# Full Python script to reproduce figures import pandas as pd import seaborn as sns sns.boxplot(data=matches, x='decade', y='total_goals') plt.title("Goals per Match by Decade") plt.savefig("goals_decade.png")
We focus on three testable hypotheses:
Unlike flatter datasets, Fjelstul’s work contains over organized into 27 distinct datasets . This allows for complex relational queries that simple CSV files often cannot support without significant cleaning. Key Data Tables (CSV Format) jfjelstul worldcup data-csv
Originally hosted on GitHub and Kaggle , this project provides a multi-table structure that covers all and all 8 women’s tournaments (1991–2019) .
Looking for clean #WorldCup data? 🏆
You can download the raw CSV files or the full database through the following official channels:
matches = pd.read_csv("matches.csv") matches['year'] = pd.to_datetime(matches['date']).dt.year matches['decade'] = (matches['year'] // 10) * 10 matches['total_goals'] = matches['home_goals'] + matches['away_goals'] # Full Python script to reproduce figures import
Best for quick sharing.
The dataset is downloaded from the official repository or as CSV files: Looking for clean #WorldCup data
If you are working on a sports analytics project, data visualization, or just love football history, this is a goldmine. Joshua Fjelstul has compiled a comprehensive CSV dataset covering the FIFA World Cup from 1930 to the present.