Former professional basketball player turning into basketball analytics, data engineering, and performance science.
I build end-to-end data pipelines, analytical models, and decision-support tools using real basketball data — focused on lineups, player impact, game context, and movement analytics.

My work sits at the intersection of basketball intuition, statistical modeling, and practical engineering — with the goal of supporting coaching, scouting, and front-office decision-making.

• Basketball analytics & player evaluation
• Lineup analysis, on/off impact, and game context
• Predictive modeling & applied statistics
• Data pipelines, SQL-based ETL, and reproducible analysis
• Performance science & biomechanics (motion data, movement trends)

Languages

• Python
• SQL

Analytics & Modeling

• pandas · NumPy · scikit-learn
• Statistical modeling & regression
• Time-series & game-state analysis
• Feature engineering

Data Engineering & Tools

• NBA API
• SQLite / Postgres
• Git / GitHub
• Docker (containerized workflows)
• Jupyter / Streamlit

Visualization

• Matplotlib
• Plotly
• Dash / Streamlit dashboards

• Context matters more than totals
• Short stints reveal more than full-game averages
• Data should aid — not replace — basketball intuition

My background as a professional player directly shapes how I frame analytical problems and interpret results.

• Advanced lineup impact models (RAPM-style)
• Win probability & game-state modeling
• Tracking-data inspired movement analytics
• Scalable tooling for team & staff usage

• 📧 Email: rbcu25@gmail.com
• 🔗 LinkedIn: https://www.linkedin.com/in/robukawuba
• 🌍 GitHub: https://github.com/Rukawuba

Always open to conversations around basketball, basketball analytics, performance science, or applied data work in sports.