transfermarkt

📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.

A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat

API service to get data from Transfermarkt

Flatiron School Capstone project. Trying to find out how well players' on-field performance metrics can be used to predict their transfer values.

Your GitHub, valued like a football player. Calculate your dev market value, transfer history, and get dynamic cards for your profile README.

Football transfer intelligence platform that combines player performance, historical transfer data, and machine learning to estimate transfer values.

asyncIO, Github Actions, GCP, dbt, Terraform, Docker

Unofficial documentation for Transfermarkt's private API endpoints. A community-driven guide for developers.

Full pipeline to scrape Transfermarkt player data, expose it via FastAPI, and visualize it with Streamlit.

The repository for the first internship project of Quera data analysis bootcamp

Data-driven football possession value model (xT/VAEP) to quantify ball progression and player impact.

Scrapping of Historical Market Values (MV) of football players from www.transfermarkt.com using Scrapy.

Integrating FPL, Transfermarkt, and Understat data, our strategy optimizes squad selection. This approach, leveraging market values and detailed player statistics, ensures both financial prudence and on-field performance. Strategic acquisitions align with set rules and budget constraints, emphasizing value-driven choices for a robust squad

Scrape and analyze football players kits number from transfermarkt

Scrape top transfer and query fotball club in world

A repository for storing web scraped Transfermarkt data

This repository contains a collection of scripts that scrape data from Transfermarkt.com. The objective of this project is to analyze the relationship between player characteristics and player value.

Identify undervalued soccer players by analyzing market value vs performance using CatBoost regression with 5-Fold CV and interactive dashboard.

Map of the Bundesliga teams from 1991 to 2021

A Python script for web scraping Transfermarkt transfer data