I tracked 327 MLB prop picks over 30 days with an ML model. 72.8% hit rate. Here's what actually matters and what doesn't.

Been building an MLB intelligence system called loyAI for the past few months. It runs LightGBM models across every prop type, pulls real-time odds from 7 sportsbooks, and auto-grades every pick nightly. No manual tracking, no cherry-picking — the system grades itself and the numbers are what they are.

The Record (auto-graded nightly)

Window	Record	Hit Rate
7-day	129-48	72.9%
14-day	206-85	70.8%
30-day	238-89	72.8%

These aren't all picks — the raw model generates 300+ picks per night. Most of that is noise. The system filters down to 30-40 Sniper Picks using proven stat types, proven directions, and a 1.0-3.0 edge sweet zone. That filter is where the accuracy lives.

(May 3)

• K OVER: 11/15 (73.3%)
• HA OVER: 5/5 (100%)
• Plus-money K picks: 6/8 (75%) — Yesavage K OVER 3.5 at +150 cashed with 6 K's
• Game Run Lines: 8/11 (72.7%)

Freeland threw 7 K's on a 2.5 line. Henderson threw 8 on a 4.5 line. Ashcraft threw 6 at +110. These aren't flukes — the model found them because batting order position, opposing pitcher K/9, and workload features actually predict strikeouts.

What the System Does Differently

Most prop models give you a prediction and assume -110 juice. loyAI pulls real odds from DraftKings, FanDuel, BetOnline, and 4 other books and calculates true expected value on every pick. If the model says OVER but the sportsbook is charging -200 juice, the system flags it as negative EV and tells you to skip it.

That single filter — only betting positive EV at real odds — changed everything. Plus-money picks (+100 to +150) outperform chalk (-150 to -200) even though accuracy is similar. The difference is profit.

The system also tracks opening line movement — it captures odds at 6 AM before sharp money moves them, then shows you exactly how much the line shifted by game time. If you're betting at 2 PM on lines that sharps moved at 10 AM, you're leaving money on the table.

The Findings That Surprised Me

Direction is everything. From 15,000+ graded picks across the full database:

Stat	Best Direction	Accuracy	Wrong Direction	Accuracy
ER	OVER	72.4%	UNDER	9.1%
HR	UNDER	91.1%	OVER	8.1%
RBI	UNDER	74.8%	OVER	28.1%
R	UNDER	69.1%	OVER	39.3%
HA	OVER	65.1%	UNDER	32.8%

Betting both directions averages out to ~55%. Betting only the proven direction hits 65-91%. Same model, same data — the filter makes the difference.

Batting order is the #1 feature. It accounts for 38% of model gain in batter prop models. A cleanup hitter facing a weak pitcher has 3x the RBI opportunity of a #9 hitter against an ace. Most prop models completely ignore lineup position.

Away +1.5 run line is a hidden gem. 74.6% accuracy on 63+ games when the model favors the road team at 55%+ conviction. Home -1.5 hits at just 42%. The model identifies quality road teams that either win outright or lose close — the +1.5 covers both.

High confidence ≠ high accuracy. Picks at 80%+ model confidence actually hit at 43%. Edge size is far more predictive than confidence score. The system now uses edge-based tiers instead of confidence-based ones.

The Honest Disclaimer

72.8% includes base rate effects. HR UNDER at 91% is partly just "most batters don't homer." ER OVER at 72% is partly "most pitchers allow a run." The model's true edge above base rate is 3-10 percentage points depending on the stat type.

But 3-10 points of edge at plus-money odds is exactly what professional bettors build careers on. The system doesn't predict the future — it finds mispriced lines and only bets when the math is in your favor. Consistently. Every night. Auto-graded so you know exactly where you stand.

Building this in public. If you want to follow along or see the nightly picks, check out loyAI — the dashboard shows every Sniper Pick with real sportsbook odds, EV calculations, and line movement tracking. Always looking for feedback from sharps who want to poke holes in the methodology.

http://loyAi.life