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Goal: Estimate the total one-way group-stage air travel burden for each national team in the 2026 World Cup.

Introduction

This project combines training-site geocoding with airport proximity mapping to estimate how far each team is likely to travel by air during the group stage. The main metric is a one-way distance total from each team's training site to each match venue. The result is a static, GitHub Pages-friendly portfolio site with a precomputed JSON dataset and an interactive chart.

Data Sources

• Group stage schedule exported from Google Sheets
• Team origin data with training-site locations exported from Google Sheets
• Airport reference data used for nearest-airport matching

Methodology

1. Load the CSV exports with Python and pandas.
2. Geocode each unique training site with geopy and Nominatim.
3. Find the nearest valid commercial airport with airports-py.
4. Resolve each venue to a destination airport with a city-level fallback.
5. Calculate airport-to-airport distance in kilometers and sum the three one-way group-stage flights for each team.

Outputs

• Dashboard
• Executive Summary
• Python script

Metric Note

Kilometers are the canonical unit in the analysis. The dashboard also shows a flight time equivalent based on the same one-way distance total, using 900 km/h as the cruise-speed assumption. That time is a convenience for readers, not a separate routing model.

Packages Used

pandas, geopy, airports-py, Chart.js,