Exploratory Data Analysis With SQL

An Analysis of the FIFA22 Players Dataset

Exploratory Data Analysis(EDA) is a crucial first step in the data analysis process. it invloves the use of various techniques, tools, and visualization to understand the structure, patterns, and characteristics of a data-set. The primary goal of EDA is to gain insights from the data, identify patterns and trends, detect anomalies, and prepare the data for further analysis and modeling.

The FIFA22 dataset is extracted from the popular football video games of FIFA. Every year, there is a new game released with updated characteristics and skills for all players from all the biggest competitions in the world. This means that there are up-to-date characteristics and skills of the players that were transferred between 2015 and 2022. The player characteristics can be found in the variables: player name, age, height in centimeters, weight in kilogram, nationality, player position, preferred foot, international reputation, and contract duration etc.The field player skills are divided into six subcategories, namely passing, defending, physical, dribbling, shooting, and pace.
For goalkeepers, the subcategories are kicking, handling, shooting, reflexes, diving, and speed. The subcategories all have subcategories themselves as well. Also, every player has an overall rating and a potential rating in the game. All ratings are in the range of 1 and 99, where the lowest is 1 and where 99 is the highest possible score.

The information in the datasets is derived from sofifa.com, which is a well-known website with an overview of all FIFA data per year. A copy of the data-set can be downloaded from this kaggle data repositry

The aim of this article is to use EDA technique to dive into the FIFA22 data-set, derive some interesting insights from the data-set and identify anomalies.

The comon question we want to answer include:

• Who are the top fastest players in FIFA 2022?
• Which Players are paid the most?
• Who are the Top tallest players?
• Who are the Top strongest players?
• Who are the best players with Long passes?
• Who are the best defenders?

PREREQUISITE

You need to have a basic knowledge of SQl in order to follow through this guide, also you can install any of the SQL tool which we are going to be using to run the codes i.e. MySQL, MSSQL, PostgreSQL etc.

LOADING THE DATA SET

At first we need to load the dataset we are going to work with. There are several ways to load/import data set into an SQl database, for this guidei am going to use python to import the data set into PostgreSQL.

Note: other SQL work bench allow's to import directly from the workbench eg.MySQL. Since i am using Postgres where importing data set is somehow complecated, i decided to use python to import the data set into the PSQL db which is a more faster and convinience way.

To import data set into SQL using python;

The to_sql() function in Python is used to write records from a Pandas DataFrame to a SQL database. It is a very powerful function that can be used to store data in a variety of database formats.

The syntax for the to_sql() function is as follows:

import pandas as pd
player_df = pd.read_csv('player_22')

from sqlalchemy import create_engine
engine = create_engine('postgresql://postgres:mlyt09@localhost:5432/projectsql')

Here: postgresql:// > the sql_tool, postgres: > the user, mlyt09 > the password, @localhost: > the host, 5432 > the port, /projectsql the database.

player_df.to_sql(name='players', con=engine,index=False if_exists='replace')

• player_df is the Pandas DataFrame that you want to write to the database.
• name is the name of the table that you want to create in the database.
• con is a connection object to the database.
• if_exists specifies what to do if the table already exists. The possible values are fail, replace, and append.
• index specifies whether to write the index of the DataFrame to the database.
• dtype specifies the data types of the columns in the DataFrame.
• schema specifies the schema of the database.

Check to confirm the data is in PSQL...

projectsql=# \dt players

List of relations

Schema	Name	Type	Owner
public	players	table	postgres

projectsql=# \d players

Table "public.players"

Column	Type	Collation	Nullable	Default
sofifa_id	bigint
player_url	text
short_name	text
long_name	text
year	bigint
player_positions	text
overall	bigint
potential	bigint
value_eur	double precision
wage_eur	double precision
age	bigint
dob	date
height_cm	bigint
weight_kg	bigint

100+ other columns.....

Let Start by Using SQL Queries to answer some of our quetions.

