This is a repository that scraps the https://understat.com/league/EPL to get information about team and player data for the english premier league.

The code is set up in a way to instantiate two airflow dags.

• The first does the scraping part
• The second transforms the tables into tables to extract meaningful insights.

Finally the notebook visualizations.ipynb access those transformed tables to show ideal players to be selected in the fantasy premier league game, based on the player form, the recent team performance and the schedule coming ahead.

To setup the dependences (including airflow), one needs to go in the terminal to the folder and then run:

python -m venv .
source bin/activate
python -m pip install --upgrade pip

export AIRFLOW_HOME=`pwd`
export AIRFLOW_VERSION=2.7.0
PYTHON_VERSION="$(python --version | cut -d " " -f 2 | cut -d "." -f 1-2)"

CONSTRAINT_URL="https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt"

pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"
pip install -r requirements.txt

The airflow instance assumes that there is a postgres databased called fantasy. That it will use to store raw and transformed data.

Then to initiate the airflow instance from a new terminal run:

source bin/activate
export AIRFLOW_HOME=`pwd`
export $(grep -v '^#' .env | xargs)
source .airflow_env
airflow standalone

Please note that there needs to be a .env file with the database credentials as in the env.example file.

Now airflow should be running at http://localhost:8080/, where you can start the dag undestat_scraping and once the dag finishes start the transformation_layer dag to fully populate the tables needed.

Then to get some visualization and insights, please start a new terminal and run

source bin/activate
export $(grep -v '^#' .env | xargs)
jupyter notebook

and then open the visualizations.ipynb file.