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Padres vs. Red Sox prediction: Boston favored to win Sunday's MLB matchup [4/5/2026]

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Written by Jason Bevilacqua

Get San Diego Padres vs Boston Red Sox predictions for MLB Sunday.

San Diego Padres-Boston Red Sox Predictions and Game Preview.
San Diego Padres vs. Boston Red Sox MLB prediction for Sunday, April 5, 2026.

The Boston Red Sox and San Diego Padres will meet in MLB action at Fenway Park on Sunday, starting at 1:35 PM ET.

The Red Sox will have Ranger Suarez (0-1, 8.31 ERA) as their starting pitcher, pitting him against Walker Buehler (0-1, 6.75 ERA) for the Padres.

Based on updated simulations, Dimers' proven MLB model projects the Red Sox as the most likely winner of the Padres vs. Red Sox game.

This article offers an expanded analysis of Sunday's matchup, including best bets and picks.

Padres vs. Red Sox prediction: Who will win and cover?

Using trusted machine learning and data analysis, we have simulated the result of Sunday's MLB game between the Padres and Red Sox, forming part of our MLB predictions coverage.

Our independent predictive model currently gives the Red Sox a 59% chance of beating the Padres.

According to our model, the Padres (+1.5) have a 58% chance of covering the run line, while the over/under total of 9.5 runs has a 51% chance of staying under.

 

Padres vs. Red Sox odds [4/5/2026]

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

Bet Type Padres Red Sox
Run Line +1.5 (-150) -1.5 (+129)
Moneyline +130 -154
Total o9.5 (+104) u9.5 (-118)

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

Padres vs. Red Sox picks and best bet

After matching our predictions against the best current 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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