Today's matchup
Lazaro draws Boston Red Sox on the road today. He shows up on 12 boards — hits (37.5/100, #164), singles (36.9/100, #174), doubles (34.6/100, #168), h+r+rbi (30.2/100, #186). We've got him at 0.9 on the hits board. The model doesn't love the spot — the numbers are below. Open any board row for the full breakdown of what's driving it.
He's a left-handed bat
Lazaro hits from the left side, and that's the first thing the model looks at. Left-handed hitters see right-handed pitching most nights, which is the favourable side — so a lefty bat's baseline is set against righties, and drawing a left-handed starter is a genuine downgrade rather than a rounding error. When you see his score fall on a day he's still in the lineup, an opposing lefty is usually why. Handedness also shapes the park read: pull-side geometry matters more for a left-handed swing than the raw park factor suggests.
Where he lands across our boards
Lazaro was scored on 12 boards in that matchup, and the spread is the interesting part: hits graded 37.5/100 while stolen bases came in at 7.1/100. Same player, same game, same opposing arm — different questions. A spread that wide usually means the matchup favours one kind of outcome over another, and it's the clearest argument for reading the board that matches what you actually care about.
How the model reads him
Lazaro gets scored the same way a backup infielder does — same inputs, same scale, no thumb on it. What he's done against arms of this handedness, what this particular pitcher and bullpen give up, his likely plate appearances, the ballpark, the conditions, his recent form. The weighting behind those is ours and gets re-tuned weekly against what actually happened. The output is public and graded, which is the only claim worth making.
What we'd flag
Read this page knowing what it can't do. It can't show you a line no book posted — coverage peaks around 87% for confirmed starters. It can't see a lineup change that happens after the projection publishes. And it can't tell you anything useful from one game, because the model is only measurable across a season-sized sample. For Lazaro specifically, we don't have a meaningful graded record on him yet.