Introduction

FIFA 22 is a football simulation video game published by Electronic Arts. It is the 29th installment in the FIFA series, and was released worldwide on 1 October 2021 for Microsoft Windows, Nintendo Switch, PlayStation 4, PlayStation 5, Xbox One and Xbox Series X/S.Wikipedia

In this project I tried to answer questions such as the who is highest paid player, best player, fastest player, best finisher using descriptive and visual analysis.

I used Google Colab to carry out this project.

The FIFA 22 Dataset I used can be found at Kaggle.

The notebook can be found on my GitHub.

Import the Required Libraries

import numpy as np
import pandas as pd
import os
import seaborn as sns
from scipy import stats
import missingno as msno
import math


%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import style
sns.set(style='ticks', color_codes=True)
sns.set(style='darkgrid')
import plotly.express as px


import warnings
warnings.filterwarnings(action="ignore", message="^internal gelsd")
warnings.filterwarnings('always')
warnings.filterwarnings('ignore')

Installing other requirements like kaggle for loading the dataset

! pip install wordcloud

Requirement already satisfied: wordcloud in /usr/local/lib/python3.7/dist-packages (1.5.0)
Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from wordcloud) (1.21.6)
Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from wordcloud) (7.1.2)

from wordcloud import WordCloud

Installing the kaggle libraries for interacting with the kaggle api and uploading my kaggle api key to my environment.

! pip install -q kaggle
from google.colab import files

files.upload()

! mkdir ~/.kaggle

! cp kaggle.json ~/.kaggle/

Downloading the kaggle fifa dataset into my colab environment

! kaggle datasets download -d stefanoleone992/fifa-22-complete-player-dataset

/root/.kaggle/kaggle.json'
Downloading fifa-22-complete-player-dataset.zip to /content
 82% 89.0M/109M [00:00<00:00, 105MB/s]
100% 109M/109M [00:01<00:00, 110MB/s] 

Unzipping the contents of the zip dataset into our environment. In this case I'll be working with the 'players_22.csv' file.

! unzip fifa-22-complete-player-dataset.zip

Archive:  fifa-22-complete-player-dataset.zip
  inflating: Career Mode female player datasets - FIFA 16-22.xlsx  
  inflating: Career Mode player datasets - FIFA 15-22.xlsx  
  inflating: female_players_16.csv   
  inflating: female_players_17.csv   
  inflating: female_players_18.csv   
  inflating: female_players_19.csv   
  inflating: female_players_20.csv   
  inflating: female_players_21.csv   
  inflating: female_players_22.csv   
  inflating: players_15.csv          
  inflating: players_16.csv          
  inflating: players_17.csv          
  inflating: players_18.csv          
  inflating: players_19.csv          
  inflating: players_20.csv          
  inflating: players_21.csv          
  inflating: players_22.csv          

Reading the csv file with pandas and displaying the first 5 rows of the dataset

fifa = pd.read_csv('players_22.csv')
fifa.head()

Getting the column names in the dataset.

print(list(fifa.columns))

['sofifa_id', 'player_url', 'short_name', 'long_name', 'player_positions', 'overall', 'potential', 'value_eur', 'wage_eur', 'age', 'dob', 'height_cm', 'weight_kg', 'club_team_id', 'club_name', 'league_name', 'league_level', 'club_position', 'club_jersey_number', 'club_loaned_from', 'club_joined', 'club_contract_valid_until', 'nationality_id', 'nationality_name', 'nation_team_id', 'nation_position', 'nation_jersey_number', 'preferred_foot', 'weak_foot', 'skill_moves', 'international_reputation', 'work_rate', 'body_type', 'real_face', 'release_clause_eur', 'player_tags', 'player_traits', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure', 'defending_marking_awareness', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'goalkeeping_speed', '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', 'gk', 'player_face_url', 'club_logo_url', 'club_flag_url', 'nation_logo_url', 'nation_flag_url']

Turning the CSV file, 'players_22.csv' into a pandas Dataframe.

fifa_df=pd.DataFrame(fifa)

Setting the new columns that will be used for analysis.

new_columns = ['long_name', 'player_positions', 'overall', 'potential', 'value_eur', 'wage_eur', 'age', 'dob', 'height_cm', 'weight_kg', 'club_name', 'league_name', 'league_level', 'club_position', 'club_loaned_from', 'club_joined', 'club_contract_valid_until', 'nationality_name', 'nation_team_id', 'nation_position', 'preferred_foot', 'weak_foot', 'skill_moves', 'international_reputation', 'work_rate', 'body_type', 'real_face', 'release_clause_eur', 'player_tags', 'player_traits', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure', 'defending_marking_awareness', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'goalkeeping_speed','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', 'gk']

fifa_df=fifa[new_columns]

Getting the first 5 rows of the Dataset.

fifa_df.head()

VISUAL ANALYSIS OF FIFA 22 PLAYER RATINGS

In this section I used bar charts, pie charts, scatter plots etc to carry out visual analysis and gain insight from the dataset.

fig, ax = plt.subplots(figsize=(22,20))
sns.set_context('poster')
corr = fifa_df.corr()
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns,cmap='Blues')
ax.set_title('Collinearity of FIFA 2022 Attributes')
plt.savefig('cormap.png')

WordCloud of Nationalities and Clubs.

