Today's matchup
Mitch draws Cincinnati Reds at home today. He shows up on 6 boards — outs recorded (67.4/100, #9), earned runs (54.1/100, #3), hits allowed (40.2/100, #3), fantasy points (13.7/100, #9). We've got him at 13.9 on the outs recorded board. Open any board row for the full breakdown of what's driving it.
Season line
8 starts, 1-2, 4.17 ERA, 1.47 WHIP, 36.2 innings. He's struck out 25 over that span, against 22 walks. 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 left-handed
Mitch is a left-hander, and that matters more for a starter than it does for most hitters. Lineups get built specifically to attack lefties — managers stack right-handed bats against them — so the lineup he draws on a given night swings his projection further than a righty's would. The model prices the actual lineup rather than a season-long rate for exactly that reason. A stacked righty lineup is the spot where his strikeout projection comes down hardest.
His last 5 starts
Mitch has 18 strikeouts in 26.0 innings across his last 5 starts, a 6.2 K/9, with a 3.81 ERA over that stretch and 16 walks. Strikeouts are the one pitcher market where our model has found real, graded room — projected workload and the specific lineup he draws move that number more than a season-long rate does. Start-by-start detail is in the log below.
Strikeouts and control
Mitch has 25 strikeouts and 22 walks in 36.7 innings this season — 6.1 K/9 against 5.4 BB/9, a 1.1-to-1 ratio. That walk rate is the risk in every projection on him. Free passes run up pitch counts, and a short outing caps the strikeout and outs numbers no matter how favourable the lineup looks. His floor is genuinely lower than his stuff suggests.
What his floor looks like
Mitch has made 8 starts this season, averaging 4.6 innings with a 4.17 ERA across them. In the 8 starts we hold detailed lines for, he got through five or more innings 4 times. That ratio is the honest read on his floor, and it's what separates an arm worth projecting on the outs board from one whose good starts are unpredictable enough that the market prices them better than we do.
How long he goes
Mitch is averaging 4.6 innings a start across his last 8 outings, with a high of 7.0 and about 76 pitches a night. He's not going deep, and that caps the strikeout and outs projections no matter how good the matchup looks — you can't strike out hitters you don't face. Expected batters faced is one of the bigger inputs to his score for exactly this reason.
His best start in the log
Mitch's high-water mark was Wednesday, August 5, 2026 against SD: 9 strikeouts over 7.0 innings, 0 earned runs. One start doesn't set a projection — the model works off the blend of his recent work against the specific lineup in front of him — but it does show the ceiling when the matchup lines up.
Where he lands across our boards
Mitch was scored on 6 boards in that matchup, and the spread is the interesting part: outs recorded graded 67.4/100 while walks allowed came in at 0/100. 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
Mitch'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.