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Orioles vs. Royals prediction: Baltimore favored to win Monday's MLB matchup [4/20/2026]

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Written by Jason Bevilacqua

Get Baltimore Orioles vs Kansas City Royals predictions for MLB Monday.

Baltimore Orioles-Kansas City Royals Predictions and Game Preview.
Baltimore Orioles vs. Kansas City Royals MLB prediction for Monday, April 20, 2026.

The Kansas City Royals and Baltimore Orioles will meet in MLB action at Kauffman Stadium on Monday, starting at 7:40 PM ET.

Seth Lugo (1-1, 1.48 ERA) will start on the mound for the Royals, while the Orioles are going with Kyle Bradish (1-2, 5.49 ERA).

Based on the latest simulations, Dimers' advanced MLB model gives the Orioles a slight edge with a 51% win probability over the Royals.

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

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

Using innovative machine learning and data, we have simulated the outcome of Monday's MLB game between the Orioles and Royals, forming part of our MLB predictions coverage.

Our famous predictive model gives the Orioles a 51% chance of defeating the Royals.

According to our model, the Royals (+1.5) have a 61% chance of covering the run line, while the over/under total of nine runs has a 53% chance of going over.

 

Orioles vs. Royals odds [4/20/2026]

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

Bet Type Orioles Royals
Run Line -1.5 (+145) +1.5 (-163)
Moneyline -115 -102
Total o9 (-105) u9 (-105)

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

Orioles vs. Royals 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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