FPL Optimization Tools

This repository provides a set of tools for solving deterministic Fantasy Premier League (FPL) optimization problems. The Python code uses pandas for data management, sasoptpy for building the optimization model, and HiGHS via highspy to solve the model.

It allows users to:

• Automatically select the best FPL squad based on the given projection data and solver settings.
• Customize squad constraints, formation rules, transfer strategies, and more.
• Modify data sources and parameters to suit personal models or preferences.

🔧 Installation

1. Install uv

uv handles both Python installation and dependency management, so you do not need to install Python separately.

Windows (PowerShell)

Open PowerShell and run:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

macOS / Linux

wget -qO- https://astral.sh/uv/install.sh | sh

Restart your terminal after installation, then verify:

uv --version

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2. Install Git

Windows

Download from git-scm.com and accept all default installation options.

macOS

Git is usually pre-installed. If not, run:

brew install git

3. Clone the Repository

Open a terminal (search for Command Prompt in Windows) and run:

cd Documents
git clone https://github.com/solioanalytics/open-fpl-solver.git
cd open-fpl-solver

4. Install Dependencies (and Python)

uv sync

🚀 Running the Optimizer

1. Add Projection Data

Place your projections file (e.g., solio.csv) in the data/ folder.

2. Configure Data Source

If you are not using the default data source, update the datasource field in data/user_settings.json to match your CSV file name.

Example: if you are using a file named projections.csv, the settings file should contain:

"datasource": "projections"

3. Edit Settings

Edit any desired settings in comprehensive_settings.json or user_settings.json.

• The majority of useful settings for most people will be in user_settings.json.
• comprehensive_settings.json provides a wider range of options that will be used as defaults unless altered in user_settings.json.

Details of what each setting does can be found in the .md file in the /data/ folder.

4. Run the Solver

cd run
uv run python solve.py

🎥 Videos

There is a YouTube playlist here by Sertalp, showing the early stages of this tool, explaining how it was built, and discussing ideas around optimization with a focus on FPL.

🌍 Browser-based optimization

There is also a browser-based version of the optimizer that doesn't require the download or installation of anything to your device, and works on mobile. It is hosted in a google colab notebook that can be found here. Simply follow the instructions on that page to run the optimizer.

🛠️ Issues

If you have issues, feel free to open an issue on GitHub and I will get back to you as soon as possible. Alternatively, you can email me at chris.musson@hotmail.com.