][measurement].idxmax()]]['Value'].to_string(index = False),
FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'].str.contains(i)][measurement].idxmax()]]['Wage'].to_string(index = False)
])
FIFA18_copy.drop(FIFA18_copy[FIFA18_copy['Position'].str.contains(i)][measurement].idxmax(),
inplace = True)
return np.mean([x[2] for x in store]).round(2), pd.DataFrame(np.array(store).reshape(11,7),
columns = ['Position', 'Player', measurement, 'Age', 'Club', 'Value', 'Wage']).to_string(index = False)
def get_summary_n(squad_list, squad_name, nationality_list):
summary = []
for i in nationality_list:
count = 0
for j in squad_list:
# for overall rating
O_temp_rating, _ = get_best_squad_n(formation = j, nationality = i, measurement = 'Overall')
# for potential rating & corresponding value
P_temp_rating, _ = get_best_squad_n(formation = j, nationality = i, measurement = 'Potential')
summary.append([i, squad_name[count], O_temp_rating.round(2), P_temp_rating.round(2)])
count += 1
return summary
Also let’s make our squad choices more strict:
squad_343_strict = ['GK', 'CB', 'CB', 'CB', 'RB|RWB', 'CM|CDM', 'CM|CDM', 'LB|LWB', 'RM|RW', 'ST|CF', 'LM|LW']
squad_442_strict = ['GK', 'RB|RWB', 'CB', 'CB', 'LB|LWB', 'RM', 'CM|CDM', 'CM|CAM', 'LM', 'ST|CF', 'ST|CF']
squad_4312_strict = ['GK', 'RB|RWB', 'CB', 'CB', 'LB|LWB', 'CM|CDM', 'CM|CAM|CDM', 'CM|CAM|CDM', 'CAM|CF', 'ST|CF', 'ST|CF']
squad_433_strict = ['GK', 'RB|RWB', 'CB', 'CB', 'LB|LWB', 'CM|CDM', 'CM|CAM|CDM', 'CM|CAM|CDM', 'RM|RW', 'ST|CF', 'LM|LW']
squad_4231_strict = ['GK', 'RB|RWB', 'CB', 'CB', 'LB|LWB', 'CM|CDM', 'CM|CDM', 'RM|RW', 'CAM', 'LM|LW', 'ST|CF']
squad_list = [squad_343_strict, squad_442_strict, squad_4312_strict, squad_433_strict, squad_4231_strict]
squad_name = ['3-4-3', '4-4-2', '4-3-1-2', '4-3-3', '4-2-3-1']
3.2 France
Let’s explore different squad possibility of France and how it affects the ratings:
France = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['France'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
France.set_index('Nationality', inplace = True)
France[['Overall', 'Potential']] = France[['Overall', 'Potential']].astype(float)
print (France)
Squad Overall Potential
Nationality
France 3-4-3 84.55 89.55
France 4-4-2 84.00 89.91
France 4-3-1-2 84.55 89.64
France 4-3-3 84.64 89.91
France 4-2-3-1 84.55 89.91
So we can say that France has the best squard as 4-3-3 for the current squad; and 4-4-2, 4-3-3, and 4-2-3-1 for the future squad based on team ratings. Let’s check out the best 11 squad line-up of France in 4-3-3 for current rating as well as 4-4-2 for potential rating:
rating_433_FR_Overall, best_list_433_FR_Overall = get_best_squad_n(squad_433_strict, 'France', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_433_FR_Overall))
print(best_list_433_FR_Overall)
-Overall-
Average rating: 84.6
Position Player Overall Age Club Value Wage
GK H. Lloris 88 30 Tottenham Hotspur €38M €165K
RB K. Zouma 79 22 Stoke City €15M €96K
CB R. Varane 85 24 Real Madrid CF €46.5M €175K
CB A. Laporte 84 23 Athletic Club de Bilbao €35.5M €36K
LB L. Kurzawa 80 24 Paris Saint-Germain €16.5M €69K
CM N. Kanté 87 26 Chelsea €52.5M €190K
CDM P. Pogba 87 24 Manchester United €66.5M €195K
CM B. Matuidi 85 30 Juventus €28.5M €145K
RM F. Thauvin 82 24 Olympique de Marseille €28M €40K
