Today's matchup
Justin draws Tampa Bay Rays on the road today. He shows up on 6 boards — outs recorded (64.8/100, #18), walks allowed (62.5/100, #11), strikeouts (52.1/100, #16), earned runs (33.4/100, #24). We've got him at 15 on the outs recorded board. Open any board row for the full breakdown of what's driving it.
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.
Where he lands across our boards
Justin was scored on 6 boards in that matchup, and the spread is the interesting part: outs recorded graded 64.8/100 while hits allowed came in at 9.7/100. 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
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.