Context

This is a data dump of the football section of Statbunker's searchable football statistics database. I have uploaded League data for these European leagues:

1. Premier League
2. Bundesliga
3. La Liga
4. French Ligue 1
5. Eredivisie
6. Serie A
7. Scottish Premiership

Content

I have pulled data for the following seasons:

• 2014-15
• 2015-16
• 2016-17
• 2017-18 (Current)

Based on the following disciplines:

• Player stats
• Away attendance
• Home attendance
• League Nationalities
• League Tables
• Team Defense
• Team Offense

Acknowledgements

All data pulled can be found on the Statbunker website: https://www.statbunker.com/

Inspiration

For anyone who enjoys footbal, and analyzing football stats. Please feel free to run kernels!

This dataset provides an archive of Fantasy Premier League (FPL) player performance data for eight seasons, spanning from 2016-2024.

The data was originally collected from https://github.com/vaastav/Fantasy-Premier-League, a public repository for FPL data.

The dataset has been meticulously cleaned and processed to ensure accuracy and consistency. This may include handling missing values, correcting inconsistencies, and standardizing formats.

The dataset includes a wide range of player statistics captured on a gameweek-by-gameweek basis. This allows you to analyze trends, identify patterns, and gain valuable insights into player performance.

This dataset can be a powerful tool for FPL enthusiasts and data scientists alike. Here are some potential applications: - Trend Analysis: Identify historical trends in player performance across different seasons and positions. - Predictive Modeling: Develop machine learning models to predict player points, performance, and transfers. - Informed Team Selection: Make data-driven decisions to optimize your FPL team for each gameweek. - Comparative Analysis: Compare player statistics across seasons and positions to uncover hidden gems and potential breakout stars.

Using this dataset, you can gain a deeper understanding of FPL player performance and enhance your decision-making for the upcoming season.

Data Sources :- The following data sources were used for this model: - Player attributes - FIFA 16-21 data - Injury history - Transfermarkt injury history data. Pulled and scraped from there using worldfootballR R package

Players/seasons in scope :- - Original scope was all players who have played in the British Premier League at any point between 2016/17 season and 2020/21 season - Due to complications and difficulties in joining 3 datasets from entirely different sources, this came out to a total of 685 rows of data, consisting of 317 players

Training Data :- - 3 separate data sources were combined to create a datset which included player attributes (i.e. - pace, height, weight), player injury history and player game time - Data was grouped on a player-year level