(Qr1) Check to confirm the total number of records/players in the dataset:

Note: Qr here means query, so Qr1,2,3 > Query1,2,3 etc.

SELECT COUNT(*) AS total_no_of_players FROM players;

total_no_of_players
19239

(Qr2) Top 10 Nationality of Players for 2022:

SELECT DISTINCT nationality_name, COUNT(nationality_name) AS total_no_of_players
    FROM players  GROUP BY nationality_name
    ORDER BY total_no_of_players DESC LIMIT 10;

nationality_name	total_no_of_players
England	1719
Germany	1214
Spain	1086
France	980
Argentina	960
Brazil	897
Japan	546
Netherlands	439
United States	413
Poland	403

As can be noted above, we use the DISTINCT key word to select the unique/distinct nationality of players. With the query above, using the COUNT function we are able to count the total number of players represeting each country,
from the result we could see that england has the highest representative in club-football world @2022.

(Qr3) Countries with the most Football Talented (by pontential) Players:

The potential of a football player is a complex and multifaceted concept. It can be defined as the player's ability to reach their full potential as a footballer. This potential is determined by a number of factors, including the player's physical attributes, technical skills, tactical awareness, mental strength, and psychological makeup.

The potential of a football player is not something that is fixed. It can be developed and improved through training, hard work, and dedication. Players who are willing to put in the hard work and dedication can reach their full potential and become world-class footballers.

SELECT sum(potential) FROM players  WHERE potential > 79;

SELECT DISTINCT nationality_name, ROUND((SUM(potential)/147969)*100,2) potential
    FROM players
    WHERE potential > 79
    GROUP BY nationality_name
    ORDER BY potential DESC LIMIT 10;

nationality_name	potential
Spain	13.03
England	9.29
France	8.85
Brazil	6.13
Argentina	5.95
Germany	5.04
Portugal	4.78
Netherlands	4.30
Italy	3.93
Belgium	2.86
(10 rows)

Age and Performance are use to rate the potential of a player, the more younger and good performance the more highly potentialy rated. These are one of the metrics club-scouts use to scout for new young and talented players to joined their project. As can be seen above, we calculated for the percentage of potiential players each county has, it's no suprise to see that players from european and south american country have the best potential players.

(Qr4) Top 10 Fastest Players:

In the following code, these atrribute listed below will be use to rank the fastest players;


movement_acceleration, movement_sprint_speed, pace,club_position.

The most important player attribute from movement_acceleration, movement_sprint_speed, and pace is movement_acceleration. This is because acceleration is the rate at which a player can change their speed. This is important for players in all positions, but it is especially important for players who play in attacking positions, such as wingers and strikers.

Movement_sprint_speed is also important, but it is not as important as movement_acceleration. This is because sprint_speed is the maximum speed that a player can reach. However, players do not need to reach their maximum speed very often. They need to be able to accelerate quickly in order to get past defenders and create scoring opportunities.

Pace is a combination of movement_acceleration and movement_sprint_speed. It is a good overall measure of a player's speed, but it is not as specific as movement_acceleration or movement_sprint_speed.

SELECT short_name, movement_acceleration,
       movement_sprint_speed, pace,club_position,nationality_name
    FROM players ORDER BY movement_acceleration DESC LIMIT 10;

short_name	movement_acceleration	movement_sprint_speed	pace	club_position	nationality_name
K. Mbappé	97	97	97	ST	France
Adama Traoré	97	96	96	LW	Spain
M. Diaby	96	92	94	RM	France
D. James	96	95	95	SUB	Wales
A. Davies	96	96	96	LB	Canada
A. Hakimi	95	95	95	RB	Morocco
Vinícius Jr.	95	95	95	SUB	Brazil
R. Sterling	95	88	91	SUB	England
U. Antuna	95	90	92	SUB	Mexico
C. Ejuke	95	93	94	LM	Nigeria

(10 rows)

Acceleration usually defines the rate at which someone/thing moves. together with other varibles concider we could see that the most fastest player is Kylian mbape which is no suprising due to his high attacking game.

