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UConn vs. Duke prediction: Duke favored to win men's March Madness Elite Eight matchup [3/29/2026]

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Written by Jason Bevilacqua

Get UConn vs Duke predictions for college basketball Sunday.

UConn-Duke Predictions and Game Preview.
UConn vs. Duke March Madness prediction for Sunday, March 29, 2026.

Duke will take on UConn in March Madness Elite Eight action at Capital One Arena on Sunday, starting at 5:05 PM ET.

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

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

UConn vs. Duke prediction: Who will win and advance to the Final Four?

Using powerful machine learning and statistical analysis, we have simulated the result of Sunday's NCAAM Tournament matchup between UConn and Duke, forming part of our men's college basketball predictions coverage.

Our independent predictive model currently gives Duke a 73% chance of defeating Connecticut. Our predicted final score has Duke winning 72-66.

According to our model, Duke (-5.5) has a 55% chance of covering the spread, while the over/under total of 135.5 points has a 55% chance of going over.

 

UConn vs. Duke odds

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

Bet Type UConn Duke
Spread +5.5 (-115) -5.5 (-102)
Moneyline +200 -230
Total o135.5 (-105) u135.5 (-110)

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

UConn vs. Duke picks and best bet

After matching our probabilities 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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