A word cloud is a collection, or cluster, of words depicted in different sizes. The bigger and bolder the word appears, the more often it’s mentioned within a given text and the more important it is.

Word Cloud of Nationalities

d_frame = fifa_df.nationality_name

plt.subplots(figsize=(15,15))
wordcloud = WordCloud(
                   background_color='white',
                        width = 2000,
                        height = 1200
    ).generate(" ".join(d_frame))

plt.imshow(wordcloud)
plt.axis('off')

Names of Most frequently mentioned Countries

fifa_df.groupby("nationality_name").long_name.count().sort_values(ascending=False).head(10)/100

nationality_name
England          17.19
Germany          12.14
Spain            10.86
France            9.80
Argentina         9.60
Brazil            8.97
Japan             5.46
Netherlands       4.39
United States     4.13
Poland            4.03
Name: long_name, dtype: float64

Word Cloud of Club Names

# Word Cloud of Club Names
fifa_df['club_name']= fifa_df['club_name'].apply(str) # Convert Club names to string because some clubs have numbers in their name e.g Schalke 04
d_frame = fifa_df.club_name

plt.subplots(figsize=(15,15))
wordcloud = WordCloud(
                   background_color='white',
                        width = 2000,
                        height = 1200
    ).generate(" ".join(d_frame))

plt.imshow(wordcloud)
plt.axis('off')

Occurence of Club Names

fifa_df.groupby("club_name").long_name.count().sort_values(ascending=False).head(10)/100

club_name
Real Betis Balompié          0.33
ESTAC Troyes                 0.33
Newcastle United             0.33
RCD Espanyol de Barcelona    0.33
RCD Mallorca                 0.33
Arsenal                      0.33
Brighton & Hove Albion       0.33
Villarreal CF                0.33
Brentford                    0.33
Name: long_name, dtype: float64

Top 20 Best FIFA 22 Players Based on Overall Rating

Overall = fifa_df["overall"]
Player_name = fifa_df["long_name"]


data = pd.DataFrame({'long_name': Player_name,'overall':Overall})

x = data.long_name.head(20)  
y = data.overall.head(20)

sns.set_context('talk')
plt.figure(figsize=(15,10))

ax= sns.barplot(x=y, y=x,palette ='Blues_r') 
plt.xticks(rotation= 90)
plt.xlabel('Overall', size = 20) 
plt.ylabel('Name', size = 20 ) 
plt.title('Best Players in FIFA 22 Based on Overall Rating') # title
plt.show()
plt.savefig('Top20.png')
for p in ax.patches:
    ax.annotate(str(p.get_height()), (p.get_x()-0.1, p.get_height()+30))

It can be seen from the chart that Lionel Messi who currently plays for Paris Saint German is the best player in FIFA 22.

Players Preferred foot

preferred_foot_labels = fifa_df["preferred_foot"].value_counts().index 
preferred_foot_values = fifa_df["preferred_foot"].value_counts().values 
explode = (0, 0.1) # used to separate a slice of cake


plt.figure(figsize = (7,7))
plt.pie(preferred_foot_values, labels=preferred_foot_labels,explode=explode, autopct='%1.2f%%')
plt.title('Football Players Preferred Feet',color = 'darkblue',fontsize = 15)
plt.legend()
#plt.show()
plt.savefig('Foot Piechart.jpg')

It can be seen from the pie chart that a very high percentage of players are right-footed.

Top 10 Highest Paid Players in FIFA 22 and their Respective Nationalities

# TOp 10 Players with the highest wages with Nationality
plt.figure(figsize=(15,8))
player_name = fifa_df.nlargest(10, ['wage_eur']).set_index('long_name')
sns.barplot(player_name['wage_eur'], player_name.index, ci = None, hue = player_name['nationality_name'],palette="deep")
plt.xlabel('Total wages')

It can be seen from the bar chart that Kevin De Bruyne who plays for Manchester City FC in the English Premier League is the highest paid player in FIFA 22.

