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Giants vs. Orioles prediction: Baltimore favored to win Friday's MLB matchup [4/10/2026]

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Written by Jason Bevilacqua

Get San Francisco Giants vs Baltimore Orioles predictions for MLB Friday.

San Francisco Giants-Baltimore Orioles Predictions and Game Preview.
San Francisco Giants vs. Baltimore Orioles MLB prediction for Friday, April 10, 2026.

MLB action continues on Friday at 7:15 PM ET as the San Francisco Giants take on the Baltimore Orioles at Oriole Park at Camden Yards.

The Orioles will have Shane Baz (0-0, 4.09 ERA) as their starting pitcher, while the Giants will counter with Landen Roupp (1-1, 4.22 ERA).

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

This article offers a closer look at Friday's Giants vs. Orioles matchup, including best bets and picks.

Giants vs. Orioles prediction: Who will win and cover?

Using powerful machine learning and data, we have simulated the outcome of Friday's MLB matchup between the Giants and Orioles, forming part of our MLB predictions coverage.

Our famous predictive model currently gives the Orioles a 54% chance of beating the Giants.

According to our model, the Giants (+1.5) have a 63% chance of covering the run line, while the 8.5-run over/under has a 53% chance of going over.

 

Giants vs. Orioles odds [4/10/2026]

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

Bet Type Giants Orioles
Run Line +1.5 (-193) -1.5 (+165)
Moneyline +109 -118
Total o8.5 (+100) u8.5 (-117)

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

Giants vs. Orioles picks and best bet

After matching our predictions 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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