Analyze European soccer datasets using advanced SQL techniques to uncover meaningful insights. This project demonstrates data aggregation, window functions, and problem-solving skills applied to sports analytics.
This project uses a 3-layer approach to SQL problem-solving:
1️⃣ Clarify the Goal – Determine exactly what output is needed. 2️⃣ Identify Relationships – Decide aggregation level and comparison strategy. 3️⃣ Build Step-by-Step – Start with basic queries, then add metrics, groupings, and filters gradually.
This structured approach ensures clarity and efficiency in writing complex SQL queries.
- Open
european_soccer_sql_analysis.sqlin your SQL editor (Snowflake, PostgreSQL, MySQL, etc.) - Execute queries step-by-step following the layered thinking approach.
- Modify queries to explore other insights or add new metrics.
- SQL Aggregations (
SUM,AVG,COUNT) - Grouping & Filtering (
GROUP BY,HAVING) - Subqueries & CTEs
- Window Functions (
ROW_NUMBER(),RANK()) - Analytical problem-solving & data interpretation
Here are some example insights you can generate using this project:
- Highest scoring match per country per season
- Teams outperforming seasonal averages
- Top players based on goals/assists
- League-wide scoring trends over time
These insights are easily extendable for deeper analysis in soccer analytics.
This repository is ideal for showcasing:
- SQL expertise
- Analytical thinking
- Portfolio-ready data projects