Top 10 Highest Paid Players and their Clubs

plt.figure(figsize=(15,8))
player_name = fifa_df.nlargest(10, ['wage_eur']).set_index('long_name')
sns.barplot(player_name['wage_eur'], player_name.index, ci = None, hue = player_name['club_name'],palette="deep")
plt.xlabel('Total wages')

Text(0.5, 0, 'Total wages')

Player Height Distribution

# Player Height Distribution
sns.set_style('darkgrid')
sns.set_context('talk')
plt.figure(figsize=(26,14))
ax = fifa_df.groupby('height_cm')['long_name'].count().plot.bar()
plt.title('Number of Players')
plt.xticks(fontsize = 12,rotation=0)
for p in ax.patches:
    ax.annotate(str(p.get_height()), (p.get_x()-0.1, p.get_height()+30))
plt.savefig('heightplot.png')

This chart shows that most player heights average around 180cm in FIFA 22.

Degrees of Players and Skill Moves Rating

sns.set_context('paper')
skill_moves_labels = fifa_df["skill_moves"].value_counts().index 
skill_moves_values = fifa_df["skill_moves"].value_counts().values
colors = ['orange','indigo','grey','violet','red']


plt.figure(figsize = (6,6))
circle = plt.Circle((0,0),0.3,color = "white") 
plt.pie(skill_moves_values, labels = skill_moves_labels, colors = colors)
p = plt.gcf()
p.gca().add_artist(circle) # we add the hole to the chart
plt.title("Degrees of Players and Skill Moves Rating")
plt.legend()

plt.savefig('DonutChartSkillMove.png')

Skill moves are special tricks a player can execute in order to beat his man i.e get past a player or just to taunt a player. A player having a rating of 5 for skill moves means the player can execute all the skills in the game. It can be seen though that there is a very little amount of players who have 5 star skill rating in FIFA 22.

Relationship between Player Potential and Wage

plt.figure(figsize=(7, 5))
ax = sns.scatterplot(x =fifa_df['potential'], y = fifa_df['wage_eur'])
plt.xlabel("Potential") 
plt.ylabel("Wage EUR")
plt.title("Potential & Wage", fontsize = 18)
plt.show()
plt.savefig('chart1.png')

It can be seen from this scatter plot that player wage increases as player potential increases.

Relationship between Age and Wage

plt.figure(figsize=(7, 5))
ax = sns.scatterplot(x =fifa_df['age'], y = fifa_df['wage_eur'])
plt.xlabel("Age") 
plt.ylabel("Wage EUR")
plt.title("Age & Wage", fontsize = 18)
plt.show()
plt.savefig('chart2.png')

Relationship between Player Overall and Age

plt.figure(figsize=(7, 5))
ax = sns.scatterplot(x =fifa_df['overall'], y = fifa_df['age'])
plt.xlabel("Overall") 
plt.ylabel("Age")
plt.title("Overall & Age", fontsize = 18)
plt.show()
plt.savefig('chart3.png')

Descriptive Analysis of FIFA 22 Player Ratings

In this section I used the dataset to carry out descriptive analysis and answer questions like who the fastest player is, the best finisher, best player etc.

Who are the top 10 fastest players?

# fastest players
player_name = fifa_df[["long_name",'age',"player_positions",'nationality_name','movement_sprint_speed',"movement_acceleration"]].nlargest(10, ['movement_acceleration']).set_index('long_name')
player_name

It can be seen that Kylian Mbappe Lottin of Paris Saint German is the fastest player in FIFA 22. Personally I feel using PSG is like cheating because they have so many good players:laughing:

Who are the top 10 slowest players?

# Top 10 Slowest Players
player_name = fifa_df[["long_name",'age',"player_positions",'nationality_name','movement_sprint_speed',"movement_acceleration"]].nsmallest(10, ['movement_acceleration']).set_index('long_name')
player_name

It can be seen that the slowest players in the game are all Goalkeepers.

Who are the Top 10 Tallest Players?

# Tallest Players
player_name = fifa_df[["long_name",'age',"player_positions",'nationality_name','height_cm']].nlargest(10, ['height_cm']).set_index('long_name')
player_name

Unsurprisingly it can be seen that the tallest players are mostly Goalkeepers. Height is a very important trait of being a goalkeeper.

Who are the top 10 shortest players?

# Shortest Players
player_name = fifa_df[["long_name",'age',"player_positions",'nationality_name','height_cm']].nsmallest(10, ['height_cm']).set_index('long_name')
player_name

Who are the top 10 heaviest players?