ST K. Benzema 86 29 Real Madrid CF €44.5M €295K
LW A. Griezmann 88 26 Atlético Madrid €75M €150K
rating_442_FR_Potential, best_list_442_FR_Potential = get_best_squad_n(squad_442_strict, 'France', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_442_FR_Potential))
print(best_list_442_FR_Potential)
-Potential-
Average rating: 89.9
Position Player Potential Age Club Value Wage
GK A. Lafont 89 18 Toulouse FC €11.5M €10K
RB K. Zouma 86 22 Stoke City €15M €96K
CB R. Varane 92 24 Real Madrid CF €46.5M €175K
CB A. Laporte 89 23 Athletic Club de Bilbao €35.5M €36K
LB L. Hernández 88 21 Atlético Madrid €13.5M €36K
RM A. Pléa 86 24 OGC Nice €20.5M €41K
CDM P. Pogba 92 24 Manchester United €66.5M €195K
CAM O. Dembélé 92 20 FC Barcelona €40M €150K
LM T. Lemar 91 21 AS Monaco €38.5M €37K
ST K. Mbappé 94 18 Paris Saint-Germain €41.5M €31K
ST A. Martial 90 21 Manchester United €33M €115K
3.3 Germany
The holding champion is certainly a heavy candidate for this year’s 1st place:
Germany = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Germany'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
Germany.set_index('Nationality', inplace = True)
Germany[['Overall', 'Potential']] = Germany[['Overall', 'Potential']].astype(float)
print (Germany)
Squad Overall Potential
Nationality
Germany 3-4-3 86.27 88.55
Germany 4-4-2 85.09 88.36
Germany 4-3-1-2 85.36 88.00
Germany 4-3-3 86.27 88.55
Germany 4-2-3-1 86.09 88.55
As you can see, Germany’s current ratings peak with either 3-4-3 or 4-3-3 formation; while those 2 plus 4-2-3-1 are their best options for the future. With that, I’ll show Germany’s best 11 squad with 4-3-3 for current ratings and 4-2-3-1 for potential ratings.
rating_433_GER_Overall, best_list_433_GER_Overall = get_best_squad_n(squad_433_strict, 'Germany', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_433_GER_Overall))
print(best_list_433_GER_Overall)
-Overall-
Average rating: 86.3
Position Player Overall Age Club Value Wage
GK M. Neuer 92 31 FC Bayern Munich €61M €230K
RB A. Rüdiger 82 24 Chelsea €24.5M €105K
CB J. Boateng 88 28 FC Bayern Munich €48M €215K
CB M. Hummels 88 28 FC Bayern Munich €48M €215K
LB J. Hector 80 27 1. FC Köln €14M €42K
CDM T. Kroos 90 27 Real Madrid CF €79M €340K
CDM I. Gündoğan 85 26 Manchester City €46M €190K
CDM S. Khedira 84 30 Juventus €29M €160K
RW M. Özil 88 28 Arsenal €60M €265K
ST T. Müller 86 27 FC Bayern Munich €47.5M €190K
LW M. Reus 86 28 Borussia Dortmund €45M €120K
rating_4231_GER_Potential, best_list_4231_GER_Potential = get_best_squad_n(squad_4231_strict, 'Germany', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_4231_GER_Potential))
print(best_list_4231_GER_Potential)
-Potential-
Average rating: 88.5
Position Player Potential Age Club Value Wage
GK M. Neuer 92 31 FC Bayern Munich €61M €230K
RB A. Rüdiger 86 24 Chelsea €24.5M €105K
CB N. Süle 89 21 FC Bayern Munich €30.5M €78K
CB J. Boateng 88 28 FC Bayern Munich €48M €215K
LB B. Henrichs 86 20 Bayer 04 Leverkusen €11M €36K
CDM T. Kroos 90 27 Real Madrid CF €79M €340K
CDM L. Goretzka 88 22 FC Schalke 04 €30M €46K
RW M. Özil 88 28 Arsenal €60M €265K
CAM J. Brandt 88 21 Bayer 04 Leverkusen €22M €49K
LM L. Sané 91 21 Manchester City €34.5M €125K
CF K. Havertz 88 18 Bayer 04 Leverkusen €8M €25K
3.4 Spain
How about our 2010’s winner?
