Start with the Core Problem
Stop guessing. Build a model that actually predicts run lines, not wishful thinking. The market is efficient enough to punish amateurs, but sloppy data scientists still thrive.
Data Gathering
Look: you need raw game logs, player stats, park factors, and weather feeds. Scrape MLB’s API, grab Statcast, pull historical lines from sportsbooks. No shortcuts. Two‑hour download, then you’re set.
Feature Engineering
Here’s the deal: transform raw numbers into predictive power. Use batting average on balls in play (BABIP), left‑right splits, bullpen fatigue, pitcher‑hitter historic matchups. Toss in park-adjusted ERA. Slice and dice until you feel the edge.
Model Selection
Pick a tool. Logistic regression for simplicity, XGBoost for depth, neural nets if you love complexity. Don’t overengineer; each extra layer costs interpretability. Run a quick cross‑validation, watch the AUC spike, then prune.
Validation & Edge
Split your data: train, test, hold‑out. Simulate a betting bankroll, track ROI. If you’re breaking even after commission, you’re not ready. Sharpen the model until the Kelly criterion suggests a positive edge. Beware overfitting, it’s a silent killer.
Deployment
Deploy on a cloud VM, schedule daily updates, feed new line odds from bookmakers. Automate alerts: when predicted win probability exceeds implied odds by 2‑3%, fire off a bet. Keep logs, iterate weekly.
Resources
For raw data feeds and community chatter, swing by baseball-bet.com. It’s a gold mine of line history and sanity checks.
Final Actionable Advice
Pick a single metric—say, pitcher’s first‑inning FIP—and build a one‑variable regression. Bet only when it outperforms the market by 1.5% and watch the bankroll climb.