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Tulsa vs. UAB prediction: UAB favored to win men's college basketball matchup [1/18/2026]

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Written by Jason Bevilacqua

Get Tulsa vs UAB predictions for college basketball Sunday.

Tulsa-UAB Predictions and Game Preview.
Tulsa vs. UAB men's college basketball prediction for Sunday, January 18, 2026.

UAB will face Tulsa in men's college basketball action at Bartow Arena on Sunday, commencing at 3:00 PM ET.

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

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

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

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

Our famous predictive model currently gives UAB a 58% chance of defeating Tulsa. Our predicted final score has UAB winning 79-77.

According to our model, the bookies have got it right, and Tulsa and UAB each have a 50% chance of covering the spread, while the over/under total of 158.5 points has a 52% chance of staying under.

 

Tulsa vs. UAB odds

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

Bet Type Tulsa UAB
Spread +1.5 (-102) -1.5 (-118)
Moneyline +114 -130
Total o158.5 (-108) u158.5 (-110)

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

Tulsa 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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