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

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_<team_id>_data18-19" with individual files of all the important data

Notable Usages of this Repository

• Picking the Ultimate Fantasy Premier League Team with ArcticDB by Matthew Simpson

• Analysing Fantasy Premier League data in R Course by Arif P. Sulistiono

• Point Predictor via Random Forests by Francesco Barbara

• Money (Foot)Ball – how will our virtual football team selected entirely by Machine Learning compete in the big leagues?

• An introduction to SQL using FPL data by Liam Connors

• Hindsight Optimization for FPL by Sertalp B. Cay

• Data Science to get top 1% on return to FPL by James Asher

• 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

• 2019-20 Winner Joshua Bull's Oxford Maths Public Lecture

• 2019-20 Lottery Analysis by @theFPLKiwi

• Fantasy Nutmeg Website by code247

• Fantasy Premier League 19/20, a review by Hersh Dhillon

• Visualisasi Data: Fantasy Premier League 19/20 by Erwindra Rusli

• Machine Learning Model by pratz

• xA vs xG for Attacking Midfielders/Forwards by u/JLane1996

• Expected Goals vs Actual Goals for Manchester United by u/JLane1996

• Tableau Viz by u/richkelana

• Top Players against GW13 rival by u/LiuSiuMing

• Captaincy Choice GW4 2019-20 post by Matthew Barnfield

• Building a dataset for Fantasy Premier League analysis by djfinnoy

• Value in FPL 2019-20 Report by Who Got The Assist?

• Talisman Theory 2018-19 Report by Who Got The Assist?

• Historical Analyses in fplscrapR by Rasmus Chrisentsen

• Linearly Optimising Fantasy Premier League Teams by Joseph O'Connor

• How to Win at Fantasy Premier league using Deep learning by Paul Solomon

• graphql API by u/jeppews

• FPL modeling and prediction by @alsgregory

• FPL.co.id Talismans by @FPL_COID

• Leicester City Brendan Rodgers Impact Analysis on twitter by @neilswmurrayFPL

• Stat Analysis on twitter by @StatOnScout

• Arsenal-Chelsea LinkedIn article by Velko Kamenov

• Form vs Fixture Medium article by JinHyunCheong

• Visualization by u/dkattir

• Visualization by u/Dray11

• Visualization website by @antoniaelek

• FPL Captain Classifier by Raghunandh GS

• My Personal Blog

• FPL.zoid.dev - Query FPL data with SQL in your browser

• Premier League Table by FPL Points by Edward F

• FPL Manager Medals by Edward F

• SiegFPL by @infinitetrooper