Optimizing Marcel Projection Weights for NPB — Grid Search + Bootstrap Validation

Background The Marcel projection system is a simple but effective player performance forecasting method created by Tom Tango. It uses a weighted average of the last 3 seasons plus regression to the mean. GitHub : https://github.com/yasumorishima/npb-marcel-weight-study The default parameters were calibrated for MLB data : Parameter Meaning MLB Default w0 / w1 / w2 Weights for last 3 years 5 / 4 / 3 REG_PA Regression strength (hitters) 1200 REG_IP Regression strength (pitchers) 600 Are these optimal for NPB (Nippon Professional Baseball)? I ran a comprehensive grid search to find out. Study Design Grid Search Target Search Space Combinations Hitters w0(3-8) × w1(1-5) × w2(1-4) × REG_PA(6 values) 720 Pitchers w0(3-8) × w1(1-5) × w2(1-4) × REG_IP(5 values) 600 Evaluation Cross-validation: 2019–2025 (7 years) Two scenarios: with 2020 (COVID-shortened season) / without 2020 Metric: MAE (Mean Absolute Error) Data: 3,780 hitter rows / 3,773 pitcher rows (2015–2025) Runtime: ~4.5 hours on GitH