I want to turn my rich collection of college-basketball data into a model that reliably forecasts game results before tip-off and updates the probability as plays unfold. You’ll be working with three data streams I already have in place—historical game logs, detailed player performance metrics, and a live in-game feed. The core of the job is to design, train, and validate a predictive pipeline in Python that combines Monte Carlo simulation with machine-learning techniques. I’m especially interested in seeing how simulation at both the game level and the play-by-play level can blend with gradient-boosting, neural networks, or any other supervised approaches you believe outperform a pure simulation baseline. Robust statistical testing and model-evaluation routines are essential so we can quantify lift over publicly available benchmarks. Because the ultimate goal is dependable win-probability estimates, I need clear deliverables: • Clean, well-documented Python code (Jupyter notebooks or .py modules) • Reproducible training scripts tied to my data folders • Version-controlled experiments showing validation accuracy and calibration metrics • A short technical report summarizing methodology, assumptions, and recommendations for next steps If you’ve previously built Monte Carlo or machine-learning models for sports—especially basketball—tell me about the algorithms you used, how you handled lineup changes, and any performance gains you achieved. I’m ready to iterate quickly, provide domain guidance.