How Dr. TrueLine's MLB Model Works
Most betting models are black boxes. “Trust our algorithm” is the pitch, and the methodology stays hidden. We're more transparent than most — but we also protect the specific calculations that give our model its edge.
This page explains what goes into our MLB model at a conceptual level. You'll understand what categories of data we analyze, why those categories matter, and how our filtering strategy turns raw predictions into profitable picks. What we don't share are the specific weights, formulas, and thresholds — that's our proprietary edge.
The approach
Every game starts with a league-average baseline. The model then adjusts each team's projection up or down based on multiple categories of variables. Those adjusted projections are converted to a win probability, which becomes our true moneyline — free of vig and book manipulation.
We compare our true line to the sportsbook's vig-stripped line. The gap between them is the edge. Larger gaps mean higher conviction.
What the model evaluates
Our variables fall into four broad categories:
The biggest factor in any game. Advanced metrics that measure what a pitcher actually does — not just ERA — blended across seasons with dampening so small early-season samples don't overreact, plus recent form and workload.
Advanced hitting metrics for the lineup, bullpen quality for the innings after the starter leaves, and today's actual posted lineup compared to what the team normally runs.
Park factors, weather, the handedness matchup between lineup and pitcher, and home-field advantage — the context casual bettors miss.
Flags teams riding unsustainable luck — in hitting, pitching, and record — and pulls their projection back toward what the data says they should be doing.
What makes our model different
There are other MLB models out there. What separates ours:
Multi-source data synthesis. We don't rely on a single stat or a single data provider. The model pulls from multiple sources and cross-references them. When sources agree, confidence is high. When they disagree, the model accounts for the uncertainty.
Bayesian dampening. Early in the season, a pitcher's stats are unreliable — three starts isn't enough to judge anyone. Our model blends current performance with historical data in a way that gradually shifts weight toward current-year stats as the sample grows. By midseason, the current year dominates. This prevents the wild early-season swings that plague simpler models.
Dynamic inputs. Lineups change. Weather changes. Bullpen availability changes. Our model refreshes multiple times per day as new information becomes available. The pick you see at 11 AM might look different from the one generated at 6 PM — because the inputs changed.
The filter — where prediction meets profit
This is arguably more important than the model itself. Raw predictions, even accurate ones, don't automatically translate to profit. The odds structure determines whether an edge is actually worth betting.
Our research across hundreds of MLB games revealed clear patterns: certain categories of picks consistently make money while others consistently lose — even when the model's prediction is correct. We built a filter that separates profitable picks from money traps based on these patterns.
The filter considers the edge size and situational factors to determine whether a pick is recommended. Games that pass the filter are labeled Best Bet (highest conviction) or Undervalued (profitable over volume). Games where the book has the price right are labeled No Play — tracked for transparency but not recommended.
We don't reveal the specific filter rules because they're the product of extensive proprietary research. But we do show you the results: the picks we include are profitable, and the picks we exclude are net losers. That's verifiable on our public scoreboard.
What the model doesn't do
We believe in being honest about limitations.
The model does not account for umpire tendencies — a factor we're researching but haven't integrated. It does not use closing line value or reverse line movement — both require paid data feeds. It does not model player props or totals — the engine is moneyline-only for MLB.
The model is also still growing. Our tracked results are meaningful but not yet at the thousands-of-games level where statistical certainty is absolute. We're confident in the methodology, but we're transparent that the sample size is building. Every pick and every result gets published as it happens.
See the model in action
Every output of this model is published daily on our Today's Plays page. Enable push alerts in your Account to get notified when official picks lock during the final hour before game time. Every result is tracked on the scoreboard. You don't need to take our word for any of this — the data is right there.