Adding Bayesian Ensemble + Monte Carlo to an NPB Prediction App

Introduction I've been running a personal NPB (Japanese pro baseball) prediction app: Dashboard: npb-prediction.streamlit.app GitHub: npb-prediction It used Marcel projections (3-year weighted average) and ML (XGBoost/LightGBM). Decent, but I wanted better accuracy. After adding Bayesian corrections, the predicted standings changed significantly. Terms Term Meaning Marcel Predict next year from weighted average of past 3 years Bayesian Combine prior knowledge with data. Gives uncertainty estimates CI Credible interval — range where the true value falls with 80%/95% probability OPS On-base + Slugging. Overall batting metric ERA Earned Run Average. Runs allowed per 9 innings MAE Mean Absolute Error. Average prediction miss. Lower = better Problems with the Previous Approach Problem 1: All Foreign Players Treated as "Average" Marcel needs 3 years of NPB data. First-year foreign players have none, so all 24 of them were treated as league-average. Dalbec (Giants, .355 wOBA in MLB) and Humme