BONUS: Code Explanation

Let's break down the code line by line:

• SELECT is the keyword that tells the database to select specific columns from a table.
• long_name, movement_acceleration, movement_sprint_speed, pace, club_position, and nationality_name are the columns that we want to select.
• FROM is the keyword that tells the database which table to select the columns from. In this case, the table is called players.
• ORDER BY is the keyword that tells the database to order the results by the movement_acceleration column in descending order.
• DESC is the keyword that tells the database to order the results in descending order.
• LIMIT is the keyword that limits the results to the first 10 rows.

The code will return the following results:


The long_name, movement_acceleration, movement_sprint_speed, pace, club_position, and nationality_name of the 10 players with the highest movement_acceleration.
The results will be ordered by the movement_acceleration column in descending order.

(Qr5) Top 10 Strongest Players by Stregnth:

SELECT short_name, power_strength, power_stamina,physic, weight_kg, club_name
    FROM players
    ORDER BY power_strength DESC LIMIT 10;

short_name	power_strength	power_stamina	physic	weight_kg	club_name
A. Akinfenwa	97	34	74	110	Wycombe Wanderers
T. Petrášek	96	57	79	99	Raków Częstochowa
D. Dike	96	59	78	100	Orlando City Soccer Club
R. Lukaku	95	71	83	94	Chelsea
A. Seck	95	73	86	95	Royal Antwerp FC
A. Méndez	95	81	86	82	Club Nacional de Football
G. Kondogbia	94	82	89	76	Atlético de Madrid
S. Coates	94	80	87	92	Sporting CP
C. Luyindama	94	73	85	91	Galatasaray SK
K. Koulibaly	94	70	85	89	Napoli

(10 rows)

The strength and stamina of a players usually determines thier stability, compose and control of the ball. Players with high qualities of the aforemention usually have an edge over oposition players(defend or attack).

(Qr6) Top 10 Strongest Players by stamina:

SELECT long_name, power_strength, power_stamina,physic, weight_kg, club_name
 FROM players
 ORDER BY power_stamina DESC LIMIT 10

long_name	power_strength	power_stamina	physic	weight_kg	club_name
N'Golo Kanté	72	97	83	70	Chelsea
Jewgienij Baszkirow	52	96	67	71	Zagłębie Lubin
Rhyan Bert Grant	77	95	82	79	Sydney FC
Vladimír Darida	48	95	63	67	Hertha BSC
Andrew Robertson	65	95	76	64	Liverpool
Nicol├▓ Barella	69	95	78	68	Inter
奥埜 博亮	71	95	73	68	Cerezo Osaka
Pieter Gerkens	58	94	71	72	Royal Antwerp FC
Didier Andrés Moreno Asprilla	79	94	79	77	Junior FC
Denzel Justus Morris Dumfries	89	94	89	80	Inter

(10 rows)

Best Players FIFA22 (position wise)

Who were the best players(position wise) for the FIFA22 video game, concidering variables that are related to each player position we are going to answer the following questions;

(Qr7) Best Wingers:

Looking at the dataset, we will realize both the lw(Left-Wing) and rw(Right-Wing) column are formatted as text even though they contain numbers, with the below query we could understand why.
Some of the number contain character that are not number, we will have to clean this up and convert the columns into an integer!.

--lw
SELECT lw FROM players WHERE lw LIKE '%-%';

The code above is a SQL SELECT statement. It is used to select all rows from the players table where the lw column contains a hyphen (-).

lw
83-1
83-1
82-1
81-1
81-1

...... 300+ other values

SELECT lw, SUBSTRING(lw,1,POSITION('-' in  lw)-1) AS lw_cleaned
    FROM players
    WHERE lw LIKE '%-%';

The code above is an SQL SELECT statement that will return the lw column from the players table, along with a new column called lw_cleaned. The lw_cleaned column will contain the value of the lw column, but with the hyphen (-) removed.

lw	lw_cleaned
83-1	83
83-1	83
82-1	82
81-1	81
81-1	81

.... 300+ other values

UPDATE players SET lw = SUBSTRING(lw,1,POSITION('-' in  lw)-1) WHERE lw LIKE '%-%'

UPDATE 326

The code above is an SQL UPDATE statement that will update the lw column in the players table. The update will remove the hyphen (-) from the lw column, if it is present.

