football-data-analysis

A repository for exploratory analysis of European football league data using SQL in Python.

Dependencies

• Jupyter Notebook
• SQLite
• pandas
• NumPy
• SciPy
• Matplotlib
• seaborn

Data

I utilized the "European Soccer Database" from Kaggle. The database tracks fixture data from the 2008/09 season through to 2015/16, detailing over 25,000 matches and 11,000 players across 11 European top-flight leagues. There is also information about each of 296 teams, as well as player attributes originating from the FIFA video game series.

Methodology

• Things to consider:

  1. What does the data look like?
  2. What is interesting about this data?
  3. What can we learn from the data?

• Data cleaning, visualization and analysis

Questions Explored

1. Win/draw/loss percentage of each team, and goals scored and conceded home and away per season
2. Individual seasons with highest (and lowest) margin of victory by country; average margin of victory across 8 seasons for each country
3. Addressing the "eye test": Are left-footed players really more creative/technically gifted?