Spain = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Spain'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
Spain.set_index('Nationality', inplace = True)
Spain[['Overall', 'Potential']] = Spain[['Overall', 'Potential']].astype(float)
print (Spain)
Squad Overall Potential
Nationality
Spain 3-4-3 86.27 88.82
Spain 4-4-2 86.45 88.73
Spain 4-3-1-2 86.55 88.09
Spain 4-3-3 86.64 89.00
Spain 4-2-3-1 86.64 89.00
Well, Spain does best with either 4-3-3 or 4-2-3-1 for both current and potential rating. I’ll choose 4-2-3-1 for the current squad and 4-3-3 for the potential squad.
rating_4231_ESP_Overall, best_list_4231_ESP_Overall = get_best_squad_n(squad_4231_strict, 'Spain', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_4231_ESP_Overall))
print(best_list_4231_ESP_Overall)
-Overall-
Average rating: 86.6
Position Player Overall Age Club Value Wage
GK De Gea 90 26 Manchester United €64.5M €215K
RB Carvajal 84 25 Real Madrid CF €32M €195K
CB Sergio Ramos 90 31 Real Madrid CF €52M €310K
CB Piqué 87 30 FC Barcelona €37.5M €240K
LB Jordi Alba 85 28 FC Barcelona €30.5M €215K
CDM Thiago 88 26 FC Bayern Munich €70.5M €225K
CM Sergio Busquets 86 28 FC Barcelona €36M €250K
RM Juan Mata 84 29 Manchester United €30.5M €195K
CAM Cesc Fàbregas 86 30 Chelsea €41M €210K
LM David Silva 87 31 Manchester City €44M €220K
ST Diego Costa 86 28 Chelsea €46M €235K
rating_433_ESP_Potential, best_list_433_ESP_Potential = get_best_squad_n(squad_433_strict, 'Spain', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_433_ESP_Potential))
print(best_list_433_ESP_Potential)
-Potential-
Average rating: 89.0
Position Player Potential Age Club Value Wage
GK De Gea 92 26 Manchester United €64.5M €215K
RB Héctor Bellerín 88 22 Arsenal €21M €91K
CB Sergio Ramos 90 31 Real Madrid CF €52M €310K
CB Piqué 87 30 FC Barcelona €37.5M €240K
LB Azpilicueta 87 27 Chelsea €37.5M €160K
CDM Thiago 90 26 FC Bayern Munich €70.5M €225K
CAM Dani Ceballos 88 20 Real Madrid CF €16.5M €105K
CDM Diego Llorente 87 23 Real Sociedad €16M €29K
RM Saúl 90 22 Atlético Madrid €32M €59K
ST Morata 88 24 Chelsea €41M €170K
LW Marco Asensio 92 21 Real Madrid CF €46M €175K
3.5 England
Although having the best soccer league in Europe, England did not seem to do that well at the national level. Let’s figure out their options for the upcoming World Cup:
England = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['England'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
England.set_index('Nationality', inplace = True)
England[['Overall', 'Potential']] = England[['Overall', 'Potential']].astype(float)
print (England)
Squad Overall Potential
Nationality
England 3-4-3 82.64 87.18
England 4-4-2 82.64 87.27
England 4-3-1-2 82.27 86.73
England 4-3-3 82.73 87.36
England 4-2-3-1 82.36 87.36
England should stick to 4-3-3 with their current squad and either 4-3-3 or 4-2-3-1 with their potential squad. Thus, I’ll choose 4-3-3 and 4-2-3-1 respectively.
