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FIFA'21 Exploratory Data Analysis

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Introduction

The project is for people who love data science and have grown up playing football and are FIFA enthusiasts. The data is scraped from the website https://sofifa.com by extracting the Player personal data, followed by Player IDs and their playing and style statistics.

Insights and correlations between player value, wage, age, special attributes, and performance can be derived from the dataset. This uninterpreted data can be converted into information by analysing it. We have derived summary statistics for teams, clubs, & players. Through extensive football experience: the insights provided in our results, alongwith understanding, and contextualized information enables users to act smartly when playing FIFA, picking a better team for say Fantasy Premier league, or increase their betting odds.

Data Description

The fifa21.csv data was scrapped from the sofifa website using a python crawling script by Aman Shrivastava. The website contains the data from the EA Sports' game FIFA and gets updated regularly with the release of new versions of the game. Data developed by Electronic Arts for the latest edition of their FIFA game franchise. Through several research projects done on soccer analytics, it has been established in the field of academia that the use of data from the FIFA franchise has several merits that traditional datasets based on historical data do not offer. Since 1995 the FIFA Soccer games provide an extensive and coherent scout of players worldwide.

For each attribute,the data has an integer from 0 to 100 that measures how good a player is at that attribute. Examples of attributes are: dribbling, aggression, vision, marking and ball control. Observe that it seems to be unfeasible to accurately characterize players in these attributes automatically. Thus, all of those are gathered and curated by the company whose job is to bring the gameplay closer to reality as possible, hence preserving coherence and representativeness across the dataset.

The FIFA 21 dataset that has been used for this analysis provides statistics of about 18500 players on over 90 different attributes. These attributes are optimal indicators to determine the performance of a player at a particular playing position.

Explorations Achieved using the data :-

  • Clustering players by Nationality
  • Value & Wages of players with age
  • Value of players with position
  • Overall & Potential rating with age
  • Players preffered playing positions
  • Top 20 players key metrics
  • Variation in wages for top clubs
  • Comparing the performance of Left-footed and Right-footed footballers
  • Height variation of Players

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally

  1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

  2. Create a Conda environment and install the required libraries by running these commands on the terminal:

  • conda create -n zerotopandas -y python=3.8
  • conda activate zerotopandas
  • pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade
  1. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:
  • jovian clone notebook-owner/notebook-id
  1. Enter the newly created directory using cd directory-name and start the Jupyter notebook.
  • jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.