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LSU vs. Texas Tech prediction: Why Texas Tech is favored to win men's college basketball matchup [12/7/2025]

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Written by Jason Bevilacqua

Get LSU vs Texas Tech predictions for college basketball Sunday.

LSU-Texas Tech Predictions and Game Preview.
LSU vs. Texas Tech men's college basketball prediction for Sunday, December 7, 2025.

Texas Tech and LSU will meet in men's college basketball action at Dickies Arena on Sunday, with tipoff at 3:00PM ET.

Based on the latest simulations, Dimers' advanced college basketball model predicts Texas Tech as the most likely winner of the LSU vs. Texas Tech game.

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

LSU vs. Texas Tech prediction: Who will win and cover?

Using trusted machine learning and data, we have simulated the result of Sunday's NCAA basketball matchup between LSU and Texas Tech 10,000 times as part of our men's college basketball predictions coverage.

Our famous predictive model currently gives Texas Tech a 66% chance of beating LSU. Our predicted final score has Texas Tech winning 79-74.

According to our model, LSU (+5.5) has a 54% chance of covering the spread, while the 153.5-point over/under has an equal 50-50 chance of hitting.

 

LSU vs. Texas Tech odds

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

Bet Type LSU Texas Tech
Spread +5.5 (-110) -5.5 (-110)
Moneyline +185 -225
Total o153.5 (-115) u153.5 (-105)

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

LSU vs. Texas Tech 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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