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

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Written by Jason Bevilacqua

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

San Diego Padres-Boston Red Sox Predictions and Game Preview.
San Diego Padres vs. Boston Red Sox MLB prediction for Saturday, April 4, 2026.

The Boston Red Sox and San Diego Padres are scheduled to meet in MLB action at Fenway Park on Saturday, starting at 4:10 PM ET.

Connelly Early (0-0, 1.69 ERA) will get the ball for the Red Sox, pitting him against Randy Vásquez (1-0, 0.00 ERA) for the Padres.

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

This article offers a closer look at Saturday's matchup, including best bets and picks.

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

Using powerful machine learning and data, we have simulated the outcome of Saturday's MLB game between the Padres and Red Sox, forming part of our MLB predictions coverage.

Our famous predictive model currently gives the Red Sox a 57% chance of beating the Padres.

According to our model, the Padres (+1.5) have a 60% chance of covering the run line, while the over/under total of 7.5 runs has a 56% chance of going over.

 

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

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 (-175) -1.5 (+153)
Moneyline +125 -144
Total o7.5 (-105) u7.5 (-110)

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 probabilities 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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