Türkiye EURO 2020 Analysis

A comprehensive data analysis of Turkey's performance in EURO 2020.

PythonPandasMatplotlibSeabornJupyter Notebook
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Türkiye EURO 2020 Analysis

This project provides a comprehensive statistical analysis of Turkey's national football team performance during the UEFA EURO 2020 tournament. Through data visualization and statistical methods, it examines what went wrong and what lessons can be learned for future tournaments.

Project Motivation

Turkey's disappointing performance at EURO 2020, finishing bottom of their group with three losses, raised many questions. This analysis aims to provide data-driven insights into the team's performance, going beyond simple match results to understand underlying patterns.

Analysis Components

Performance Metrics

  • Possession Statistics: Analyzing ball control and territorial advantage
  • Shot Accuracy: Expected goals (xG) vs actual goals scored
  • Defensive Performance: Tackles, interceptions, and goals conceded analysis
  • Passing Networks: Visualizing team cohesion and play patterns
  • Player Heatmaps: Individual player positioning and movement

Key Findings

The analysis revealed several critical issues:

  • Low shot conversion rate compared to tournament average
  • Defensive vulnerabilities during set pieces
  • Midfield possession without creating clear chances
  • Lack of coordination in attacking transitions

Comparative Analysis

Turkey's statistics were compared against:

  • Group stage opponents (Italy, Wales, Switzerland)
  • Tournament average across all teams
  • Turkey's performance in previous tournaments

Visualization Techniques

Using Matplotlib and Seaborn, the project creates compelling visualizations including:

  • Interactive match timelines showing key events
  • Player performance radar charts
  • Team formation analysis visualizations
  • Shot maps showing shooting patterns

Technical Tools

The analysis is conducted in Jupyter Notebooks, making it reproducible and educational. Pandas handles data manipulation, while Matplotlib and Seaborn create publication-quality visualizations. Data is sourced from official UEFA statistics and specialized football analytics APIs.

Insights and Conclusions

The analysis provides concrete evidence for tactical and strategic improvements needed for future tournaments, making it valuable for coaches, analysts, and football enthusiasts interested in understanding the beautiful game through data.