https://github.com/nishanthmuruganantham/football-player-wages-eda

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.
https://github.com/nishanthmuruganantham/football-player-wages-eda

data-analysis data-science data-visualization eda football football-analytics football-data kaggle kaggle-dataset pandas plotly python

Last synced: about 2 months ago
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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.

• Host: GitHub
• URL: https://github.com/nishanthmuruganantham/football-player-wages-eda
• Owner: NishanthMuruganantham
• Created: 2024-01-30T14:08:36.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-02-07T14:06:59.000Z (12 months ago)
• Last Synced: 2024-02-14T11:27:40.882Z (11 months ago)
• Topics: data-analysis, data-science, data-visualization, eda, football, football-analytics, football-data, kaggle, kaggle-dataset, pandas, plotly, python
• Language: Jupyter Notebook
• Homepage:
• Size: 4.63 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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:

 - https://www.kaggle.com/datasets/ultimus/football-wages-prediction

## Tools Used:

- Python

- Plotly for interactive visualizations