Finally we need to convert the the column to integer to be able to apply any arithmetic operation.

ALTER TABLE players ALTER COLUMN  lw TYPE Bigint USING lw::bigint

ALTER TABLE

The above is an SQL ALTER TABLE statement that will change the data type of the lw column in the players table to Bigint.

Let's apply the above to rw(right-winger):

-- rw
SELECT rw FROM players WHERE rw LIKE '%-%'

rw
75-1
83-1
82-1
81-1
81-1

.....300+

SELECT rw, SUBSTRING(rw,1,POSITION('-' in  rw)-1) AS rw
    FROM players
    WHERE rw LIKE '%-%'

rw	rw
75-1	75
83-1	83
82-1	82
81-1	81
81-1	81
80-1	80
80-1	80
80-1	80

UPDATE players SET rw = SUBSTRING(rw,1,POSITION('-' in  rw)-1) WHERE rw LIKE '%-%'

UPDATE 326

ALTER TABLE players ALTER COLUMN  rw type Bigint USING rw::bigint

ALTER TABLE

Finally we can add the values for lw & rw for each players to get the highest rating player, playing in the wing role!.

The code below is an SQL SELECT statement that will select specific columns from the players table and order the results by the wingers column in descending order. The LIMIT clause limits the results to the first 10 rows.

The most important attribute of a winger from skill_dribbling, attacking_short_passing, and movement_acceleration is skill_dribbling. This is because dribbling is the ability to control the ball while moving, which is essential for wingers who are tasked with beating defenders and creating scoring opportunities.

Wingers need to be able to dribble past defenders in order to create space for themselves and their teammates. They also need to be able to dribble in tight spaces, such as in the penalty area.

Attacking_short_passing is also important for wingers, as they need to be able to pass the ball accurately and quickly to their teammates in order to create scoring opportunities. However, it is not as important as skill_dribbling, as wingers do not need to pass the ball as often as they need to dribble.

Movement_acceleration is also important for wingers, as they need to be able to accelerate quickly in order to get past defenders. However, it is not as important as skill_dribbling or attacking_short_passing, as wingers do not need to accelerate as often as they need to dribble or pass the ball.

SELECT short_name, rw + lw wingers,skill_dribbling,attacking_short_passing,
       movement_acceleration,overall,age, club_name
       FROM players  ORDER BY wingers DESC LIMIT 10;

short_name	wingers	skill_dribbling	attacking_short_passing	movement_acceleration	overall	age	club_name
L. Messi	184	96	91	91	93	34	Paris Saint-Germain
Neymar Jr	180	95	86	93	91	29	Paris Saint-Germain
K. Mbappé	180	93	85	97	91	22	Paris Saint-Germain
Cristiano Ronaldo	176	88	80	85	91	36	Manchester United
M. Salah	176	90	84	89	89	29	Liverpool
K. De Bruyne	176	88	94	76	91	30	Manchester City
R. Sterling	174	87	83	95	88	26	Manchester City
H. Son	174	87	84	85	89	28	Tottenham Hotspur
S. Mané	174	90	84	93	89	29	Liverpool
P. Dybala	174	90	87	88	87	27	Juventus

(10 rows)

Here is an explanation of the 'rw + lw' AS 'wingers' part of the code:

• The rw + lw expression adds the values of the rw and lw columns.
• The AS wingers clause gives the new column the name wingers.
• This means that the wingers column will contain the sum of the values of the rw and lw columns. This is useful because it allows us to rank the players by their overall attacking ability, regardless of whether they are primarily right wingers, left wingers, or both.

