FPL Optimization Repository

This repository is a collection of optimization tutorials and recipes for Fantasy Premier League (FPL).

Python code mainly use pandas for data management and sasoptpy for optimization modeling.

It is being actively developed. The content and the structure of the repository might change.

Tutorials

If you are interested in using optimization for FPL, see my YouTube tutorials on the subject.

Python

Link: https://youtube.com/playlist?list=PLrIyJJU8_viOags1yudB_wyafRuTNs1Ed

Python tutorials include following topics

Goalkeeper selection problem
Single-period expected value maximization (squad, lineup, captain)
Multi-period expected value maximization (squad, lineup, captain)
Alternative solution generation
Multi-objective optimization (2-Step and Weight methods)
Bench decisions
Auto-bench weights and iterative solution for nonlinear case
Noise in expected values
Sensitivity analysis
Data collection from FPL API with login
Wildcard (chip) optimization

Excel

Link: https://youtube.com/playlist?list=PLrIyJJU8_viOLw3BovPDx5QLKkCb8XOTp

My Excel tutorials are rather short but might give you an idea what optimization is capable of doing. Reach out to me if you need the raw data to give it a try.

Goalkeeper selection problem
Single-period expected value maximization (squad, lineup, captain)
Multi-period expected value maximization (squad, lineup, captain)

Instructions

Steps

You will need to follow steps below to install required platform and also optimization solver (CBC).

Download and install Python and Git to your machine
Download CBC optimization solver binary and add it to your environment path (example: https://youtu.be/DFXCXoR6Dvw?t=1642)
Clone the repository

git clone https://github.com/sertalpbilal/FPL-Optimization-Tools.git fpl-optimization

Install required packages

cd fpl-optimization
python -m pip install -r requirements.txt

Download FPLReview projections and save it under data and rename it to fplreview.csv
Navigate to run directory

cd ..\run

And run either solve_regular.py (for regular GW solve) or solve_wildcard.py (for wildcard optimization)
See instructions below.
Log in FPL from your browser and open https://fantasy.premierleague.com/api/my-team/MY_TEAM_ID/ after replacing MY_TEAM_ID with your team id. Copy the content of the page into data\team.json file, by creating one.

A sample team.json file is provided for your reference: team.json.sample

Multi-period GW optimization

Edit content of regular_settings.json file
```
  {
      "horizon": 5,
      "ft_value": 1.5,
      "itb_value": 0.2,
      "decay_base": 0.84,
      "no_future_transfer": true,
      "no_transfer_last_gws": 0,
      "randomized": false,
      "xmin_lb": 2,
      "banned": [],
      "locked": [],
      "delete_tmp": true,
      "secs": 300,
      "use_cmd": false,
      "future_transfer_limit": null,
      "no_transfer_gws": [],
      "booked_transfers": [],
      "use_wc": null,
      "use_bb": null,
      "use_fh": null,
      "chip_limits": {"bb": 0, "wc": 0, "fh": 0, "tc": 0},
      "num_transfers": null,
      "hit_limit": null,
      "preseason": false,
      "cbc_path": "",
      "no_opposing_play": false,
      "pick_prices": {"G": "", "D": "", "M": "", "F": ""}
  }
```
- horizon: length of planning horizon
- ft_value: value assigned to the extra free transfer
- itb_value: value assigned to having 1.0 extra budget
- decay_base: value assigned to decay rate of expected points
- no_future_transfer: true or false whether you want to plan future transfers or not
- no_transfer_last_gws: the number of gws at the end of the period you want to ban transfers
- randomized: true or false whether you would like to add random noise to EV
- xmin_lb: cut-off for dropping players below this many minutes expectation
- banned: list of banned player IDs
- locked: list of player IDs to always have during the horizon (e.g. 233 for Salah)
- delete_tmp: true or false whether to delete generated temporary files after solve
- secs: time limit for the solve (in seconds)
- use_cmd: whether to use os.system or subprocess for running solver, default is false
- future_transfer_limit: upper bound how many transfers are allowed in future GWs
- no_transfer_gws: list of GW numbers where transfers are not allowed
- booked_transfers: list of booked transfers for future gameweeks. needs to have a gw key and at least one of transfer_in or transfer_out with the player ID (e.g. 233 for Salah),
- use_wc: GW to use wildcard (fixed)
- use_bb: GW to use bench boost (fixed)
- use_fh: GW to use free hit (fixed)
- chip_limits: how many chips of each kind can be used by solver (you need to set it to at least 1 when force using a chip)
- num_transfers: fixed number of transfers for this GW
- hit_limit: limit on total hits can be taken by the solver for entire horizon
- preseason: solve flag for GW1 where team data is not important
- cbc_path: binary location of the cbc solver (bin folder)
- no_opposing_play: true if you do not want to have players in your lineup playing against each other in a GW
- pick_prices: price points of players you want to force in a comma separated string. For example, to force two 11.5M forwards, and one 8M midfielder, use "pick_prices": {"G": "", "D": "", "M": "8", "F": "11.5,11.5"}
Run the multi-period optimization
```
python solve_regular.py
```

Find the optimal plans under data\results directory with timestamp

  > cd ../data/results
  > ls
  regular_2021-11-04_10-00-00.csv

Run in Docker

A Dockerised version of the solver is included in this repo which includes all dependencies required to run the program and save results. Docker must be installed on the host machine.

To pull the Docker image:

> docker pull ghcr.io/prmac/fploptimizationtools:gw1

Then to run the solver:

docker run -ti -v /path/to/data/folder/:/fpl-optimization/data/ fploptimizationtools:GW1

where /path/to/data/folder is the absolute path to a folder containing the following files:

team.json
regular_settings.json
fplreview.csv

License

Apache-2.0 License

ryanbehdad/FPL-Optimization-Tools