AIrsenal is a package for using Machine learning to pick a Fantasy Premier League team. For some background information and details see https://www.turing.ac.uk/research/research-programmes/research-engineering/programme-articles/airsenal.

It is recommended that you use a conda or virtualenv environment to install and run AIrsenal.
The Stan model used to predict match results 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 .

Once you've installed the module, you will need to set five 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 (this is only needed for filling past seasons results if not already present as a csv file in the data/ directory.)
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).
4. FPL_LOGIN: your FPL login, usually email (this is only required to get league standings)
5. FPL_PASSWORD: your FPL password (this is only required to get league standings)

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.

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 the command

update_airsenal_database

The next step is to use the team- and player-level Stan models to predict the expected points for all players for the next fixtures. This is done using the command

run_airsenal_predictions --weeks_ahead 3

(we normally look 3 weeks ahead, as this is an achievable horizon to run the optimization over, but also because things like form and injuries can change a lot in 3 weeks!)

Finally, we need to run the optimizer to pick the best transfer strategy over the next weeks (and hence the best team for the next week).

run_airsenal_optimization --weeks_ahead 3

This will take a while, but should eventually provide a printout of the optimal transfer strategy, in addition to the teamsheet for the next match (including who to make captain, and the order of the substitutes).