Best MLB Home Runs Matchups — Thursday, August 13, 2026
Top home runs spot: Kyle Schwarber
Kyle Schwarber (PHI) tops the board at 100, facing RHP Taj Bradley. The lefty is going deep on .070 HR/PA against righties this year — and .042 over the last two weeks, elite bat that turns into a home run in about 7% of his trips. And Taj Bradley has been keeping the ball in the park against righties lately — .000 home runs per batter faced. The bullpen behind him is roughly average to that side. He's hitting in a spot worth about 4.7 trips, so the volume's there. He's owned Taj Bradley too — .500 across 2 career trips. It all sets up in a neutral park, though the weather fights it.
The rest of the top of the board
- Shohei Ohtani (LAD) (100) vs LHP Shane Drohan: big-time bat at .061 into an arm with little track record against the same side.
- Griffin Conine (MIA) (89) vs RHP Braxton Ashcraft: big-time bat at .063 into an arm keeping the ball in the park against the same side (.000).
- Ben Rice (NYY) (85) vs RHP Logan Gilbert: big-time bat at .053 into an arm getting taken deep by the same side (.067).
- Byron Buxton (MIN) (85) vs RHP Aaron Nola: elite bat at .067 into an arm getting taken deep by the same side (.111), due to bounce back.
- Bryce Harper (PHI) (80) vs RHP Taj Bradley: big-time bat at .052 into an arm keeping the ball in the park against the same side (.000).
- Brandon Lowe (PIT) (78) vs RHP Tyler Phillips: big-time bat at .057 into an arm keeping the ball in the park against the same side (.000).
- Joc Pederson (TEX) (73) vs RHP Walbert Ureña: big-time bat at .050 into an arm keeping the ball in the park against the same side (.000).
Platoon edges to target
- Kyle Schwarber (PHI) — lefty bat vs RHP, .070 against righties this year.
- Griffin Conine (MIA) — lefty bat vs RHP, .088 against righties this year.
- Ben Rice (NYY) — lefty bat vs RHP, .069 against righties this year.
- Bryce Harper (PHI) — lefty bat vs RHP, .065 against righties this year.
- Brandon Lowe (PIT) — lefty bat vs RHP, .060 against righties this year.
How it played out
1 of the top 10 home runs matchups landed at least one home run. Top play Kyle Schwarber finished with 2 home runs. We post the result next to every projection so you can grade the board yourself — and so the model gets re-tuned against what actually happened.
How to read these home runs matchups
Each score (0–100) starts with the hitter's home runs per plate appearance against the hand he's facing — weighted toward the last two weeks, then the season, then a two-year baseline. Then it layers in the bullpen, his spot in the order, and park and weather. Higher means more of it points his way. It's context, not a lock — a great spot still goes 0-for-4 sometimes, and a tough one runs into one. The edge is in stacking the odds, and since we grade every board, you can see how often the top of the list delivers.
What the home runs board is
The Home Runs board projects the chance a hitter goes deep, park and matchup baked in. One statistical model scores every matchup on the slate the same way — a star and a backup judged on the matchup in front of them, not their name — and every number is graded against the real box score once the games go final.
How the model gets its number
It isn't a gut call or a name game. The projection is built from a few things:
- The hitter's power vs the pitcher's hand.
- The park's home-run environment and the wind.
- How hard and how often he hits it in the air.
Those pieces combine into one number, and the model re-tunes itself weekly against how its past calls actually landed.
Is there a betting edge here?
Home runs is one of the most efficiently priced props in the sport — every config we've backtested loses to the line, and our disagreements point away from the book's mistakes, not toward them. So HR is a transparency board: our projection, graded, no play attached. The honest read is the book is simply sharper here.
How to use it
Read it for who's in a live spot to run into one — but don't bet our number against the line; there's no edge to press.
Everything here is a research signal from the model, graded in public — not betting advice, and no outcome is guaranteed.
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