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data set is comprised of 15000 soccer players with 108 columns from the Fifa website

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Data Description:

The data set is comprised of 15000 soccer players with 108 columns from the Fifa website. 

Data Preparation

In order to clean the data and reduce the number of null values, I divided the data into 2 big groups: the field players(field_clean.csv) and the goalkeepers(gk_clean.csv). Also, the data set had a lot of columns with variables values; In other words the baseline for a player speed could be 80 + 3 for the adjusted monthly performance. For this data set I kept it to the baseline performance and removed the adjusted performance.

Data Analysis

For the data analysis, I narrowed down the 108 different columns to 16 essential ones and I attempted to determine how much each of the columns where correlated to the overall player rating.

Columns:

age

The player’s age

Height_cm

The player’s height in cm

 

Overall

The player’s overall rating

Weight_kg

The player’s weight in kg

Potential

The player Potential

Value_eur

The player value in euro

Wage_eur

The player’s salary

International_reputation

The player’s international performance

Weak_foot

The player’s weak foot ability

Skill_moves

The player’s skill move ability

Pace

The player’s pave

Shooting

The player’s shooting

Passing

The player’s passing

Dribbling

The player’s dribbling

Defending

The player’s defending ability

Physic

The player’s physical rating

 

Results:

I grouped the results into 4 files: field_corr.csv, attack_corr.csv, defense_corr.csv, midfield_corr.csv. They each have 2 columns with the same headers: player_attr(the player attribute) and corr_to_over(correlation to overall)

 Observation and insights:

 

What we observe after running the correlation on every field player is that there is not a strong correlation between most of the attributes except for potential, value and salary. In other words, there isn’t a standard definition of an excellent player.

After we split up the correlation by positions (offense, midfield, defense) the picture becomes clearer.

We notice that passing is very important in the midfield with a correlation of over 88%, dribbling and shooting are the most important skills for attacker, and physicality is vital for every defender.

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