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Carlton Blues vs. Collingwood Magpies prediction: Why Magpies are favored to win Thursday's AFL Round 6 matchup [4/16/2026]

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Written by Jason Bevilacqua

Get Carlton Blues vs Collingwood Magpies predictions for AFL Thursday.

Carlton Blues-Collingwood Magpies Predictions and Game Preview.
Carlton Blues vs. Collingwood Magpies AFL prediction for Thursday, April 16, 2026.

The Carlton Blues and Collingwood Magpies are scheduled to meet in the Australian Football League (AFL) at Melbourne Cricket Ground on Thursday, starting at 5:30 AM ET.

Based on the latest simulations, Dimers' advanced AFL model projects the Magpies as the most likely winner of the Magpies vs. Blues game.

This article offers a closer look at Thursday's matchup, including best bets and picks.

Blues vs. Magpies prediction: Who will win Round 6 game?

Using powerful machine learning and data analysis, we have simulated the result of Thursday's AFL matchup between the Magpies and Blues, forming part of our AFL predictions coverage.

Our leading predictive model gives the Magpies a 66% chance of beating the Blues.

According to our model, the Magpies (-8.5) have a 56% chance of covering the spread, while the over/under total of 155.5 points has an equal 50-50 chance of hitting.

 

Collingwood vs. Carlton odds

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

Bet Type Magpies Blues
H2H (Moneyline) -179 +140
Line (Spread) -8.5 (-115) +8.5 (-115)
Total o155.5 (-115) u155.5 (-115)

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

Magpies vs. Blues picks and best bet

After matching our probabilities 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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