rating_433_ENG_Overall, best_list_433_ENG_Overall = get_best_squad_n(squad_433_strict, 'England', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_433_ENG_Overall))
print(best_list_433_ENG_Overall)
-Overall-
Average rating: 82.7
Position Player Overall Age Club Value Wage
GK J. Hart 82 30 West Ham United €14M €110K
RWB K. Walker 83 27 Manchester City €24M €130K
CB G. Cahill 84 31 Chelsea €21M €160K
CB M. Keane 81 24 Everton €21M €91K
LWB D. Rose 82 26 Tottenham Hotspur €21M €99K
CM A. Lallana 83 29 Liverpool €25M €135K
CM E. Dier 82 23 Tottenham Hotspur €25M €85K
CM J. Henderson 82 27 Liverpool €21.5M €115K
RW R. Barkley 81 23 Everton €24M €105K
ST H. Kane 86 23 Tottenham Hotspur €59M €165K
LM D. Alli 84 21 Tottenham Hotspur €43M €115K
rating_4231_ENG_Potential, best_list_4231_ENG_Potential = get_best_squad_n(squad_4231_strict, 'England', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_4231_ENG_Potential))
print(best_list_4231_ENG_Potential)
-Potential-
Average rating: 87.4
Position Player Potential Age Club Value Wage
GK J. Butland 87 24 Stoke City €18M €50K
RWB A. Oxlade-Chamberlain 85 23 Liverpool €20M €105K
CB M. Keane 87 24 Everton €21M €91K
CB J. Stones 85 23 Manchester City €14.5M €105K
LB L. Shaw 84 21 Manchester United €14M €91K
CM A. Gomes 90 16 Manchester United €975K €9K
CM E. Dier 87 23 Tottenham Hotspur €25M €85K
RM M. Rashford 89 19 Manchester United €22M €74K
CAM M. Edwards 87 18 Tottenham Hotspur €1.2M €11K
LM D. Alli 90 21 Tottenham Hotspur €43M €115K
ST H. Kane 90 23 Tottenham Hotspur €59M €165K
3.6 Brazil
Having won the World Cup the most times in history, the Samba team will no doubt be one of the top candidates for this summer in Russia.
Brazil = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Brazil'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
Brazil.set_index('Nationality', inplace = True)
Brazil[['Overall', 'Potential']] = Brazil[['Overall', 'Potential']].astype(float)
print (Brazil)
Squad Overall Potential
Nationality
Brazil 3-4-3 85.36 88.45
Brazil 4-4-2 84.73 88.00
Brazil 4-3-1-2 84.64 88.00
Brazil 4-3-3 85.45 88.73
Brazil 4-2-3-1 85.36 88.73
As you can see, Brazil has similar options like England. I’ll go with 4-3-3 for the current rating and 4-2-3-1 for the potential rating.
rating_433_BRA_Overall, best_list_433_BRA_Overall = get_best_squad_n(squad_433_strict, 'Brazil', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_433_BRA_Overall))
print(best_list_433_BRA_Overall)
-Overall-
Average rating: 85.5
Position Player Overall Age Club Value Wage
GK Ederson 83 23 Manchester City €26M €87K
RB Dani Alves 84 34 Paris Saint-Germain €9M €115K
CB Thiago Silva 88 32 Paris Saint-Germain €34M €175K
CB David Luiz 86 30 Chelsea €33M €190K
LB Marcelo 87 29 Real Madrid CF €38M €250K
CDM Casemiro 85 25 Real Madrid CF €42M €195K
CAM Coutinho 86 25 Liverpool €56M €205K
CAM Willian 84 28 Chelsea €31.5M €200K
RM Douglas Costa 82 26 Juventus €24M €115K
CF Jonas 83 33 SL Benfica €16.5M €21K
LW Neymar 92 25 Paris Saint-Germain €123M €280K
rating_4231_BRA_Potential, best_list_4231_BRA_Potential = get_best_squad_n(squad_4231_strict, 'Brazil', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_4231_BRA_Potential))
print(best_list_4231_BRA_Potential)
-Potential-
Average rating: 88.7
Position Player Potential Age Club Value Wage
GK Ederson 89 23 Manchester City €26M €87K
RB Dani Alves 84 34 Paris Saint-Germain €9M €115K
CB Marquinhos 89 23 Paris Saint-Germain €30.5M €75K
CB Thiago Silva 88 32 Paris Saint-Germain €34M €175K
LB Marcelo 87 29 Real Madrid CF €38M €250K
CDM Casemiro 89 25 Real Madrid CF €42M €195K
CDM Fabinho 88 23 AS Monaco €29.5M €37K
RM Malcom 87 20 Girondins de Bordeaux €24.5M €47K
CAM Coutinho 89 25 Liverpool €56M €205K
LW Neymar 94 25 Paris Saint-Germain €123M €280K
ST Gabriel Jesus 92 20 Manchester City €31M €115K
3.7 Argentina
Lionel Messi is still waiting for the only trophy he hasn’t gotten yet in his career. Can he carry Argentina to the top after going short in the final 4 years ago?
