MLB Analytics Lab 1,000 PA Monte Carlo Simulation

MLB CONTROL VS. VARIANCE EXPLORER

"There's no point worrying about things you can't fix" — Quantifying Pitcher Skill vs. Environmental Luck

Presets:

Controllable Pitcher Skill Vectors

Process

Strikeout Rate (K%) 26.0%

10% (Poor) 22% (Avg) 40% (Elite)

Walk Rate (BB%) 7.0%

3% (Elite) 8% (Avg) 15% (Poor)

Hard-Hit Rate (95+ mph %) 38.0%

25% (Soft) 38% (Avg) 55% (Crushed)

Barrel Rate % 6.5%

2% (Suppressed) 7% (Avg) 15% (High Risk)

Uncontrollable Environmental Noise

Luck / Context

BABIP Noise Factor .305

.240 (Unlucky for hitter / Lucky P) .300 (MLB Avg) .360 (High BABIP)

Defensive OAA / Positioning 0 Runs

-20 (Poor Defense) 0 (Average) +20 (Gold Glove Defense)

Park Factor Multiplier 1.02

0.85 (Pitcher Park) 1.00 (Neutral) 1.25 (Coors Field)

⚾ Ballpark Batted-Ball Trajectory Simulator

1,000 simulated batted balls mapped against outfield walls and defensive zones

Out 1B 2B 3B HR

📊 Process (Control) vs Outcome (Noise) Diagnostics

Variance Delta: +0.58 ERA

Process FIP

3.24

True pitching skill

Outcome ERA

3.82

Distorted by luck/def

Process xwOBA

.298

Expected contact wOBA

Outcome wOBA

.322

Actual allowed wOBA

Seasonal ERA vs. FIP Outcome Variance Density Curves 162-Game Regression Projection

💡 Regression-to-the-Mean Telemetry: Elite (Top 15%)

The pitcher demonstrates elite core skills (high K%, low barrel rate), but defensive gaps and an elevated BABIP (.305) inflated their ERA by +0.58. Over a full 162-game sample, expect strong positive regression toward their 3.24 FIP.