# Top 10 Heaviest Players
player_name = fifa_df[["long_name",'age',"player_positions",'nationality_name','weight_kg']].nlargest(10, ['weight_kg']).set_index('long_name')
player_name

It can be seen striker for League One club Wycombe Wanderers, Saheed Adebayo Akinfenwa weighs the most in FIFA 22.

Who are the top 10 oldest players?

player_name = fifa_df[["long_name","player_positions",'nationality_name','age']].nlargest(10, ['age']).set_index('long_name')
player_name

It can be seen that Kazuyoshi Miura is the oldest player in FIFA 22. Miura holds the records for being the oldest goalscorer in the J-League, the footballer with the world's longest professional career and, as of 2022, is the oldest professional footballer in the world at 55.He also holds the possibly unique distinction of having played professional football in five separate decades (1980s–2020s). Wikipedia

Who are the top 10 youngest players?

# Youngest Players
player_name = fifa_df[["long_name","player_positions",'nationality_name','age']].nsmallest(10, ['age']).set_index('long_name')
player_name

Top 10 Players Based on Defensive Awareness Rating

player_name = fifa_df[["long_name","defending_marking_awareness","player_positions",'age','nationality_name']].nlargest(10, ['defending_marking_awareness']).set_index('long_name')
player_name

Giorgio Chiellini who plays for Juventus in Italy has the best Defensive Marking rating in FIFA 22.

Top 10 Players Based on their Long Passing Rating

player_name = fifa_df[["skill_long_passing","long_name","attacking_short_passing","power_stamina",'age','nationality_name','weight_kg','player_positions']].nlargest(10, ['skill_long_passing']).set_index('long_name')
player_name

Kevin De Bruyne is a mid field all rounder and its not surprising to see that he has the best long pass rating in FIFA 22.

Top 10 Players Based on the Dribbling Rating

player_name = fifa_df[["dribbling","long_name","skill_ball_control","movement_agility",'age','nationality_name','weight_kg','player_positions']].nlargest(10, ['dribbling']).set_index('long_name')
player_name

Lionel Messi who many people consider the GOAT(Greatest of All Time) is the best dribbler in FIFA 22.

Top 10 Players Based on their Crossing Ability

# Best Crosser of the Ball
player_name = fifa_df[["attacking_crossing","long_name","overall",'age','nationality_name','club_name']].nlargest(10, ['attacking_crossing']).set_index('long_name')
player_name

KDB again 😀

Best Strikers based on their Finishing Ability

player_name = fifa_df[['attacking_finishing','mentality_penalties', 'mentality_composure','pace', 'shooting',"long_name","overall",'age','nationality_name','club_name']].nlargest(10, ['attacking_finishing']).set_index('long_name')
player_name

Lionel Messi and Robert Lewandowski who plays for Bayern Munich in Germany are the best finishers in FIFA 22.

Top Rated Players based on their Wages

player_name = fifa_df[["wage_eur","long_name","value_eur","overall",'age','nationality_name','potential','club_name']].nlargest(10, ['wage_eur']).set_index('long_name')
player_name

KDB and Karim Benzema who plays as a striker for Real Madrid in Spain are the highest paid players in FIFA 22 and rightly so they're incredible players.

Top 10 Best Goalkeepers

player_name = fifa_df[['goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'goalkeeping_speed',"long_name","overall",'age','nationality_name','club_name']].nlargest(10, ['goalkeeping_diving']).set_index('long_name')
player_name

These are the top 10 Goalkeepers in FIFA 22 and their respective Goalkeeper related ratings.

Summary

In summary I've been able to answer the following questions through visual and descriptive analysis:

• Who are the top 10 fastest players?
• Who are the top 10 slowest players?
• Who are the Top 10 Tallest Players?
• Who are the top 10 shortest players?
• Who are the top 10 heaviest players?
• Who are the top 10 oldest players?
• Who are the top 10 youngest players?

I've also been able to list out the best players based oon respective ratings :

• Top 20 Best FIFA 22 Players Based on Overall Rating
• Players Preferred foot
• Top 10 Highest Paid Players in FIFA 22 and their Respective Nationalities
• Top 10 Highest Paid Players and their Clubs
• Top 10 Players Based on Defensive Awareness Rating
• Top 10 Players Based on their Long Passing Rating
• Top 10 Players Based on the Dribbling Rating
• Top 10 Players Based on their Crossing Ability
• Best Strikers based on their Finishing Ability
• Top Rated Players based on their Wages
• Top 10 Best Goalkeepers.

I also showed the distribution and relationship of certain player features such as:

• Player Height Distribution
• Degrees of Players and Skill Moves Rating
• Relationship between Player Potential and Wage
• Relationship between Age and Wage
• Relationship between Player Overall and Age.