Argentina = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Argentina'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
Argentina.set_index('Nationality', inplace = True)
Argentina[['Overall', 'Potential']] = Argentina[['Overall', 'Potential']].astype(float)
print (Argentina)
Squad Overall Potential
Nationality
Argentina 3-4-3 84.27 87.36
Argentina 4-4-2 83.45 87.27
Argentina 4-3-1-2 83.55 86.73
Argentina 4-3-3 84.27 87.36
Argentina 4-2-3-1 84.00 87.09
Both 3-4-3 and 4-3-3 fare very well for the current and potential ratings of Argentine players. For the sake of diversity, I’ll choose 3-4-3 for their current squad and 4-3-3 for their future squad.
rating_343_ARG_Overall, best_list_343_ARG_Overall = get_best_squad_n(squad_343_strict, 'Argentina', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_343_ARG_Overall))
print(best_list_343_ARG_Overall)
-Overall-
Average rating: 84.3
Position Player Overall Age Club Value Wage
GK G. Rulli 83 25 Real Sociedad €25.5M €28K
CB N. Otamendi 83 29 Manchester City €20M €140K
CB M. Musacchio 83 26 Milan €27M €105K
CB E. Garay 83 30 Valencia CF €19M €40K
RB P. Zabaleta 79 32 West Ham United €7M €99K
CM E. Banega 83 29 Sevilla FC €25.5M €27K
CM L. Biglia 83 31 Milan €17.5M €105K
LB M. Rojo 82 27 Manchester United €21M €130K
RW L. Messi 93 30 FC Barcelona €105M €565K
ST G. Higuaín 90 29 Juventus €77M €275K
LW A. Di María 85 29 Paris Saint-Germain €37.5M €145K
rating_433_ARG_Potential, best_list_433_ARG_Potential = get_best_squad_n(squad_433_strict, 'Argentina', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_433_ARG_Potential))
print(best_list_433_ARG_Potential)
-Potential-
Average rating: 87.4
Position Player Potential Age Club Value Wage
GK G. Rulli 89 25 Real Sociedad €25.5M €28K
RB J. Figal 82 23 Independiente €8M €16K
CB M. Musacchio 86 26 Milan €27M €105K
CB E. Mammana 86 21 Zenit St. Petersburg €10M €36K
LB M. Rojo 83 27 Manchester United €21M €130K
CM E. Barco 90 18 Independiente €6.5M €8K
CM G. Lo Celso 86 21 Paris Saint-Germain €10M €48K
CDM S. Ascacibar 86 20 VfB Stuttgart €7M €17K
RW L. Messi 93 30 FC Barcelona €105M €565K
ST P. Dybala 93 23 Juventus €79M €215K
LM M. Lanzini 87 24 West Ham United €24.5M €95K
3.8 Belgium
The Red Devils has some of the best players in English Premier League, but can’t never seem to make it far in the national level. Can Hazard and De Bruyne drive them far this time?
Belgium = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Belgium'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
Belgium.set_index('Nationality', inplace = True)
Belgium[['Overall', 'Potential']] = Belgium[['Overall', 'Potential']].astype(float)
print (Belgium)
Squad Overall Potential
Nationality
Belgium 3-4-3 83.91 86.55
Belgium 4-4-2 83.36 86.73
Belgium 4-3-1-2 83.09 86.09
Belgium 4-3-3 84.27 87.27
Belgium 4-2-3-1 84.00 87.00
Again, 4-3-3 is the best formation for Belgium’s both current and future squad.
