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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 take on the St. Louis Cardinals in MLB action at Comerica Park on Sunday, beginning at 7:20 PM ET.

Keider Montero (0-0, 0.00 ERA) will be on the bump for the Tigers, while the Cardinals are going with Kyle Leahy (0-1, 7.20 ERA).

Based on the latest simulations, Dimers' proven MLB model projects 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 innovative machine learning and data, we have simulated the result of Sunday's MLB matchup between the Cardinals and Tigers, forming part of our MLB predictions coverage.

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

According to our model, the Cardinals (+1.5) have a 61% chance of covering the run line, while the 8-run over/under 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 (+165)
Moneyline +124 -140
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 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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