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Tulane vs. Memphis prediction: Why Memphis is favored to win men's college basketball matchup [2/1/2026]

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Written by Jason Bevilacqua

Get Tulane vs Memphis predictions for college basketball Sunday.

Tulane-Memphis Predictions and Game Preview.
Tulane vs. Memphis men's college basketball prediction for Sunday, February 1, 2026.

Memphis will square off with Tulane in men's college basketball action at FedExForum on Sunday, beginning at 3:00 PM ET.

Based on updated simulations, Dimers' advanced college basketball model predicts Memphis as the most likely winner of the Tulane vs. Memphis game.

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

Tulane vs. Memphis prediction: Who will win and cover?

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

Our independent predictive model currently gives Memphis an 80% chance of defeating Tulane. Our predicted final score has Memphis winning 79-70.

According to our model, Tulane (+9.5) has a 52% chance of covering the spread, while the over/under total of 146.5 points has a 58% chance of staying under.

 

Tulane vs. Memphis odds

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

Bet Type Tulane Memphis
Spread +9.5 (-108) -9.5 (-112)
Moneyline +375 -500
Total o146.5 (-112) u146.5 (-108)

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

Tulane vs. Memphis picks and best bet

After matching our predictions against the best available 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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