A 6-layer probability engine that predicts scoreless MLB innings with 89% verified accuracy. Every pick auto-generated before first pitch, every result verified against MLB box scores. No cherry-picking. No hindsight.
The Garden principle applied to information markets. The engine doesn't force predictions — it creates conditions where signal emerges from noise. Six analytical layers (pitcher command, park factors, umpire zones, platoon splits, velocity trends, weather) feed into a probability surface. The system doesn't pick winners; it identifies innings where the conditions for scorelessness are overwhelmingly present. Emergence, not orchestration.
150+ verified picks since opening day. 134 wins, 17 losses. 89% win rate. Unit profit tracked in real-time with confidence-based sizing — STRONG edge picks at 1.5 units, MOD edge picks at 1.0 unit.
ROI north of 57% at -130 average line. Multiple perfect days with zero losses. A 13-game win streak at its peak. Every single number is publicly verifiable at brainboot.dev/labs/nrfi/results with timestamps proving each pick was generated before the game started.
Layer 1: Pitcher command profiles — K%, BB%, LOB%, expected ERA, ground ball rates, pitch arsenal data from Statcast. Not just traditional stats. The advanced metrics that tell you when a pitcher is better or worse than their surface numbers suggest.
Layer 2: Park factors — every MLB stadium has a measurably different effect on scoring. Coors Field inflates runs. Petco Park suppresses them. The model accounts for stadium-specific effects on the specific type of contact each pitcher induces.
Layer 3: Umpire zone analysis — each home plate umpire has a statistically distinct strike zone. Some zones expand, suppressing walks and scoring. Some zones compress, inflating them. The model factors in the specific umpire assigned to each game.
Layer 4: Platoon splits and lineup depth — left/right handedness matchups across the full batting order, lineup construction quality, and the times-through-order penalty that compounds after the second time through.
Layer 5: Velocity trends and bullpen fatigue — pitcher velocity decay over recent starts, workload management, and bullpen availability states that affect when relievers enter.
Layer 6: Environmental conditions — stadium-specific weather predictions for game time, including wind direction relative to ballpark orientation, temperature effects on ball carry, and precipitation modeling.
The engine runs on Vercel Cron jobs with zero human intervention. Every morning at 11am ET, the scan cron fetches the MLB schedule, analyzes every game with confirmed starting pitchers, and saves picks that clear the confidence threshold to the database.
Every morning at 1am ET, the verify cron fetches MLB box scores for yesterday's games and marks each pick as WIN or LOSS. The verification system is independent — there is no mechanism to retroactively edit or delete results.
Weekly packages are auto-bundled every Monday. Pro and VIP subscribers receive email alerts with the day's picks before first pitch. The entire pipeline from analysis to delivery to verification runs autonomously.
The 1st inning NRFI market is well-modeled by sportsbooks. Everyone builds models for it. The 3rd through 6th inning market is genuinely inefficient — nobody constructs granular probability engines for mid-game innings where pitcher fatigue curves, times-through-order penalties, and lineup depth effects start compounding.
The engine's selectivity is as valuable as its accuracy. It skips games that don't meet threshold. It skips innings that don't meet threshold. The decision to NOT bet is doing most of the heavy lifting.
The You Betcha engine is a circuit built on the Brainboot platform. It's the proof of concept that composable brains with typed I/O and invariant checking actually work on real-world problems with objectively verifiable outcomes.
The engine uses the same architecture as Content Empire and Sales Engine — brain composition, scheduled execution, and automated verification. The domain is different but the infrastructure is identical. If you can predict scoreless innings at 89%, the composition model works.