Club_Financial

Club Financials Analysis

Data analysis project exploring football club financial data (2010–2026) using Python and pandas. The project covers descriptive statistics, correlation analysis, and a simple predictive model for club profitability.

Overview

This project analyzes a dataset of club-level financial metrics across major European leagues to answer three questions:

• How fast is a given club's revenue growing over time?
• Which financial metrics are most correlated with a club's profitability?
• Can club profitability be predicted from wage spending and transfer activity?

Dataset

The dataset (club_financials.csv) includes yearly financial records per club:

Column	Description
year	Season year (2010–2026)
club_name	Club name
league	National league
country	Club's country code
stadium_capacity	Stadium capacity
revenue_eur_m	Total revenue (€M)
wage_bill_eur_m	Total wage bill (€M)
wages_to_revenue_pct	Wage-to-revenue ratio (%)
net_transfer_spend_eur_m	Net transfer spend (€M)
operating_profit_eur_m	Operating profit (€M)

Project Structure

├── Datasets/
│   └── club_financials.csv
├── First_Step/
│   ├── Financial/
│   │   ├── dataset.py          # ClubFinancials class (data access layer)
│   │   └── settings.py         # Config / constants
│   └── import_data.py
└── README.md

Key Components

ClubFinancials class

An object-oriented wrapper around the dataset that filters club data by dynamic criteria (club name, year, etc.) using an encapsulated indexing property with getter/setter, so filter criteria can be updated without exposing internal state directly.

Analysis

• Descriptive statistics per club and per league (.describe(), .groupby())
• Growth analysis (CAGR of revenue per club)
• Correlation analysis between wage spending, transfer spend, and operating profit
• League-level comparison (revenue, wage-to-revenue ratio, cumulative profit)

Requirements

pandas
numpy
matplotlib

Usage

from dataset import ClubFinancials
import pandas as pd

data = pd.read_csv("Datasets/club_financials.csv")
cf = ClubFinancials({"club_financials": data}, club_name="Manchester City")

club_data = cf.get_club()

Author

Fidèle Togbédji ZOGBE — GitHub Student in Génie Informatique et Télécommunications, EPAC (Université d'Abomey-Calavi)