🏆 SQL Data Analytics Portfolio
Build real-world SQL projects using an 11-table normalized relational database with primary keys, foreign keys, and production-style relationships. Practice complex JOINs, CTEs, window functions, aggregations, ranking queries, and performance optimization while analyzing player statistics, match events, team performance, referees, stadiums, and tournament trends. Perfect for SQL portfolio projects, database management courses, and interview preparation.
🤖 Machine Learning Match Prediction
Train machine learning models to predict Quarter-finals, Semi-finals, Final, match outcomes, scorelines, and expected goals (xG) using the included match_prediction_features.csv. The feature set already includes rolling team form, FIFA rankings, Elo ratings, squad quality, market value, rest days, venue altitude, and historical performance, allowing you to focus on model development instead of feature engineering.
⚽ Sports Analytics & Performance Analysis
Analyze how teams and players performed throughout the FIFA World Cup 2026 using detailed statistics such as possession, shots, expected goals (xG), passing, fouls, corners, cards, substitutions, and match events. Build dashboards, compare teams, discover performance trends, and generate insights for sports analytics, business intelligence, and football research projects.
🧠 AI & LLM Applications
Build AI-powered football assistants using structured tournament data. Create RAG applications, chatbots, match report generators, player search tools, and natural language analytics systems that answer questions about teams, players, fixtures, statistics, and tournament history using the relational dataset.
⚙️ Data Engineering Projects
Learn how production sports datasets are built by exploring the complete ETL pipeline, automated validation scripts, SQLite database generation, and live update workflow. The project demonstrates data collection, normalization, feature engineering, quality checks, and continuous updates after every completed World Cup match.
🖥️ Interactive Dashboards & Web Apps
Develop modern dashboards and web applications using Streamlit, Dash, Power BI, Tableau, or React. Visualize fixtures, live standings, player rankings, match events, team comparisons, xG trends, and tournament statistics through interactive charts, filters, and searchable interfaces.
📚 Sports Research & Academic Studies
Use the dataset for research on match prediction, expected goals (xG), player valuation, referee decisions, home advantage, venue altitude, rest-day effects, team rankings, and tournament performance. Suitable for undergraduate projects, master's research, sports analytics studies, and data science publications.
📁 Prediction Feature Dataset
The included match_prediction_features.csv provides a ready-to-use machine learning dataset with engineered features including rolling form, xG averages, FIFA rankings, Elo ratings, squad quality, market value, venue altitude, rest days, and target labels. Simply load the CSV into Scikit-learn, XGBoost, LightGBM, CatBoost, or TensorFlow and begin training prediction models without spending hours on preprocessing.
