Best MLB Hits Matchups — Tuesday, September 8, 2026
Top hits spot: Elly De La Cruz
Elly De La Cruz (CIN) tops the board at 100, facing LHP Tarik Skubal. The righty is hitting .306 H/PA against lefties this year — and .435 over the last two weeks, a strong bat that turns into a hit in about 27% of his trips. And Tarik Skubal has been getting tattooed by lefties lately — .325 hits per batter faced. One catch: the bullpen behind him has been stingy to that side late. He's hitting in a spot worth about 4.5 trips, so the volume's there. He's just .000 in 3 career PA against Tarik Skubal, but that's a tiny sample and the matchup says regression. It all sets up in a park that leans hitter.
The rest of the top of the board
- Brett Bateman (TOR) (100) vs RHP Jack Perkins: an excellent bat at .278 into an arm mostly holding up against the same side (.214).
- Yandy Díaz (TB) (97) vs RHP AJ Smith-Shawver: a strong bat at .269 into an arm tough on the same side (.176).
- Jackson Chourio (MIL) (91) vs LHP David Peterson: a strong bat at .270 into an arm getting tattooed by the same side (.306).
- Dane Myers (CIN) (89) vs LHP Tarik Skubal: a strong bat at .256 into an arm getting tattooed by the same side (.325).
- Jonah Cox (SF) (88) vs LHP Quinn Mathews: a strong bat at .261 into an arm mostly holding up against the same side (.210).
- Michael Harris II (ATL) (86) vs RHP Freddy Peralta: a strong bat at .260 into an arm tough on the same side (.111).
- Nathan Lukes (TOR) (85) vs RHP Jack Perkins: a strong bat at .256 into an arm mostly holding up against the same side (.214), hot bat.
Platoon edges to target
- Elly De La Cruz (CIN) — righty bat vs LHP, .306 against lefties this year.
- Brett Bateman (TOR) — lefty bat vs RHP, .289 against righties this year.
- Jackson Chourio (MIL) — righty bat vs LHP, .262 against lefties this year.
- Dane Myers (CIN) — righty bat vs LHP, .254 against lefties this year.
- Jonah Cox (SF) — righty bat vs LHP, .273 against lefties this year.
Hot bats and bounce-back spots
Swinging hot bats: Elly De La Cruz (CIN), Nathan Lukes (TOR), TJ Rumfield (COL), Dominic Canzone (SEA), Iván Herrera (STL), and Ketel Marte (AZ). Cold but due to bounce back: Jeremy Peña (HOU), Miguel Rojas (LAD), Bryce Harper (PHI), Ty France (SD), and Konnor Griffin (PIT).
How it played out
7 of the top 10 hits matchups landed at least one hit. Top play Elly De La Cruz finished with 2 hits. 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 hits matchups
Each score (0–100) starts with the hitter's hits 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 hits board is
The Hits board projects the chance a hitter records at least one hit, matchup-adjusted. 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 contact rate vs this pitcher's hand.
- The starter's hit-suppression and the park.
- Recent form, without over-reacting to a hot or cold week.
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?
Hits is an efficient market — the book prices a 1+ hit line about as well as we do, so we publish the WizLine as graded transparency, not a play. Our projection is sharp (the ranking is well-calibrated); the book just isn't leaving room on it.
How to use it
Use it to see who the model likes to square one up, and how that squares with the price — but there's no systematic edge here, so treat it as research, not a lean.
Everything here is a research signal from the model, graded in public — not betting advice, and no outcome is guaranteed.
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