Fantasy-Premier-League

ARCHIVAL NOTICE

This repository will be archived at the end of the 2024-25 season. NO updates will be posted to this repository after the 2024-25 season.

Description

A FPL library that gets all the basic stats for each player, gw-specific data for each player and season history of each player

How to CIte this dataset?

BibTeX:

@misc{anand2016fantasypremierleague,
  title = {{FPL Historical Dataset}},
  author = {Anand, Vaastav},
  year = {2022},
  howpublished = {Retrieved August 2022 from \url{https://github.com/vaastav/Fantasy-Premier-League/}}
}

Acknowledgement

rin-hairie for adding master team lists and merge scripts
ergest for adding merged_gw.csv files for 2016-17 and 2017-18 seasons
BDooley11 for providing top managers script
speeder1987 for providing 2018/19 fixtures.csv file
ravgeetdhillon for github actions automation for data update
kz4killua for fixing GW37 data for the 21-22 season
SaintJuniper for id-dictionary update for the 21-22 season

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.

Accessing the Data Directly in Python

You can access data files within this repository programmatically using Python and the pandas library. Below is an example using the data/2023-24/gws/merged_gw.csv file. Similar methods can be applied to other data files in the repository. Note this is using the raw URL for direct file access, bypassing the GitHub UI.

import pandas as pd

# URL of the CSV file (example)
url = "https://raw.githubusercontent.com/vaastav/Fantasy-Premier-League/master/data/2023-24/gws/merged_gw.csv"

# Read the CSV file into a pandas DataFrame
df = pd.read_csv(url)

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__data18-19" with individual files of all the important data

Notable Usages of this Repository

vaastav / Fantasy-Premier-League

readme