rating_433_BEL_Overall, best_list_433_BEL_Overall = get_best_squad_n(squad_433_strict, 'Belgium', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_433_BEL_Overall))
print(best_list_433_BEL_Overall)
-Overall-
Average rating: 84.3
Position Player Overall Age Club Value Wage
GK T. Courtois 89 25 Chelsea €59M €190K
RB T. Foket 73 22 KAA Gent €5.5M €19K
CB T. Alderweireld 86 28 Tottenham Hotspur €40.5M €165K
CB J. Vertonghen 85 30 Tottenham Hotspur €28.5M €130K
LB T. Vermaelen 78 31 FC Barcelona €7M €120K
CDM M. Dembélé 83 29 Tottenham Hotspur €25M €115K
CAM R. Nainggolan 86 29 Roma €42.5M €130K
CAM A. Witsel 82 28 NaN €0 €0
RM K. De Bruyne 89 26 Manchester City €83M €285K
ST R. Lukaku 86 24 Manchester United €59M €210K
LW E. Hazard 90 26 Chelsea €90.5M €295K
rating_433_BEL_Potential, best_list_433_BEL_Potential = get_best_squad_n(squad_433_strict, 'Belgium', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_433_BEL_Potential))
print(best_list_433_BEL_Potential)
-Potential-
Average rating: 87.3
Position Player Potential Age Club Value Wage
GK T. Courtois 92 25 Chelsea €59M €190K
RB T. Foket 79 22 KAA Gent €5.5M €19K
CB T. Alderweireld 87 28 Tottenham Hotspur €40.5M €165K
CB L. Dendoncker 86 22 RSC Anderlecht €15M €25K
LB J. Lukaku 83 22 Lazio €10M €39K
CM D. Rigo 84 18 PSV €1.3M €3K
CAM Y. Tielemans 90 20 AS Monaco €23.5M €35K
CAM R. Nainggolan 86 29 Roma €42.5M €130K
RM K. De Bruyne 92 26 Manchester City €83M €285K
ST R. Lukaku 90 24 Manchester United €59M €210K
LW E. Hazard 91 26 Chelsea €90.5M €295K
3.9 - Portugal
The winner of Euro 2016 and the best player in the world 3 times in a row, Cristiano Ronaldo, has a real chance in this tournament as well.
Portugal = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Portugal'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
Portugal.set_index('Nationality', inplace = True)
Portugal[['Overall', 'Potential']] = Portugal[['Overall', 'Potential']].astype(float)
print (Portugal)
Squad Overall Potential
Nationality
Portugal 3-4-3 83.00 87.00
Portugal 4-4-2 83.00 87.27
Portugal 4-3-1-2 82.82 86.55
Portugal 4-3-3 83.45 87.45
Portugal 4-2-3-1 83.45 87.27
OK, I’ll go with 4-2-3-1 for current rating and 4-3-3 for potential rating of Portugal.
rating_4231_POR_Overall, best_list_4231_POR_Overall = get_best_squad_n(squad_4231_strict, 'Portugal', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_4231_POR_Overall))
print(best_list_4231_POR_Overall)
-Overall-
Average rating: 83.5
Position Player Overall Age Club Value Wage
GK A. Lopes 83 26 Olympique Lyonnais €22.5M €60K
RB Nélson Semedo 81 23 FC Barcelona €20.5M €125K
CB Pepe 86 34 Beşiktaş JK €13.5M €94K
CB José Fonte 79 33 West Ham United €5M €99K
LB Antunes 79 30 Getafe CF €9M €29K
CM William Carvalho 83 25 Sporting CP €27.5M €21K
CM Danilo Pereira 83 25 FC Porto €27.5M €18K
RM Bernardo Silva 84 22 Manchester City €43.5M €165K
CAM João Mário 83 24 Inter €34M €70K
LM Nani 83 30 Lazio €24.5M €47K
ST Cristiano Ronaldo 94 32 Real Madrid CF €95.5M €565K
rating_433_POR_Potential, best_list_433_POR_Potential = get_best_squad_n(squad_433_strict, 'Portugal', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_433_POR_Potential))
print(best_list_433_POR_Potential)
-Potential-
Average rating: 87.5
Position Player Potential Age Club Value Wage
GK A. Lopes 86 26 Olympique Lyonnais €22.5M €60K
RB Nélson Semedo 87 23 FC Barcelona €20.5M €125K
CB Pepe 86 34 Beşiktaş JK €13.5M €94K
CB Rúben Semedo 85 23 Villarreal CF €12.5M €27K
LB Rafa Soares 83 22 Fulham €8.5M €11K
CM William Carvalho 86 25 Sporting CP €27.5M €21K
CAM João Mário 88 24 Inter €34M €70K
CAM André Gomes 88 23 FC Barcelona €30M €150K
RM Bernardo Silva 91 22 Manchester City €43.5M €165K
ST Cristiano Ronaldo 94 32 Real Madrid CF €95.5M €565K
LM Gelson Martins 88 22 Sporting CP €25M €20K
3.10 - Uruguay
Uruguay has the 2 best strikers in Europe: Suarez & Cavani. Can they perform this time?
