Today's matchup
Jonathan draws Baltimore Orioles on the road today. He shows up on 12 boards — rbis (85.6/100, #3), h+r+rbi (85/100, #3), hits (82.3/100, #9), singles (80.7/100, #4). We've got him at 0.6 on the rbis board. That's one of the stronger spots on the board today. Open any board row for the full breakdown of what's driving it.
Recent form
Jonathan is 9-for-40 (.225) over his last 10 games, in line with his .274 season line. No hot streak to fade, no slump to buy — the matchup is doing the work in his score, which is the normal case. The full game log is below.
What kind of hitter he is
Jonathan has 15 home runs and 33 extra-base hits in 463 at-bats this season. That profile is why he grades better on the total-bases and home-run boards than his batting average alone would suggest — those boards reward damage, not just contact. Park and weather matter more for a hitter like him too, and both feed the score.
He's a left-handed bat
Jonathan 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.
Floor and ceiling
Jonathan has recorded at least one hit in 83 of 122 games he's batted in — 68% — with 33 multi-hit games among them. That's about average — most regulars land somewhere near it.
Home and away
Jonathan is hitting .288 at home (67-for-233) and .261 on the road (60-for-230) across the season. There's not much in it, which is the normal case. Home-road splits get talked about far more than they hold up.
Against Baltimore Orioles
Jonathan is 7-for-27 (.259) against Baltimore Orioles in our log, over 7 games. Worth knowing, worth not over-reading. 27 at-bats against one club is a tiny sample, and the model treats it as a minor input next to what that pitching staff actually allows to his handedness. Batter-versus-pitcher history is the most quoted number in baseball and one of the least predictive.
His best game in the log
Jonathan's biggest night was Saturday, August 15, 2026 against BAL — 3-for-5. That's the ceiling, not the expectation. The projection you'll see on a board is closer to his typical game than his best one, which is the whole point of projecting rather than remembering.
How our calls on Jonathan have graded
We've ranked Jonathan a top-20 hitter matchup 33 times this season. He got a hit in 26 of them — 79%. That's the model reading him well, and it's the number we'd point to if you asked why we keep ranking him. It's also a season-sized sample on one player, which is smaller than it sounds; don't extrapolate it into a promise about tonight.
Where he lands across our boards
Jonathan was scored on 12 boards in that matchup, and the spread is the interesting part: rbis graded 85.6/100 while stolen bases 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
Jonathan 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.