Did Adding Stadium Correction Improve My NPB Baseball Predictions? — A Full Backtest Comparison

Introduction This is a follow-up to my NPB (Nippon Professional Baseball) standings prediction series. I added park factor correction to the existing Marcel+Stan Bayesian system and ran a full backtest (2018–2025, 96 team-seasons) to measure the impact. Previous articles: Beyond Marcel: Adding Bayesian Regression to NPB Baseball Predictions I Calculated NPB Park Factors for 10 Years — Stadium Renovations Revealed GitHub : npb-bayes-projection Key Terms (for first-time readers) Term Meaning Marcel method Predicts next year's stats using a weighted 3-year average (weights: 5:4:3, recent years weighted higher) Bayesian prediction (Stan) Estimates probability distributions from data, capturing uncertainty in predictions Park factor Measures how much a stadium inflates or suppresses scoring. 1.0 = neutral; >1.0 = hitter-friendly; <1.0 = pitcher-friendly Pythagorean win% Estimates win% from runs scored (RS) and allowed (RA): RS^1.83 / (RS^1.83 + RA^1.83) MAE Mean Absolute Error — average pre