⚽ Football Player Data Analysis — FIFA World Cup 2026

📌 Project Overview

This project focuses on analyzing FIFA World Cup 2026 player statistics using Python and Exploratory Data Analysis (EDA).

The objective is to explore player performance, team-level statistics, age distribution, goals, assists, playing time, and relationships between different performance metrics.

🎯 Objectives

• Perform data cleaning and preprocessing
• Explore player and team-level statistics
• Analyze goals, assists, and playing time
• Compare player statistics across national teams and positions
• Identify meaningful patterns and relationships
• Generate data-driven insights from football statistics

📂 Dataset

The dataset contains football player statistics associated with the 2026 FIFA World Cup.

Key variables analyzed include:

• Player name
• National team
• Age
• Position
• Goals
• Assists
• Minutes played
• Shots
• Other player performance statistics

🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

🔍 Analysis Performed

1. Data Cleaning

• Checked missing values
• Identified duplicate records
• Reviewed data types
• Standardized data where required
• Prepared the dataset for analysis

2. GroupBy & Aggregation

Analyzed:

• Number of players by national team
• Total goals by national team
• Average player age by national team
• Highest goal scorer from each national team
• Average playing time by player position
• Total assists by national team

3. Exploratory Data Analysis

Performed:

• Univariate analysis
• Bivariate analysis
• Multivariate analysis
• Distribution analysis
• Correlation analysis

4. Data Visualization

Created visualizations including:

• Histograms
• Scatter plots
• Pie charts
• Bar charts
• Correlation heatmaps

💡 Key Insights

1. Goals and Playing Time

Goals and minutes played showed a positive correlation of 0.34, indicating that players with more playing time tended to score more goals. However, the relationship was not strong enough to suggest that playing time alone determines goal-scoring performance.

2. Average Age

Panama had the highest average player age at approximately 30.08 years, while Ivory Coast had the lowest at approximately 25.50 years.

3. Highest Goal Scorer

Kylian Mbappé recorded the highest number of goals among the players identified as the top scorer for each national team, with 10 goals, followed by Lionel Messi with 8 goals..

4. Total Goals by National Team

France and England recorded the highest total number of goals with 20 goals each, followed by Argentina with 18 goals.

5. Total Assists by National Team

France recorded the highest total number of assists with 18, followed by England with 14 and Argentina with 12.

6. Playing Time by Position

Defenders recorded the highest average playing time at 184.23 minutes, followed by midfielders at 177.70 minutes. Goalkeepers had the lowest average at 129.38 minutes.

🏆 Final Findings

The analysis revealed noticeable differences in player performance, squad age, goal contributions, assists, and playing time across national teams and player positions.

The results demonstrate how exploratory data analysis can transform raw football statistics into meaningful insights about player and team performance.

🚀 Future Scope

This project can be further extended by:

• Building machine learning models for player performance prediction
• Developing advanced player ranking systems
• Performing team performance comparisons
• Creating interactive dashboards using Power BI or Tableau
• Applying predictive analytics to goals and assists
• Performing time-series analysis across multiple seasons

📓 Project Notebook

The complete analysis is available in the Jupyter Notebook:

players_dataset.ipynb

👨‍💻 Author

Ravichandran Selvam

Data Science & Artificial Intelligence Student