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North Melbourne Kangaroos vs. Richmond Tigers prediction: Kangaroos favored to win Saturday's AFL Round 6 matchup [4/18/2026]

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Written by Jason Bevilacqua

Get North Melbourne Kangaroos vs Richmond Tigers predictions for AFL Saturday.

North Melbourne Kangaroos-Richmond Tigers Predictions and Game Preview.
North Melbourne Kangaroos vs. Richmond Tigers AFL prediction for Saturday, April 18, 2026.

The North Melbourne Kangaroos will take on the Richmond Tigers in Australian Football League (AFL) action at Marvel Stadium on Saturday, beginning at 11:10 PM ET.

Based on updated simulation results, Dimers' advanced AFL model predicts the Kangaroos as the most likely winner of the Tigers vs. Kangaroos game.

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

Kangaroos vs. Tigers prediction: Who will win Round 6 game?

Using powerful machine learning and data, we have simulated the outcome of Saturday's AFL game between the Tigers and Kangaroos, forming part of our AFL predictions coverage.

Our leading predictive model gives the Kangaroos a 74% chance of beating the Tigers.

According to our model, the Tigers (+26.5) have a 59% chance of covering the spread, while the over/under total of 182.5 points has a 52% chance of staying under.

 

Richmond vs. North Melbourne odds

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

Bet Type Tigers Kangaroos
H2H (Moneyline) +290 -385
Line (Spread) +26.5 (-115) -26.5 (-115)
Total o182.5 (-115) u182.5 (-115)

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

Tigers vs. Kangaroos 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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