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Rice vs. UAB prediction: UAB favored to win men's college basketball matchup [2/8/2026]

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Written by Jason Bevilacqua

Get Rice vs UAB predictions for college basketball Sunday.

Rice-UAB Predictions and Game Preview.
Rice vs. UAB men's college basketball prediction for Sunday, February 8, 2026.

UAB and Rice are scheduled to meet in men's college basketball action at Bartow Arena on Sunday, starting at 3:00 PM ET.

Based on the latest simulation results, Dimers' advanced college basketball model predicts UAB as the most likely winner of the Rice vs. UAB game.

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

Rice vs. UAB prediction: Who will win and cover?

Using innovative machine learning and statistical analysis, we have simulated the outcome of Sunday's NCAA basketball matchup between Rice and UAB 10,000 times as part of our men's college basketball predictions coverage.

Our leading predictive model currently gives UAB a 79% chance of beating Rice. Our predicted final score has UAB winning 80-71.

According to our model, the bookmakers have got it right, and Rice and UAB each have a 50% chance of covering the spread, while the 152.5-point over/under also has an equal 50-50 chance of hitting.

 

Rice vs. UAB odds

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

Bet Type Rice UAB
Spread +8.5 (-115) -8.5 (-102)
Moneyline +320 -375
Total o152.5 (-110) u152.5 (-105)

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

Rice vs. UAB 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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