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.
We welcome contributions and comments - if you'd like to join the AIrsenal community please refer to our contribution guidelines
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. To test you have gcc installed in your system run the following command in a terminal:
gcc --version
If this successfully returns version information you can continue with the AIrsenal installation process. If not you will need to install gcc. Common ways to do this include:
- Mac OSX:
brew install gcc - Linux (Ubuntu):
apt-get install build-essential - Windows: We recommend using conda and following the windows-specific instructions below. Alternativley, have a look at MinGW to get a working compiler.
Alternatively, please refer to the Cython installation pre-requirements for options to get a working compiler on your system here: http://docs.cython.org/en/latest/src/quickstart/install.html.
We recommend running AIrsenal in a conda environment. For instructions on how to install conda go to this link: https://docs.anaconda.com/anaconda/install/
With conda installed, run these commands in a terminal to create a new conda environment and download and install AIrsenal:
Linux and Mac OS X:
conda create -n airsenalenv python=3.7 pystan
conda activate airsenalenv
git clone https://github.com/alan-turing-institute/AIrsenal.git
cd AIrsenal
pip install .
Windows:
If you already have gcc working on your system you can follow the Linux & Mac OS X instructions above. Otherwise try the steps below based on the pystan documentation:
conda create -n airsenalenv python=3.7
conda activate airsenalenv
conda install libpython m2w64-toolchain -c msys2
conda install numpy cython pystan -c conda-forge
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 some environment variables (or alternatively you can put the values into files in the airsenal/data/ directory, e.g. airsenal/data/FPL_TEAM_ID:
Required:
FPL_TEAM_ID: the team ID for your FPL side.
Optional:
-
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 thedata/directory.) -
FPL_LEAGUE_ID: a league ID for FPL (this is only required for plotting FPL league standings). -
FPL_LOGIN: your FPL login, usually email (this is only required to get FPL league standings). -
FPL_PASSWORD: your FPL password (this is only required to get FPL league standings).
Once this is done, run the following command:
setup_airsenal_databaseYou should get a file /tmp/data.db. This will fill the database with all that is needed up to the present day.
You can run sanity checks on the data using the following command:
check_airsenal_data
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_databaseThe 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 3This 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).