AIrsenal

Machine learning Fantasy Premier League team

Install

It is recommended that you use a conda or virtualenv environment to install and run AIrsenal.
The Stan model used to predict team and player scores is in the package https://github.com/anguswilliams91/bpl, and to run this you will need a working (recent) C++ compiler. An example setup could be:

conda create -n airsenalenv python=3.7 pystan=2.18.0.0
conda activate airsenalenv
conda install -c psi4 gcc-5
pip install https://github.com/anguswilliams91/bpl/archive/master.zip
git clone https://github.com/alan-turing-institute/AIrsenal.git
cd AIrsenal
pip install .

Getting started

Once you've installed the module, you will need to set three environment variables (or alternatively you can put the values into files in the airsenal/data/ directory, e.g. airsenal/data/FPL_TEAM_ID:

1. FD_API_KEY: an API key for football data
2. FPL_TEAM_ID: the team ID for your FPL side.
3. FPL_LEAGUE_ID: a league ID for FPL (this is only required for a small subset of functionality).

Once this is done, run the following command

setup_airsenal_database

You should get a file /tmp/data.db. This will fill the database with all that is needed up to the present day.

Updating

TODO: update this once entry points are finished.

To stay up to date in the future, you will need to fill three tables: match, player_score, and transaction with more recent data, using a couple of scripts as detailed below.

To fill the match results, we will query the football-data.org API. You need an API key for this (sign up at https://www.football-data.org/ ) - put it into a file data/FD_API_KEY Then run (from scripts)

python fill_match_table.py --input_type api --gw_start <first_gameweek> --gw_end <last_gameweek+1>

Once the match data is there, you can fill the player score data by running (also from scripts)

python python fill_playerscore_this_season.py --gw_start <first_gameweek> --gw_end <last_gameweek+1>

The transaction table is a bit different, as this reflects the players we are buying and selling in our own team. As such, you will need to edit scripts/fill_transaction_table.py yourself to fill in the player_ids of players transferred in or out, and then run the script with python fill_transaction_table.py.