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Yankees vs. Mariners prediction: Seattle favored to win Wednesday's MLB matchup [4/1/2026]

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Written by Jason Bevilacqua

Get New York Yankees vs Seattle Mariners predictions for MLB Wednesday.

New York Yankees-Seattle Mariners Predictions and Game Preview.
New York Yankees vs. Seattle Mariners MLB prediction for Wednesday, April 1, 2026.

The Seattle Mariners and New York Yankees are scheduled to meet in MLB action at T-Mobile Park on Wednesday, starting at 4:10 PM ET.

George Kirby (1-0, 1.50 ERA) will be the starting pitcher for the Mariners, pitting him against Cam Schlittler (1-0, 0.00 ERA) for the Yankees.

Based on the latest simulation results, Dimers' advanced MLB model gives the Mariners a slight edge with a 54% win probability over the Yankees.

This article offers an expanded analysis of Wednesday's Yankees vs. Mariners matchup, including best bets and picks.

Yankees vs. Mariners prediction: Who will win?

Using trusted machine learning and data analysis, we have simulated the result of Wednesday's MLB game between the Yankees and Mariners, forming part of our MLB predictions coverage.

Our leading predictive model currently gives the Mariners a 54% chance of defeating the Yankees.

According to our model, the over/under total of seven runs also has an equal 50-50 chance of hitting.

 

Yankees vs. Mariners odds [4/1/2026]

We have sourced the best MLB odds in America for this game, which are listed here:

Bet Type Yankees Mariners
Moneyline +100 -118
Total o7 (+102) u7 (-120)

All odds are correct at the time of publication and are subject to change.

Yankees vs. Mariners picks and best bet

After matching our probabilities against the best available odds, we can reveal our picks for each of the major markets. Plus, find our best bet for the game below:

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Written by
Jason Bevilacqua
Head of Content and Communities

Jason Bevilacqua, an expert in MLB, NHL, NBA, MLS, and NFL, delivers game previews, best bets, and props. Since 2020, as Head of Social and Community at Dimers, he has contributed insightful articles utilizing data-driven models and simulations.

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