📊Edd Webster(@eddwebster)的足球分析项目、数据和分析的集合,包括足球分析社区发布的公开可用资源的精选列表。
transfermarkt 开源项目推荐(65 个)
transfermarkt 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 65 个仓库一个用于从FBref、Transfermark、Understat提取世界足球(足球)数据的包装器
API 服务用于从 Transfermarkt 获取数据
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.
Open-source Transfermarkt scraper in Python. Export market values, club squads, transfers and player profiles to JSON or CSV.
Unofficial documentation for Transfermarkt's private API endpoints. A community-driven guide for developers.
asyncIO, Github Actions, GCP, dbt, Terraform, Docker
A repository for storing web scraped Transfermarkt data
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
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
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.
Data-driven football possession value model (xT/VAEP) to quantify ball progression and player impact.
Exploring what affects the market value of the English Premier League players with pandas, seaborn and linear regression(Transfermarkt data, 2021-2026)
End-to-end Power BI project analyzing football player market values. Features data cleaning (Power Query), advanced DAX modeling, and interactive visualization.
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/transfermarkt。
- 该话题在 GitHub 上共有 65 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 4031 Star,覆盖 5 种主要语言,数据更新于 2026-11-08。
- 中文介绍来自 GitHub 官方话题说明(github/explore)的机器翻译,可点「查看原文」对照;译文缺失时会先展示原文。