Turkish National Team Dataset

Turkish National Team Financial and Performance Data (2018-2024)

This dataset provides comprehensive financial and performance data for the Turkish National Football Team from 2018 to 2024. It covers various aspects of the team’s annual operations, including revenue sources, expenses, and match outcomes. This dataset is ideal for exploratory data analysis (EDA), data visualization, and machine learning projects focused on sports analytics, financial trends, and performance metrics.

Dataset Content
The dataset includes the following key features:

Year: The year in which data was recorded (2018-2024).
Fixture: Type of match, including "Friendly", "Qualifying", and "Tournament" games.
Match Result: Outcome of the match, represented as "Win", "Lose", or "Draw".
Goals: Total goals scored by the team in each match.
Transport Cost (Euro): Expenses related to travel and logistics.
Office Expenses (Euro): Administrative costs associated with managing the team.
Ticket Revenue (Euro): Income generated from ticket sales for matches.
Sponsor Revenue (Euro): Revenue from team sponsors.
Attendance: Average number of spectators per match.
Total Salary Expense (Euro): Annual total salary costs for players.
Total Bonus Expense (Euro): Annual bonus expenses for players.
Merchandise Revenue (Euro): Revenue from team merchandise sales.
Advertising Revenue (Euro): Revenue generated from advertising, including match-day ads and sponsorships.
Medical Expenses (Euro): Costs associated with player health and medical care.
Key Features
Null Values: Includes some null values to simulate real-world data, encouraging users to clean and preprocess data for analysis.
Outliers and Variations: Financial and performance data may exhibit variations and outliers, reflecting realistic fluctuations in team revenues and expenses.
Mixed Data Types: Data types include integers, floats, and categories, providing opportunities for various data processing and feature engineering techniques.
Usage Examples
This dataset can be used in a variety of projects:

Exploratory Data Analysis (EDA): Gain insights into the Turkish National Team's financial structure, performance patterns, and trends.
Data Visualization: Create compelling visualizations using tools like Tableau and Power BI to showcase trends and insights.
Machine Learning: Train models for predictive analytics, such as predicting match outcomes based on historical financial data or estimating future revenue growth.
This dataset provides a realistic and multi-faceted view of the Turkish National Team's financial and operational metrics, making it a valuable resource for sports analysts, data scientists, and financial analysts interested in the sports industry. Enjoy exploring the data and uncovering insights about one of Turkey's most beloved national teams!