Season line
1 start, 0-0, 3.00 ERA, 1.00 WHIP, 3.0 innings. He's struck out 2 over that span, against 1 walk. This is the baseline the matchup adjusts from — the model starts here and then asks what the specific lineup he's facing does to strikeout arms, how the park plays, and how deep he's likely to go.
He throws right-handed
Justin throws right-handed, which is the ordinary case and means fewer wild swings in the lineup he faces. Opposing managers aren't restacking against him the way they would a left-hander, so his projection moves more on the quality of the lineup than on its handedness. What does move it: how deep he's been going lately, and whether the lineup in front of him strikes out.
How the model reads him
Justin's score comes from the matchup, not his reputation. The model weighs how the opposing lineup handles his handedness, how many batters he's likely to face given recent workload, the park, and the weather — then normalizes 0–100 against every other arm on the slate. How those factors are weighted is our own, and it re-tunes weekly against real outcomes. We publish the score and grade it in public; that's the half you can check.
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.