The goal of this notebook is to show how I collected data from the FIFA World Cup website for the 2018 World Cup Russia - Group Stage Prediction Project. Data was collected using a web crawler due to lack of API.
I collected groups statistics for all FIFA World Cups between 1994 and 2014.
That gives us 6 FIFA World Cups:
- 2014 FIFA World Cup Brazil™
- 2010 FIFA World Cup South Africa™
- 2006 FIFA World Cup Germany™
- 2002 FIFA World Cup Korea/Japan™
- 1998 FIFA World Cup France™
- 1994 FIFA World Cup USA™
The tournaments between 1998 and 2014 included 32 teams, divided into eight groups (A to H) - four teams (countries) per group. 1994 FIFA World Cup USA™ included 24 teams, divided into six groups (A to F) - four teams (countries) per group.
For each World Cup edition, I collected the following groups' statistics:
- Group - name of the group
- Teams - name of the team (country)
- Match played - how many matches did the team play
- Match won - how many matches it won
- Draw - number of draws
- Lost - number of loses
- Goals for - total number of goals
- Goals against - total number of lost goals
- Goals difference - difference between goals for and against
- Points - total points
I went through the process of building this web scraper in the Let the robot do your work! Web scraping with Python! article posted on Medium.
This data is publicaly accessible on the FIFA website in the statistics and records section. According to the robots.txt, data scraping from the statistics and records page is allowed [June 10, 2018].
Analysis has been performed in the Jupyter Notebook, using Python 3.x.
The following libraries were used:
To run this project:
- With python 3.x installed, create a virtual environment and activate it as shown:
virtualenv -p python3 my_virtualenv
source my_virtualenv/bin/activate- Clone this repository into your virtual environment:
git clone https://github.com/BarbaraStempien/DA--World-Cup-Data.git- Install project dependencies:
pip install -r DA--World-Cup-Data/requirements.txt- Open Jupter Notebook, and run the project or open
web_scraping.pyin your code editor.
I accept contributions. For details, check out CONTRIBUTING.md.
Copyright (c) 2018 Barbara Stempien
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