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Pirates vs. Rangers prediction: Why Texas is favored to win Thursday's MLB matchup [4/23/2026]

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Written by Jason Bevilacqua

Get Pittsburgh Pirates vs Texas Rangers predictions for MLB Thursday.

Pittsburgh Pirates-Texas Rangers Predictions and Game Preview.
Pittsburgh Pirates vs. Texas Rangers MLB prediction for Thursday, April 23, 2026.

The Texas Rangers and Pittsburgh Pirates will meet in MLB action at Globe Life Field on Thursday, starting at 8:05 PM ET.

Jacob deGrom (1-0, 2.29 ERA) will get the ball for the Rangers, while the Pirates have opted for Bubba Chandler (1-1, 3.15 ERA).

Based on updated simulation results, Dimers' advanced MLB model predicts the Rangers as the most likely winner of the Pirates vs. Rangers game.

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

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

Using powerful machine learning and statistical analysis, we have simulated the result of Thursday's MLB matchup between the Pirates and Rangers, forming part of our MLB predictions coverage.

Our leading predictive model gives the Rangers a 60% chance of beating the Pirates.

According to our model, the Pirates (+1.5) have a 58% chance of covering the run line, while the 7.5-run over/under has a 56% chance of going over.

 

Pirates vs. Rangers odds [4/23/2026]

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

Bet Type Pirates Rangers
Run Line +1.5 (-163) -1.5 (+140)
Moneyline +136 -160
Total o7.5 (-111) u7.5 (-105)

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

Pirates vs. Rangers picks and best bet

After matching our probabilities 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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