(Qr8) Best Forwarder (Striker):

In the following code, these atrribute listed below will be use to rank the best forwarders;


shooting, attacking_finishing, attacking_heading_accuracy, skill_dribbling, power_shot_power.

The most important attribute for a striker is shooting. This is because the main job of a striker is to score goals, and the shooting attribute determines how likely they are to do so. A good striker will have a high shooting attribute, and they will be able to score goals from a variety of different positions.

The other attributes listed are also important, but they are not as important as shooting. For example, a striker with a high attacking finishing attribute may be able to score goals from close range, but if they do not have a good shooting attribute, they will not be able to score goals from other positions.


Ultimately, the best way to judge a good striker is to watch them play. However, if you are only able to look at the attributes, then the shooting attribute is the most important one to consider.

SELECT COUNT(*) FROM players WHERE shooting IS NULL;

count
2132

UPDATE players SET shooting = 0 WHERE shooting IS NULL;

UPDATE 2132

SELECT short_name, shooting, attacking_finishing, power_shot_power,
       attacking_heading_accuracy, skill_dribbling, club_name
       FROM players  ORDER BY shooting DESC LIMIT 10;

short_name	shooting	attacking_finishing	power_shot_power	attacking_heading_accuracy	skill_dribbling	club_name
Cristiano Ronaldo	94	95	94	90	88	Manchester United
L. Messi	92	95	86	70	96	Paris Saint-Germain
R. Lewandowski	92	95	90	90	85	FC Bayern M├╝nchen
E. Haaland	91	94	94	69	78	Borussia Dortmund
H. Kane	91	94	91	86	83	Tottenham Hotspur
L. Suárez	90	93	89	84	83	Atlético de Madrid
S. Ag├╝ero	89	93	90	78	86	FC Barcelona
K. Mbappé	88	93	86	72	93	Paris Saint-Germain
H. Son	87	88	88	68	87	Tottenham Hotspur
M. Salah	87	91	82	59	90	Liverpool

(10 rows)

(Qr9) Best Midfielder:

In the following code, these atrribute listed below will be use to rank the best Midfielders;


mentality_vision, movement_balance, attacking_crossing, mentality_interceptions, defending_marking_awareness

The most important attribute to judge a midfielder is mentality_vision. This is because midfielders need to be able to see the game ahead of them and make good decisions, both offensively and defensively. A midfielder with a high mentality_vision attribute will be able to see where their teammates are and where the space is, and they will be able to make passes and crosses that create scoring opportunities.

The other attributes listed are also important, but they are not as important as mentality_vision. For example, a midfielder with a high attacking_crossing attribute may be able to make good crosses, but if they do not have a good mentality_vision attribute, they will not be able to see where their teammates are and they will not be able to make the right passes.


Ultimately, the best way to judge a midfielder is to watch them play. However, if you are only able to look at the attributes, then the mentality_vision attribute is the most important one to consider.

SELECT short_name, mentality_vision, movement_balance, attacking_crossing,
       mentality_interceptions, defending_marking_awareness, club_name
       FROM players  ORDER BY mentality_vision DESC LIMIT 10;

short_name	mentality_vision	movement_balance	attacking_crossing	mentality_interceptions	defending_marking_awareness	club_name
L. Messi	95	95	85	40	20	Paris Saint-Germain
K. De Bruyne	94	78	94	66	68	Manchester City
Luis Alberto	92	83	69	59	58	Lazio
P. Dybala	91	94	82	42	32	Juventus
T. Kroos	90	71	88	80	71	Real Madrid CF
Bruno Fernandes	90	79	87	66	72	Manchester United
Neymar Jr	90	84	85	37	35	Paris Saint-Germain
L. Modri─ç	90	92	86	80	70	Real Madrid CF
David Silva	90	89	83	50	58	Real Sociedad
Iniesta	90	76	75	59	68	Vissel Kobe

(10 rows)

(Qr10) Best Defender:

In the following code, these atrribute listed below will be use to rank the best Defenders;


movement_balance, mentality_aggression, mentality_interceptions, defending_marking_awareness, defending_sliding_tackle, defending_standing_tackle

The most important attribute to judge a defender is defending_marking_awareness. This is because defenders need to be able to mark their opponents and prevent them from scoring. A defender with a high defending_marking_awareness attribute will be able to stay close to their opponents and prevent them from getting into good scoring positions.

