Football Models and Visualisations

This repository is a personal project where I develop football models and visualisations.

It's build on top of different technologies such as:

Python: for data processing and machine learning (Tensorflow, Scikit-Learn and MLFlow).
R: all data-visualisation stuff (dplyr, ggplot2, magick).
Postgres SQL: storing and querying data easily.
Docker: to orchestrate all these elements together and easy install/startup.
Google Cloud Plateform: to run heavy jobs in cloud.

Samples & Results

Architecture

The project contains five folders:

./app : maybe deprecated design, mostly for data integration and easy of use.
./data: where raw data are stored (in addition to the database). Not in git 😉.
./model: machine learning models (expected goal for example).
./src: source file for crawlers, database connection/ingestion, SQL queries, etc...
./visualisation: source code for data-visualization, most recent works (on maps) are in ./visualisation/maps subfolders.

Usage

Running model applications

Start corresponding container: docker-compose up -d model.
For ease of use, going into the container: docker exec -it model bash
All data are mapped to the local environment: cd /data

Expected goal model: python -m model.expected_goal.main --help.
Expected assist model: python -m model.expected_assist.main --help.
Possession2Vec model: python -m model.pass2vec.main --help

Running Passmaps vizualisations

Start corresponding container: docker-compose up -d passmap.
Go to http://localhost:8082/.

TODO

Improve models
Add more documentations.
Clean some viz stuff.
Better Docker management.

Contacts

Any questions/improves on Twitter @Ben8t.