Best MLB Home Runs Matchups — Tuesday, June 23, 2026
Top home runs spot: Juan Soto
Juan Soto (NYM) tops the board at 100, facing RHP Edward Cabrera. The lefty is going deep on .076 HR/PA against righties this year — and .073 over the last two weeks, big-time bat that turns into a home run in about 5% of his trips. And Edward Cabrera has been getting taken deep by righties lately — .097 home runs per batter faced. The bullpen behind him is roughly average to that side. He's hitting in a spot worth about 4.5 trips, so the volume's there. He's just .071 in 14 career PA against Edward Cabrera, but that's a tiny sample and the matchup says regression. It all sets up in a neutral park, though the weather fights it.
The rest of the top of the board
- Pete Alonso (BAL) (100) vs RHP Ryan Johnson: real bat at .047 into an arm getting taken deep by the same side (.091).
- Yordan Alvarez (HOU) (91) vs RHP Shane Bieber: real bat at .046 into an arm with little track record against the same side.
- Ben Rice (NYY) (91) vs RHP Casey Mize: real bat at .048 into an arm keeping the ball in the park against the same side (.000).
- Brandon Lowe (PIT) (85) vs RHP George Kirby: real bat at .046 into an arm leaking power to the same side (.038), hot bat.
- Hunter Goodman (COL) (84) vs RHP Sonny Gray: real bat at .050 into an arm homer-prone to the same side (.040).
- Pete Crow-Armstrong (CHC) (73) vs RHP Kodai Senga: real bat at .043 into an arm keeping the ball in the park against the same side (.000), hot bat.
- Shohei Ohtani (LAD) (69) vs LHP Kendry Rojas: real bat at .042 into an arm with little track record against the same side.
Platoon edges to target
- Juan Soto (NYM) — lefty bat vs RHP, .076 against righties this year.
- Yordan Alvarez (HOU) — lefty bat vs RHP, .070 against righties this year.
- Ben Rice (NYY) — lefty bat vs RHP, .080 against righties this year.
- Brandon Lowe (PIT) — lefty bat vs RHP, .062 against righties this year.
- Pete Crow-Armstrong (CHC) — lefty bat vs RHP, .054 against righties this year.
How it played out
2 of the top 10 home runs matchups landed at least one home run. Top play Juan Soto finished with 0 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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