Uruguay = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Uruguay'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
Uruguay.set_index('Nationality', inplace = True)
Uruguay[['Overall', 'Potential']] = Uruguay[['Overall', 'Potential']].astype(float)
print (Uruguay)
Squad Overall Potential
Nationality
Uruguay 3-4-3 80.64 84.91
Uruguay 4-4-2 81.09 85.27
Uruguay 4-3-1-2 81.09 84.73
Uruguay 4-3-3 80.18 84.91
Uruguay 4-2-3-1 79.82 84.27
Fantastic, Uruguay does best with a 4-3-1-2 for current rating and 4-4-2 for future rating.
rating_4312_URG_Overall, best_list_4312_URG_Overall = get_best_squad_n(squad_4312_strict, 'Uruguay', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_4312_URG_Overall))
print(best_list_4312_URG_Overall)
-Overall-
Average rating: 81.1
Position Player Overall Age Club Value Wage
GK F. Muslera 82 31 Galatasaray SK €13.5M €57K
RB M. Cáceres 77 30 Hellas Verona €6.5M €28K
CB D. Godín 88 31 Atlético Madrid €40M €125K
CB J. Giménez 83 22 Atlético Madrid €28.5M €53K
LB F. Ricca 74 22 Málaga CF €7M €18K
CDM M. Vecino 78 25 Inter €13M €59K
CDM M. Lemos 77 21 UD Las Palmas €10.5M €17K
CM S. Cristóforo 76 23 Fiorentina €8.5M €48K
CF N. Lodeiro 78 28 Seattle Sounders FC €10.5M €11K
ST L. Suárez 92 30 FC Barcelona €97M €510K
ST E. Cavani 87 30 Paris Saint-Germain €48.5M €160K
rating_442_URG_Potential, best_list_442_URG_Potential = get_best_squad_n(squad_442_strict, 'Uruguay', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_442_URG_Potential))
print(best_list_442_URG_Potential)
-Potential-
Average rating: 85.3
Position Player Potential Age Club Value Wage
GK F. Muslera 82 31 Galatasaray SK €13.5M €57K
RB C. González 82 20 Sevilla Atlético €1.8M €3K
CB D. Godín 88 31 Atlético Madrid €40M €125K
CB J. Giménez 87 22 Atlético Madrid €28.5M €53K
LWB M. Saracchi 85 19 River Plate €3.4M €7K
RM F. Valverde 85 18 RC Deportivo de La Coruña €3.6M €44K
CM N. Nández 85 21 Boca Juniors €3.5M €11K
CM L. Torreira 83 21 Sampdoria €10M €26K
LM D. Laxalt 82 24 Genoa €13.5M €30K
ST L. Suárez 92 30 FC Barcelona €97M €510K
ST E. Cavani 87 30 Paris Saint-Germain €48.5M €160K
3.11 - Croatia
Well, I’m a big fan of Modric and Rakitic. Needless to say about their winning habits?
Croatia = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Croatia'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])
Croatia.set_index('Nationality', inplace = True)
Croatia[['Overall', 'Potential']] = Croatia[['Overall', 'Potential']].astype(float)
print (Croatia)
Squad Overall Potential
Nationality
Croatia 3-4-3 80.55 83.00
Croatia 4-4-2 80.82 83.00
Croatia 4-3-1-2 81.27 83.64
Croatia 4-3-3 81.64 83.91
Croatia 4-2-3-1 81.64 83.64
Dope, Croatia is superior with a 4-2-3-1 using current form and 4-3-3 using potential form.
