🎾 Game Analytics: Unlocking Tennis Data with SportRadar API

End-to-End Tennis Data Engineering & Analytics Dashboard
Built with Python, SQL Server & Streamlit

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📌 Executive Summary

This project is a full-stack data analytics solution that integrates the SportRadar API with a relational database and an interactive Streamlit dashboard to analyze tennis competitions, venues, and player rankings.

The system demonstrates:

• API data ingestion
• Relational database design
• SQL-based analytical querying
• Data transformation using Pandas
• Interactive dashboard visualization
• Dynamic filtering with real-time updates

It follows a modular architecture separating API, database, query logic, and presentation layers.

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🏗️ System Architecture

SportRadar API
      │
      ▼
JSON Extraction Layer (api/)
      │
      ▼
Data Processing & Cleaning (scripts/)
      │
      ▼
SQL Server Database (Normalized Schema)
      │
      ▼
Query Layer (queries/)
      │
      ▼
Streamlit Analytics Dashboard (app.py)

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🧱 Database Design

The database is designed using normalized relational principles.

🧱 Core Tables

Table Name	Description
Categories	Stores competition categories
Competitions	Tournament-level details
Complexes	Tennis complexes information
Venues	Individual playing venues
Competitors	Player profile information
Competitor_Rankings	Weekly player ranking statistics

Key Design Decisions

• Composite Primary Key on Competitor_Rankings: (competitor_id, type_id, year, week)
• Foreign key relationships between competitions, venues, and competitors
• Structured for analytical querying (grouping, filtering, aggregation)

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📊 Dashboard Modules

🏆 Competition Analytics

• Total competitions KPI
• Category distribution
• Type distribution (Singles / Doubles)
• Gender-based breakdown
• Parent & sub-competition mapping
• SQL-driven dynamic queries

📍 Venue & Complex Analytics

• Complex-to-venue relationship mapping
• Venue distribution by city and country
• Country-wise venue count visualization
• Timezone insights
• Interactive filtering

👤 Player Ranking Analytics

• Rank range filtering (1–500)
• Competitor-specific drill-down
• Country-based filtering

KPI Metrics:

• Total Players
• Highest Points
• Lowest Rank
• Average Points

Additional Insights:

• Top 10 by Rank
• Top 10 by Points
• Movement classification (Improved, Dropped, Unchanged)

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🎛️ Dynamic Filtering System

The dashboard supports global filtering across modules:

• Year / Season
• Country
• Gender
• Category
• Competition
• City
• Complex
• Venue
• Rank Range
• Competitor

All filters dynamically update tables, KPIs, and charts in real-time.

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🛠️ Technology Stack

Layer	Technology
Programming	Python 3.10
Frontend	Streamlit
Visualization	Plotly
Data Handling	Pandas
Database	SQL Server
API Integration	SportRadar API
Environment Config	python-dotenv
Version Control	Git

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📂 Project Structure

game-analytics-tennis/
│
├── api/
│ ├── competitions_api.py
│ ├── complexes_api.py
│ └── rankings_api.py
│
├── data/raw/
│ ├── competitions.json
│ ├── complexes.json
│ └── double_rankings.json
│
├── database/
│ ├── db_connection.py
│ ├── schema.py
│
├── queries/
│ ├── competition_queries.py
│ ├── ranking_queries.py
│ └── venue_queries.py
│
├── scripts/
│ └── insert_data.py
│
├── app.py
├──tennis_db.sqlite
├── requirements.txt
└── .env

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⚙️ Installation & Setup

1. Clone Repository

git clone https://github.com/Nihaa-20/Tennis-Game-Analysis
cd game-analytics-tennis

2. Create Virtual Environment

python -m venv venv
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file:

DB_SERVER=your_server
DB_DATABASE=tennis_db
DB_USERNAME=your_username
DB_PASSWORD=your_password

5. Initialize Database

python database/schema.py

6. Insert Data

python scripts/insert_data.py

7. Run Application

streamlit run app.py

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📈 Analytical Capabilities

• Aggregation queries (COUNT, SUM, AVG)
• Grouping by category, type, gender
• Ranking comparison analysis
• Movement trend categorization
• Top-N analysis
• Drill-down competitor filtering

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🧠 Skills Demonstrated

• API data ingestion & JSON handling
• Database schema design
• SQL analytical query writing
• Data cleaning & transformation
• Dashboard UX structuring
• Modular project architecture
• Error handling & data validation

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🔮 Future Enhancements

• Live API auto-refresh scheduling
• Player head-to-head comparison module
• Tournament bracket visualization
• Deployment on Streamlit Cloud / Azure
• Authentication & role-based access

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👩‍💻 Author

Fathima Niha
BCA Graduate | Data Analytics Enthusiast

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⭐ Acknowledgment

Data sourced from SportRadar API for academic and analytical purposes.