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Football Match Data Scraper and Analysis

This Python script enables you to scrape and analyze football match data. It's designed to be executed from the command line and offers a range of options for data scraping, analysis, and visualization. The script empowers you to access a wide variety of football match data and analyze it in different ways. Additionally, you can use the script to create visualizations that help you gain insights from the data.

Getting Started

Prerequisites

Before running the script, make sure you have installed the following packages:

  • pandas
  • rich
  • matplotlib
  • mysql-connector-python
  • selenium
  • beautifulsoup4

You will also need the Chrome browser and the corresponding ChromeDriver.

Installation

  • Install Python 3.6 or higher from the official website.
  • Install the required Python modules using pip:
pip install pandas rich matplotlib mysql-connector-python selenium beautifulsoup4

Virtualenv

It is recommended to use a virtual environment for running the Match Data Scraper. If you do not have a virtual environment set up, follow these steps:

  • Install virtualenv by running the following command:
pip install virtualenv
  • Create a new virtual environment by running the command:
virtualenv venv
  • Activate the virtual environment by running the command:

On Linux/Mac:

source venv/bin/activate

On Windows:

./venv/Scripts/activate
  • Install the required dependencies using pip:
pip install -r requirements.txt

Match Data Scraper

This Python script uses Selenium and BeautifulSoup to scrape match data from a website and save it to a CSV file or a MySQL database. It also includes an option to send an email notification once the data has been scraped.

Usage

To use the Match Data Scraper script:

  • Open the script in your Python editor of choice.
  • Modify the script as needed (e.g. change the database credentials, email settings, etc.).
  • Run the script using the command:
python footscraping.py [options]

Replace [options] with any of the following:

  • -c or --country: Specify the name of the country to scrape data for (default: "England").
  • -o or --output: Specify the name of the output file (default: "matches.csv").
  • -s or --save-to-db: Save the data to a MySQL database (default: False).
  • -e or --send-email: Send an email notification once the data has been scraped (default: False).

For example:

python footscraping.py -c Spain -o spain_matches.csv -s -e

This command will scrape match data for Spain, save it to a MySQL database, and send an email notification.

Football Match Data Analysis

This Python script analyzes football match data. The script is designed to be run from the command line and provides various options for analyzing and visualizing the data.

Usage

To use the Football Match Data Analysis script:

  • Make sure you have scraped some match data using the Match Data Scraper script, or use the provided input_matches_file.csv file.
  • Modify the script as needed (e.g. change the plot type, email settings, etc.).
  • Run the script using the command:
python footanalysis.py [input_file][output_file][plot_type]

Replace [options] with the following arguments:

  • -i or --input_file: The path to the input CSV file (default: input_matches_file.csv).
  • -o or --output_file: The path to the output CSV file (default: output_matches_file.csv).
  • -p or --plot_type: The type of plot to display. Possible values are: all, percentage, and total_goals (default: all).
  • -s or --show_plot: If present, only shows the plot and does not perform any data analysis. for example:
python footanalysis.py -i input.csv -o output.csv -p percentage

This command will perform data analysis on the input.csv file, create a output.csv file containing the analyzed data, and display a bar chart showing the percentage of wins, draws, and losses for each team.

python footanalysis.py -i input.csv -o output.csv -p total_goals -s

This command will perform data analysis on the input.csv file, create a output.csv file containing the analyzed data, and display a line chart showing the total number of goals scored in each match.

Contributing

If you have any suggestions for improving the program or finding bugs, please submit an issue or pull request on the GitHub repository.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Adam Choi for providing the website used to scrape the data.