rating_4231_CRO_Overall, best_list_4231_CRO_Overall = get_best_squad_n(squad_4231_strict, 'Croatia', 'Overall')
print('-Overall-')
print('Average rating: {:.1f}'.format(rating_4231_CRO_Overall))
print(best_list_4231_CRO_Overall)
-Overall-
Average rating: 81.6
Position Player Overall Age Club Value Wage
GK D. Subašić 85 32 AS Monaco €22M €46K
RB D. Srna 81 35 Shakhtar Donetsk €3.8M €1K
CB V. Ćorluka 82 31 Lokomotiv Moscow €15M €54K
CB D. Lovren 81 27 Liverpool €17.5M €110K
LB I. Strinić 75 29 Sampdoria €5M €31K
CDM L. Modrić 89 31 Real Madrid CF €57M €340K
CM I. Rakitić 87 29 FC Barcelona €48.5M €275K
RM J. Brekalo 70 19 VfB Stuttgart €3.1M €21K
CAM I. Perišić 84 28 Inter €31.5M €110K
LM M. Mandžukić 83 31 Juventus €23.5M €135K
ST N. Kalinić 81 29 Milan €18M €84K
rating_433_CRO_Potential, best_list_433_CRO_Potential = get_best_squad_n(squad_433_strict, 'Croatia', 'Potential')
print('-Potential-')
print('Average rating: {:.1f}'.format(rating_433_CRO_Potential))
print(best_list_433_CRO_Potential)
-Potential-
Average rating: 83.9
Position Player Potential Age Club Value Wage
GK D. Subašić 85 32 AS Monaco €22M €46K
RB T. Jedvaj 84 21 Bayer 04 Leverkusen €10M €36K
CB D. Lovren 83 27 Liverpool €17.5M €110K
CB V. Ćorluka 82 31 Lokomotiv Moscow €15M €54K
LB A. Milić 76 23 KV Oostende €2.7M €9K
CDM L. Modrić 89 31 Real Madrid CF €57M €340K
CDM M. Kovačić 88 23 Real Madrid CF €30M €150K
CM I. Rakitić 87 29 FC Barcelona €48.5M €275K
RM J. Brekalo 82 19 VfB Stuttgart €3.1M €21K
ST N. Kalinić 81 29 Milan €18M €84K
LW M. Pjaca 86 22 Juventus €12.5M €89K
4 - Final Comparison
Ok, let’s make some comparison between these 10 line-ups with the current rating of players for these strongest contenders for World Cup 2018.
teams = ('France', 'Germany', 'Spain', 'England', 'Brazil', 'Argentina', 'Belgium', 'Portugal', 'Uruguay', 'Croatia')
index = np.arange(len(teams))
average_overall = [84.6, 86.3, 86.6, 82.7, 85.5, 84.3, 84.3, 83.5, 81.1, 81.6]
plt.figure(figsize=(16,8))
plt.barh(index, average_overall, align='center', alpha=0.5)
plt.yticks(index, teams, fontsize=20)
plt.ylabel('National Teams', fontsize=25)
plt.xlabel('Average Overall', fontsize=25)
plt.title('Average Overall Rating of Players', fontsize=30, fontweight='bold', y=1.05,)
plt.show()
total_value = [446.5, 502, 484.5, 298.5, 433, 382, 440.5, 323, 283.5, 244.9]
plt.figure(figsize=(16,8))
plt.barh(index, total_value, align='center', alpha=0.5, color='red')
plt.yticks(index, teams, fontsize=20)
plt.ylabel('National Teams', fontsize=25)
plt.xlabel('Total Value in Million Euros', fontsize=25)
plt.title('Total Value of Players', fontsize=30, fontweight='bold', y=1.05,)
plt.show()
average_wage = [141, 188, 228, 119, 167, 151, 166, 118, 99, 110]
plt.figure(figsize=(16,8))
plt.barh(index, average_wage, align='center', alpha=0.5, color='green')
plt.yticks(index, teams, fontsize=20)
plt.ylabel('National Teams', fontsize=25)
plt.xlabel('Average Wages in Thousand Euros', fontsize=25)
plt.title('Average Wages of Players', fontsize=30, fontweight='bold', y=1.05,)
plt.show()
Conclusion
So based purely on the FIFA 18 Data:
- Spain has the highest average overall rating, followed by Germany and Brazil.
- Germany has the highest total value, followed by Spain and France.
- Spain has the highest average wage, followed by Germany and Brazil.
My bet is for a Spain vs France in the final, and Argentina vs Brazil for the 3rd place. Your turn?
- Also, this is personal but Portugal might just surprise us :P
- Hail Ronaldo!