This work aims at conducting EDA and statisical tests regarding the players statistics as registered in the FIFA22 Database. The underlying dataset contains player attributes from FIFA 22 for Career Mode (as a CSV file); It allows for comprehensive comparisons of player development and changes over eight consecutive game versions.
- Physical attributes: Height, weight, pace, shooting, passing, dribbling, defending, physicality.
- Financial information: Player value, wage, contract length.
- Player history: Previous clubs, international appearances.
- Player role: Position, preferred foot, work rate.
- Analyzing player growth and potential
- Identifying undervalued or overvalued players
- Exploring correlations between player attributes and performance
- Building machine learning models to predict player performance
Install imported Python libraries and run notebook (.ipynb) or simply open and run in Google Colab (link inside notebook).
