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Reds vs. Rangers prediction: Why Texas is favored to win Sunday's MLB matchup [4/5/2026]

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Written by Jason Bevilacqua

Get Cincinnati Reds vs Texas Rangers predictions for MLB Sunday.

Cincinnati Reds-Texas Rangers Predictions and Game Preview.
Cincinnati Reds vs. Texas Rangers MLB prediction for Sunday, April 5, 2026.

The Texas Rangers will take on the Cincinnati Reds, in what is expected to be a close MLB game, at Globe Life Field on Sunday, starting at 2:35 PM ET.

The Rangers will have Jack Leiter (1-0, 3.00 ERA) as their starting pitcher, while the Reds are going with Chase Burns (1-0, 0.00 ERA).

Based on updated simulation results, Dimers' proven MLB model gives the Rangers a slight edge with a 52% win probability over the Reds.

This article offers further insights into Sunday's Reds vs. Rangers matchup, including best bets and picks.

Reds vs. Rangers prediction: Who will win and cover?

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

Our independent predictive model gives the Rangers a 52% chance of beating the Reds.

According to our model, the Reds (+1.5) have a 65% chance of covering the run line, while the over/under total of 7.5 runs has a 54% chance of going over.

 

Reds vs. Rangers odds [4/5/2026]

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

Bet Type Reds Rangers
Run Line +1.5 (-201) -1.5 (+164)
Moneyline +109 -122
Total o7.5 (-102) u7.5 (-115)

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

Reds vs. Rangers 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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