The other attributes listed above are also important, but they are not as important as defending_marking_awareness. For example, a defender with a high mentality_aggression attribute may be able to put in hard tackles, but if they do not have a good defending_marking_awareness attribute, they will not be able to mark their opponents effectively.

SELECT short_name, defending_marking_awareness, defending_sliding_tackle, defending_standing_tackle,
       mentality_interceptions, mentality_aggression, club_name
       FROM players
       ORDER BY defending_marking_awareness DESC LIMIT 10;

short_name	defending_marking_awareness	defending_sliding_tackle	defending_standing_tackle	mentality_interceptions	mentality_aggression	club_name
G. Chiellini	93	88	89	89	88	Juventus
V. van Dijk	92	86	92	90	83	Liverpool
R├║ben Dias	90	85	89	85	92	Manchester City
M. Škriniar	90	82	88	86	84	Inter
K. Koulibaly	90	86	88	85	83	Napoli
S. Savić	90	83	86	87	86	Atlético de Madrid
N. Kanté	90	86	93	91	93	Chelsea
M. Hummels	90	86	90	89	71	Borussia Dortmund
S. de Vrij	89	85	87	87	77	Inter
Marquinhos	89	89	89	88	81	Paris Saint-Germain

(10 rows)

(Qr11) Best Goalkeeper:

In the following code, these atrribute listed below will be use to rank the best Goal Keepers;


goalkeeping_reflexes, goalkeeping_handling, goalkeeping_diving, goalkeeping_kicking

The most important attribute to judge a goalkeeper is goalkeeping_reflexes. This is because goalkeepers need to be able to react quickly to save shots. A goalkeeper with a high goalkeeping_reflexes attribute will be able to make saves that most other goalkeepers would miss.

The other attributes listed are also important, but they are not as important as goalkeeping_reflexes. For example, a goalkeeper with a high goalkeeping_handling attribute may be able to catch and hold onto the ball well, but if they do not have good reflexes, they will not be able to save shots that are hit with a lot of power.

SELECT short_name, goalkeeping_reflexes, goalkeeping_handling, goalkeeping_diving,
       goalkeeping_kicking,height_cm, club_name
    FROM players
    ORDER BY goalkeeping_reflexes DESC LIMIT 10;

short_name	goalkeeping_reflexes	goalkeeping_handling	goalkeeping_diving	goalkeeping_kicking	height_cm	club_name
M. ter Stegen	90	85	88	88	187	FC Barcelona
H. Lloris	90	83	88	65	188	Tottenham Hotspur
G. Donnarumma	90	83	91	79	196	Paris Saint-Germain
K. Schmeichel	90	78	84	80	189	Leicester City
J. Oblak	90	92	87	78	188	Atlético de Madrid
Alisson	89	86	86	84	191	Liverpool
K. Navas	89	84	89	75	185	Paris Saint-Germain
M. Neuer	88	88	88	91	193	FC Bayern M├╝nchen
Ederson	88	82	87	93	188	Manchester City
T. Courtois	88	89	84	74	199	Real Madrid CF

(10 rows)

(Qr12) Most Paid player(Wage in eur):

Player wages are the salaries that players receive from their clubs. They are typically paid weekly or monthly, and they can vary depending on a number of factors, including the player's age, experience, ability, and the club's financial situation.

Player wages are an important part of the economics of professional football. They can be a significant financial burden for clubs, but they are also necessary to attract and retain the best players.

