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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?