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Marist vs. Rider prediction: Why Marist is favored to win men's college basketball matchup [1/11/2026]

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Written by Jason Bevilacqua

Get Marist vs Rider predictions for college basketball Sunday.

Marist-Rider Predictions and Game Preview.
Marist vs. Rider men's college basketball prediction for Sunday, January 11, 2026.

Rider will square off with the favored Marist in men's college basketball action at Alumni Gymnasium on Sunday, starting at 2:00 PM ET.

Based on updated simulations, Dimers' proven college basketball model projects Marist as the most likely winner of the Marist vs. Rider game.

This article offers further insights into Sunday's matchup, including an expanded look at the predicted score, best bets and picks.

Marist vs. Rider prediction: Who will win and cover?

Using innovative machine learning and data, we have simulated the outcome of Sunday's NCAA basketball game between Marist and Rider 10,000 times as part of our men's college basketball predictions coverage.

Our leading predictive model currently gives Marist an 85% chance of beating Rider. Our predicted final score has Marist winning 73-63.

According to our model, Rider (+10.5) has a 51% chance of covering the spread, while the over/under total of 131.5 points has a 56% chance of going over.

 

Marist vs. Rider odds

We have researched the best men's college basketball odds in America for this game, which are listed here:

Bet Type Marist Rider
Spread -10.5 (-115) +10.5 (+100)
Moneyline -650 +500
Total o131.5 (-110) u131.5 (-108)

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

Marist vs. Rider 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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