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Cardinals vs. Tigers prediction: Detroit favored to win Sunday's MLB matchup [4/5/2026]

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Written by Jason Bevilacqua

Get St. Louis Cardinals vs Detroit Tigers predictions for MLB Sunday.

St. Louis Cardinals-Detroit Tigers Predictions and Game Preview.
St. Louis Cardinals vs. Detroit Tigers MLB prediction for Sunday, April 5, 2026.

The Detroit Tigers will square off with the St. Louis Cardinals in MLB action at Comerica Park on Sunday, starting at 7:20 PM ET.

The Tigers will start Keider Montero (0-0, 0.00 ERA), while the Cardinals have opted for Kyle Leahy (0-1, 7.20 ERA).

Based on updated simulations, Dimers' advanced MLB model predicts the Tigers as the most likely winner of the Cardinals vs. Tigers game.

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

Cardinals vs. Tigers prediction: Who will win and cover?

Using trusted machine learning and data analysis, we have simulated the result of Sunday's MLB game between the Cardinals and Tigers, forming part of our MLB predictions coverage.

Our leading predictive model gives the Tigers a 57% chance of defeating the Cardinals.

According to our model, the Cardinals (+1.5) have a 61% chance of covering the run line, while the over/under total of eight runs has an equal 50-50 chance of hitting.

 

Cardinals vs. Tigers odds [4/5/2026]

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

Bet Type Cardinals Tigers
Run Line +1.5 (-180) -1.5 (+155)
Moneyline +124 -142
Total o8 (-110) u8 (-105)

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

Cardinals vs. Tigers 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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