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Machine learning Fantasy Premier League team
| Date | Stars |
|---|---|
| 2026-07-31 | 342 |
| 2026-08-04 | 344 |
| 2026-08-06 | 345 |
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# AIrsenal  *AIrsenal* is a package for using Machine learning to pick a Fantasy Premier League team. ## Background and News For some background information and details see https://www.turing.ac.uk/research/research-programmes/research-engineering/programme-articles/airsenal. ### AIrsenal Details for 2025/26 season We have made a mini-league **"Prem-AI League"** for players using this software. To join, login to the FPL website, and navigate to the page to join a league: https://fantasy.premierleague.com/leagues then click "Join a League". The code to join is: **xoz7vm**. Hope to see your AI team there!! :) Our own AIrsenal team's ID for the 2025/26 season is **[742663](https://fantasy.premierleague.com/entry/742663/history)**. ## Installation We recommend using [uv](https://docs.astral.sh/uv/) for managing Python versions and dependencies. For instructions on how to install uv, go to: https://docs.astral.sh/uv/getting-started/installation/. ### Installation from source [Recommended] #### Linux and macOS <details> **With uv (recommended):** ```shell git clone https://github.com/alan-turing-institute/AIrsenal.git cd AIrsenal uv sync ``` **With pip:** If not using `uv` you can replace `uv sync` with `pip install .` above, but we recommend you do so in a virtual environment, e.g. ```shell git clone https://github.com/alan-turing-institute/AIrsenal.git cd AIrsenal python -m venv .venv source .venv/bin/activate pip install . ``` </details> #### Windows <details> The best ways to run AIrsenal on Windows are either to use [Windows Subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/install) (WSL), which allows you to run AIrsenal in a Linux environment on your Windows system, or Docker (see below). You can then follow the installation instructions for Linux and macOS above. You're free to try installing and using AIrsenal in Windows itself, but so far we haven't got it working. The main difficulties are with installing [jax](https://github.com/google/jax#installation) and some database/pickling errors (e.g. #165). If you do get it working we'd love to hear from you! </details> #### Docker <details> Rather than building and running natively on your machine, you can instead use a Docker image if you prefer. Build the docker-image: ```console $ docker build -t airsenal . ``` Create a volume for data persistance: ```console $ docker volume create airsenal_data ``` Run commands with your configuration as environment variables, eg: ```console $ docker run -it --rm -v airsenal_data:/tmp/ -e "FPL_TEAM_ID=<your_id>" -e "AIRSENAL_HOME=/tmp" airsenal bash ``` or ```console $ docker run -it --rm -v airsenal_data:/tmp/ -e "FPL_TEAM_ID=<your_id>" -e "AIRSENAL_HOME=/tmp" airsenal airsenal_run_pipeline ``` `airsenal_run_pipeline` is the default command. </details> ### AIrsenal on PyPi [Work in Progress] <details> ⚠️ There are currently dependency issues with installing AIrsenal from PyPI (see #733), so it's not recommended. We're working on it. ⚠️ You can now do `pip install airsenal` in your Python virtual environment of choice, and it should work out-of-the-box, allowing you to run all the `airsenal_*` commands listed in the [Getting Started section](#getting-started). We will aim to keep the version on PyPi relatively up-to-date, but if you want the very latest developments, they will appear first in Github (on the `develop` branch if you're feeling brave, or `main` if you want a more stable version), which would require [building from source](#installation-from-source) </details> ### Optional dependencies <details> AIrsenal has optional dependencies for plotting, running notebooks, and an in development AIrsenal API. To install them run: - With uv: `uv sync --all-extras` - Without uv: `pip install ".[api,notebook,plot]"` </details> ## Running commands with uv If using AIrsena
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a72f8e6e1484baef, llm:Repository description and README: 'AIrsenal is a package for using Machine learning to pick a Fantasy Premier League team.' Topics include hacktoberfest; language Jupyter Notebook.
matched fp:a72f8e6e1484baef, llm:Repository description and README: 'AIrsenal is a package for using Machine learning to pick a Fantasy Premier League team.' Topics include hacktoberfest; language Jupyter Notebook.