Name: Transfermarkt Data Analysis 22/23
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Transfermarkt Data Analysis 22/23

this graph was created in R:

A data scraping project focused on extracting transfer information from Transfermarkt for the 2022/2023 seasons using Python and Selenium the project aims to analyze club activities in the transfer market identifying instances of overpaid and bargain signings by leveraging web scraping techniques this project provides valuable insights into the financial aspects of football transfers enabling a better understanding of clubs' spending patterns during the specified seasons furthermore the extracted data is seamlessly visualized using Tableau a powerful data visualization tool Chelsea FC of England spent the most in this transfer window almost €600m their most expensive player was Enzo Fernandez Premier League of England had the most business in this transfer window with 138 incomings averaging a transfer fee of €21.98m per player and €3.3b in total the players' age has a relation with their transfer fee market value and their movement in the market players within the age of 21 to 25 have the most demand the most popular position was center forward with 386 total bought in this position followed by center back in terms of nationality Latin American players were very demanding in this transfer window with Brazil contributing 97 players signing for various clubs Europe’s top five leagues had two signings that stood out in the scatterplot Erling Haaland as a bargain €90m less than the market price and Enzo Fernandez as the most overpaid with €66m paid more than the market price to replicate the project clone the repository using the command git clone https://github.com/saadism777/Transfermarkt-Scraping navigate to the project directory with cd Transfermarkt-Scraping initialize and activate a virtual environment using virtualenv --no-site-packages venv then activate it with source venv/bin/activate install the required dependencies by running pip install -r requirements.txt download Chrome WebDriver from https://chromedriver.chromium.org/downloads and then run the scraper using python scraper.py --chromedriver_path

Tables

Sheet 1 Full Data Data

@kaggle.willianoliveiragibin_transfermarkt_data_analysis_2223.sheet_1_full_data_data

22.77 KB
965 rows
3 columns


CREATE TABLE sheet_1_full_data_data (
  "club" VARCHAR,
  "name" VARCHAR,
  "fee" DOUBLE
);