Best MLB Home Runs Matchups — Thursday, June 25, 2026
Top home runs spot: Kyle Schwarber
Kyle Schwarber (PHI) tops the board at 100, facing RHP Cade Cavalli. The lefty is going deep on .093 HR/PA against righties this year — and .111 over the last two weeks, elite bat that turns into a home run in about 7% of his trips. And Cade Cavalli has been homer-prone to righties lately — .040 home runs per batter faced. The bullpen behind him hasn't been any better to that side, so there's no relief late. He's hitting in a spot worth about 4.5 trips, so the volume's there. He's just .111 in 9 career PA against Cade Cavalli, but that's a tiny sample and the matchup says regression. It all sets up in a neutral park, weather helping.
The rest of the top of the board
- Nick Kurtz (ATH) (100) vs RHP Landen Roupp: big-time bat at .060 into an arm homer-prone to the same side (.043), due to bounce back.
- Pete Crow-Armstrong (CHC) (100) vs RHP Freddy Peralta: big-time bat at .052 into an arm getting taken deep by the same side (.068), hot bat.
- Paul Goldschmidt (NYY) (93) vs LHP Connelly Early: real bat at .050 into an arm getting taken deep by the same side (.059).
- Yordan Alvarez (HOU) (82) vs RHP Troy Melton: big-time bat at .055 into an arm getting taken deep by the same side (.121).
- Brandon Lowe (PIT) (76) vs RHP Bryce Miller: big-time bat at .056 into an arm homer-prone to the same side (.040), hot bat.
- Corey Seager (TEX) (74) vs RHP Kevin Gausman: real bat at .050 into an arm homer-prone to the same side (.040).
- Ian Happ (CHC) (68) vs RHP Freddy Peralta: real bat at .043 into an arm getting taken deep by the same side (.068), hot bat.
Platoon edges to target
- Kyle Schwarber (PHI) — lefty bat vs RHP, .093 against righties this year.
- Nick Kurtz (ATH) — lefty bat vs RHP, .054 against righties this year.
- Pete Crow-Armstrong (CHC) — lefty bat vs RHP, .058 against righties this year.
- Paul Goldschmidt (NYY) — righty bat vs LHP, .071 against lefties this year.
- Yordan Alvarez (HOU) — lefty bat vs RHP, .068 against righties this year.
How it played out
2 of the top 10 home runs matchups landed at least one home run. Top play Kyle Schwarber 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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