How We Filter Our Picks
We built a multi-variable model that calculates a true line for every MLB game. It was profitable from day one.
Then we nearly doubled its ROI without changing a single variable or weight — just by filtering which picks we actually recommend.
Every MLB recommendation we publish is an edge-based pick — Best Bet or Undervalued — that fires when our model's win probability is higher than the book's by enough to clear our threshold. The bigger the mispricing, the higher the tier.
The discovery
After tracking hundreds of MLB games, we ran an exhaustive analysis — testing every combination of edge thresholds, moneyline ranges, home/away splits, and category restrictions we could find.
The data told a clear story: home picks are profitable at lower edge thresholds than away picks. Road games carry more variance that our model can't fully capture — travel fatigue, unfamiliar parks, hostile crowds, and bullpen matchup uncertainty all add noise. Home picks convert smaller edges into profit because the home environment reduces that variance.
Why home and away need different thresholds
Consider two picks with the same edge. The home pick benefits from the team's home environment — the model's projection is more reliable because it's working with a more predictable situation. The away pick carries extra uncertainty that the model doesn't explicitly account for.
Our data across hundreds of games confirmed this pattern decisively. Home picks were consistently profitable at a lower edge threshold. Away picks needed a significantly higher edge before the signal overcame the noise.
How the filter works
Updated 2026-06-11 after a walk-forward tier review.
We recommend MLB picks in two situations:
Best Bet — the model sees a large edge (raw edge of 10%+) on either side, regardless of moneyline. These are our highest-conviction plays.
Undervalued — a home pick with a smaller edge, between 3% and 10%. The book has mispriced the home team in our favor.
Everything else is filtered out and labeled No Play. The game is tracked in our database for analysis, but it's not recommended to users.
The results
The picks that were filtered out were net losers collectively with a negative ROI. Removing them concentrated the output on the model's actual strengths.
The MLB tiers
The filter creates two recommended tiers — Best Bet and Undervalued — plus No Play.
Best Bets are picks where the model calculates a large edge (10%+ raw edge) over the book, on either side. These are the strongest mispricings we find — our highest-conviction plays. When the model is this confident, sizing up makes sense.
Undervalued picks are home teams with a smaller edge (3–10%) that falls short of a Best Bet. The book has mispriced the home team in our favor. Individually any one might lose. Collectively, they produce consistent returns.
No Play is everything the filter removes. You can still see these games on our platform if you choose “Show Everything,” but they're dimmed and clearly labeled. We track them for transparency and ongoing research.
Why simplicity wins
We tested dozens of more complex filter rules — moneyline caps, favorite/underdog distinctions, multi-range edge carve-outs, extra low-edge tiers. The complex rules performed similarly or worse than the simple two-tier approach out of sample, and they were more likely to be overfit to the specific games in our sample.
Simple rules generalize better. A high bar for Best Bets on either side, plus a lower bar for home Undervalued picks, is easy to verify and hard to overfit. It captures the one structural truth our data consistently shows: home games convert smaller edges into profit.
See the filter in action
Every pick on Dr. TrueLine is labeled Best Bet, Undervalued, or No Play. You can filter by tier, check our track record for each tier separately, and verify that the filter produces exactly the results described here.
We don't hide the No Play games. We don't pretend every game has an edge. Transparency isn't just our policy — it's our best sales tool.