⚽ Football Market Value Analysis (Power BI)

🇺🇸 English Version

📌 Project Overview

This interactive dashboard analyzes football player data from Transfermarkt to predict and visualize market values. This project demonstrates my ability to handle the full Business Intelligence lifecycle, from raw data ingestion to actionable insights using Power BI.

(Note: This project was developed locally using Power BI Desktop. The screenshots below demonstrate the dashboard's features and insights.)

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📸 Main Dashboard View

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🎯 Objectives

The goal was to answer key business questions using data:

• Which clubs and nations hold the highest total market value?
• Who are the most valuable players on the market?
• How is market value distributed across different positions and age groups?

🛠️ Technical Implementation

1. ETL & Data Cleaning (Power Query):

   • Ingested raw CSV data from Kaggle.
   • Performed data transformation: handling null values, type casting, and column selection.
   • Created calculated columns (e.g., dynamic Age calculation from birthdate).

2. Modeling & DAX:

   • Designed a Star Schema data model.
   • Wrote complex DAX measures for key metrics (Total Market Value, Player Count, Average Value).

3. Visualization & Storytelling:

   • Designed a dynamic report using Treemaps, Bar Charts, and KPIs.
   • Implemented Slicers and Filters for interactive exploration (by Club, Nationality, Position).

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✨ Interactivity Examples

1. Focus on "Midfielders"

Here is the report filtered to show only players in the "Midfield" position. The KPIs and charts update dynamically.

2. Focus on "France"

Here is the report filtered to show only players with "France" as their nationality.

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📂 Repository Contents

• DataFootTransfertmarkt.pbix: The source Power BI file.
• players.csv: Raw dataset used for analysis.
• /images: Folder containing dashboard screenshots.

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🇫🇷 Version Française

🎯 Objectif du Projet

Ce projet vise à mettre en pratique l'ensemble du processus de Business Intelligence avec Power BI, du nettoyage des données à la visualisation. L'objectif était de répondre à des questions clés telles que la répartition de la valeur marchande par club, nation et poste.

🛠️ Compétences Mises en Œuvre

1. Collecte et Nettoyage (Power Query) :

   • Importation et nettoyage du jeu de données (Kaggle).
   • Gestion des valeurs nulles et typage des données.
   • Création de colonnes calculées (âge, catégories).

2. Modélisation et DAX :

   • Création de mesures DAX optimisées pour les calculs clés.

3. Visualisation :

   • Conception d'un rapport dynamique avec filtres interactifs.

(Les captures d'écran ci-dessus illustrent le fonctionnement des filtres dynamiques sur les positions et les nationalités).