Player wages can also be affected by other factors, such as the player's contract length, the player's image rights, and the player's performance.

In recent years, player wages have been rising steadily. This is due to a number of factors, including the increasing commercialization of football and the increasing competition for talent.


As a result, player wages have become a major issue for clubs. Some clubs are struggling to afford the wages of their players, and this is leading to financial problems.

SELECT COUNT(*) FROM players WHERE wage_eur IS NULL;

count
61

UPDATE players SET wage_eur = 0 WHERE wage_eur IS NULL;

UPDATE 61

SELECT short_name, wage_eur, value_eur, overall, club_position,nationality_name, club_name
    FROM players
    ORDER BY wage_eur DESC LIMIT 10;

short_name	wage_eur	value_eur	overall	club_position	nationality_name	club_name
K. De Bruyne	350000	125500000	91	RCM	Belgium	Manchester City
K. Benzema	350000	66000000	89	CF	France	Real Madrid CF
L. Messi	320000	78000000	93	RW	Argentina	Paris Saint-Germain
Casemiro	310000	88000000	89	CDM	Brazil	Real Madrid CF
T. Kroos	310000	75000000	88	LCM	Germany	Real Madrid CF
R. Sterling	290000	107500000	88	SUB	England	Manchester City
R. Lewandowski	270000	119500000	92	ST	Poland	FC Bayern M├╝nchen
Cristiano Ronaldo	270000	45000000	91	ST	Portugal	Manchester United
M. Salah	270000	101000000	89	RW	Egypt	Liverpool
Neymar Jr	270000	129000000	91	LW	Brazil	Paris Saint-Germain

(10 rows)

(Qr13) Top Rated Player player(Overall):

The overall rating of a player is a number that represents their overall ability in FIFA. It is calculated based on a number of factors, including their attributes, their position, and their form.

The overall rating of a player can range from 1 to 99, with 99 being the highest possible rating. Players with a high overall rating are considered to be the best players in the world.

The overall rating of a player is a dynamic measure, which means that it can change over time. The player's attributes can improve as they get more experience. Their form can also improve or decline, as a result, the overall rating of a player is a good way to track their progress and to see how they compare to other players.

SELECT short_name, overall,age,club_position,club_name
    FROM players
    ORDER BY overall DESC LIMIT 10;

short_name	overall	age	club_position	club_name
L. Messi	93	34	RW	Paris Saint-Germain
R. Lewandowski	92	32	ST	FC Bayern M├╝nchen
Cristiano Ronaldo	91	36	ST	Manchester United
K. De Bruyne	91	30	RCM	Manchester City
J. Oblak	91	28	GK	Atlético de Madrid
K. Mbappé	91	22	ST	Paris Saint-Germain
Neymar Jr	91	29	LW	Paris Saint-Germain
H. Kane	90	27	ST	Tottenham Hotspur
N. Kanté	90	30	RCM	Chelsea
M. Neuer	90	35	GK	FC Bayern M├╝nchen

(10 rows)

(Qr14) Preferred Foot of a Player:

A player's preferred foot is the foot that they use most often to kick the ball. This can have a significant impact on their playing style and their ability to perform certain skills.

For example, a player who is right-footed will typically be better at crossing the ball with their right foot and shooting with their right foot. They may also be better at dribbling with their right foot and passing with their right foot.

On the other hand, a player who is left-footed will typically be better at crossing the ball with their left foot and shooting with their left foot. They may also be better at dribbling with their left foot and passing with their left foot.

Of course, there are some players who are ambidextrous and can use both feet equally well. However, most players have a preferred foot that they use more often.


The preferred foot of a player can be determined by a number of factors, including their genetics, their training, and their playing style.

Finally, a player's playing style can also affect their preferred foot. For example, a player who is a striker will typically need to be good at shooting with both feet, as they will need to be able to score goals from both sides of the goal. However, a player who is a defender may only need to be good at using one foot, as they will typically only need to defend from one side of the field.

