Skip to content

This repository uses Python for analyzing football player data, focusing on various aspects such as player positions, league distributions, wages, and the relationship between player age and appearances. It includes visualizations generated using Plotly to provide insights into the dynamics of football player demographics and performance.

Notifications You must be signed in to change notification settings

NishanthMuruganantham/football-player-wages-eda

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

23 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Football Player ED Analysis

This repository contains analysis and visualizations of various aspects of football player data. The analysis covers player positions, league distributions, wages, and the relationship between these attributes. Below is an overview of the insights gained from the analysis:

Insights:

1. Positional Analysis:

  • Pie Chart: The distribution of players across positions reveals that defenders constitute the largest proportion, followed by midfielders, forwards, and goalkeepers. This aligns with the tactical priorities of teams in prioritizing defensive solidity and offensive creativity.

2. League Distribution:

  • Sunburst Chart: The distribution of players across different leagues highlights the dominance of the English Premier League, followed by Primeira Liga, La Liga, and Serie A. The chart also emphasizes the consistent representation of player positions across leagues.

3. Wage Analysis:

  • Histogram: Average wages by player position across different leagues demonstrate that forwards and midfielders command the highest wages, followed by defenders. Goalkeepers generally receive lower average wages, reflecting the specialized nature of their role.

4. Player Age and Appearances:

  • Relationship Analysis: The relationship between player age and appearances reveals distinct patterns for different positions. Goalkeepers tend to have fewer appearances in their younger years but increase in frequency as they age, while defenders exhibit a surge in appearances during their youth. Forwards and midfielders maintain a relatively steady trajectory in appearance frequency throughout their careers.

Dataset Source:

Tools Used:

  • Python
  • Plotly for interactive visualizations

About

This repository uses Python for analyzing football player data, focusing on various aspects such as player positions, league distributions, wages, and the relationship between player age and appearances. It includes visualizations generated using Plotly to provide insights into the dynamics of football player demographics and performance.

Topics

Resources

Stars

Watchers

Forks