Fantasy-Premier-League
A FPL library that gets all the basic stats for each player, gw-specific data for each player and season history of each player
Acknowledgement
- BDooley11 for providing top managers script
- speeder1987 for providing 2018/19 fixtures.csv file
- ravgeetdhillon for github actions automation for data update
FAQ
Data Structure
The data folder contains the data from past seasons as well as the current season. It is structured as follows:
- season/cleaned_players.csv : The overview stats for the season
- season/gws/gw_number.csv : GW-specific stats for the particular season
- season/gws/merged_gws.csv : GW-by-GW stats for each player in a single file
- season/players/player_name/gws.csv : GW-by-GW stats for that specific player
- season/players/player_name/history.csv : Prior seasons history stats for that specific player.
Player Position Data
In players_raw.csv, element_type is the field that corresponds to the position. 1 = GK 2 = DEF 3 = MID 4 = FWD
Errata
- GW35 expected points data is wrong (all values are 0).
Contributing
- If you feel like there is some data that is missing which you would like to see, then please feel free to create a PR or create an issue highlighting what is missing and what you would like to be added
- If you have access to old data (pre-2016) then please feel free to create Pull Requests adding the data to the repo or create an issue with links to old data and I will add them myself.
Using
If you use data from here for your website or blog posts, then I would humbly request that you please add a link back to this repo as the data source (and I would in turn add a link to your post/site as a notable usage of this repo).
Downloading Your Team Data
You can download the data for your team by executing the following steps:
python teams_scraper.py <team_id>
#Eg: python teams_scraper.py 4582
This will create a new folder called "team_<team_id>_data18-19" with individual files of all the important data
Notable Usages of this Repository
-
FPLDASH: A customizable Fantasy Premier League Dashboard by Jin Hyun Cheong
-
How to win at Fantasy Football with Splunk and Machine Learning by Rupert Truman
-
Visualisasi Data: Fantasy Premier League 19/20 by Erwindra Rusli
-
Expected Goals vs Actual Goals for Manchester United by u/JLane1996
-
Building a dataset for Fantasy Premier League analysis by djfinnoy
-
Linearly Optimising Fantasy Premier League Teams by Joseph O'Connor
-
How to Win at Fantasy Premier league using Deep learning by Paul Solomon
-
Leicester City Brendan Rodgers Impact Analysis on twitter by @neilswmurrayFPL