SELECT DISTINCT preferred_foot, COUNT(*) AS total
    FROM players
    GROUP BY preferred_foot
    ORDER BY total DESC;

preferred_foot	total
Right	14674
Left	4565

(2 rows)

(Qr15) What is the player age distribution?:

Below, we could note that the player age distribution in soccer is typically bimodal, with a peak in the early 20s and another peak in the late 30s. This is because players tend to reach their peak physical and technical abilities in their early 20s, but they can also maintain a high level of performance into their late 30s if they stay fit and healthy.

There are a few reasons for the bimodal distribution. First, young players are often more physically gifted than older players, and they have more energy. This allows them to run more, tackle harder, and jump higher. Second, young players are often more technically gifted than older players, and they have better ball control and passing skills. This allows them to create more chances and score more goals.

However, young players often lack experience and decision-making skills. This can lead to mistakes, and it can also make them more susceptible to injuries. As players get older, they gain experience and decision-making skills. This allows them to make better decisions on the field, and it also helps them to avoid injuries.

The age distribution of players also varies depending on the position. For example, goalkeepers tend to be older than other players, because they need to have a high level of experience and decision-making skills. Defenders also tend to be older than other players, because they need to be physically strong and have a good understanding of the game.

SELECT DISTINCT age, COUNT(*) total
       FROM players GROUP BY age ORDER BY total DESC;

age	total
21	1547
22	1446
24	1442
25	1394
23	1387
20	1377
27	1200
26	1197
29	1178
28	1129
19	1099
30	901
31	825
18	733
32	634
33	468
34	354
17	271
35	258
36	146
37	105
38	62
39	39
16	20
40	14
41	7
43	3
42	2
54	1

(29 rows)

BONUS

(Qr16) What is the current player age(Now):

ALTER TABLE players ALTER COLUMN dob TYPE DATE USING dob::DATE;

ALTER TABLE

SELECT EXTRACT(YEAR FROM AGE(NOW(), dob)) age_now FROM players LIMIT 6;

age_now
36
34
38
31
32
24

(6 rows)

ALTER TABLE players ADD age_now INT;

ALTER TABLE

UPDATE players SET age_now = EXTRACT(YEAR FROM AGE(NOW(), dob))

UPDATE 19239

SELECT short_name, age AS age_2022, age_now
    FROM players
    ORDER BY overall DESC LIMIT 8;

short_name	age_2022	age_now
L. Messi	34	36
R. Lewandowski	32	34
J. Oblak	28	30
K. De Bruyne	30	32
Neymar Jr	29	31
Cristiano Ronaldo	36	38
K. Mbappé	22	24
H. Kane	27	30

(8 rows)

HIGHLIGHTS AND CONCLUSION

In this EDA of FIFA data set, we have explored a variety of features, including player age, overall rating, preferred foot, wage, and best position. We have found that there are a number of interesting trends and relationships within the data. For example, we have found that:

• Player age distribution of players is bimodal, with a peak in early 20s and another in late 30s.
• The best players playing in their role/position often have high technical abilities/qualities and can effect their team overall performance.
• The High overall rating of players of result from their form/performance and their atrribute, it can also be from how they effect their team to play better. This is a good way of tracking a player progress.
• lastly the wage of players are skyrocketing by year, this as cause major issue for football clubs. Some clubs are struggling to afford the wages of their players, and this is leading to financial problems.

Through this EDA, we have learned a number of things about Exporatory Data Analysis in SQL. For example, we have learned how to:

• Use SQL queries to explore data sets.
• Identify trends and relationships within data sets.
• Test hypotheses about data sets.
• We have also learned that EDA is a powerful tool that can be used to gain insights into data sets. By exploring data sets, we can identify trends and relationships that would not be obvious from simply looking at the data. This information can be used to make better decisions about the data, or to build better models.

The next step in this project would be to conduct more in-depth analysis of the data. This could involve using statistical tests to test our hypotheses, or building machine learning models to predict player performance.

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