Season line
0 starts, 0-0, 18.00 ERA, 2.33 WHIP, 3.0 innings. He's struck out 0 over that span, against 3 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 right-handed
Jedixson 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.
His last 4 starts
Jedixson has 3 strikeouts in 6.7 innings across his last 4 starts, a 4.0 K/9, with a 18.90 ERA over that stretch and 5 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.
How long he goes
Jedixson is averaging 1.7 innings a start across his last 4 outings, with a high of 3.7 and about 38 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.
How the model reads him
The number on Jedixson is about tonight, not about him. Strip out the name and the model is asking one question: what does this specific lineup do to this kind of arm, in this park, for as many batters as he's likely to face? That gets scored against every other starter on the slate. The combination math is proprietary and re-tuned every week. The grading isn't — it's all public, including the misses.
What we'd flag
Books don't price every player — coverage tops out around 87% even for confirmed starters, so "no line" here usually means no book posted one rather than a gap in our data. Lineups change late, and an afternoon projection can be stale by first pitch. And the model is tuned across hundreds of graded outcomes, so one game tells you nothing about whether it's working.