Kentucky vs. Tennessee prediction: Why Tennessee is favored to win in college football Week 10 [2026]

Using powerful machine learning and data analysis, we have re-simulated the outcome of Kentucky vs. Tennessee, forming part of our college football predictions coverage.

Our famous predictive model gives Tennessee a 66.8% chance of defeating Kentucky. Our predicted final score has Tennessee winning 28-22. According to our model, Tennessee (-5.5) has a 51.7% chance of covering the spread, while the over/under total of 50.5 points has a 50.4% chance of going over.

Latest betting odds

We have sourced the best college football odds in America for this game, which are listed here:

• Spread: Kentucky +5.5 (+104) / Tennessee -5.5 (-108)
• Moneyline: Kentucky +200, Tennessee -194
• Total: Over/Under 50.5 (+113/-117)

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

Model picks and best bet

After matching our predictions against the best current odds, we can reveal our picks for each of the major markets in this matchup. Plus, find our best bet for the game below:

• Spread: Tennessee -5.5 @ 52¢ via Novig
• Moneyline: Tennessee @ 66¢ via Novig
• Total: Over 50.5 @ 47¢ via Novig

According to our college football best bets, the top play to make on this game is Over 50.5 total points.

These picks are based on our probabilities matched against the implied probabilities from the current available odds.

Game info & kickoff details

Saturday's college football game between Tennessee and Kentucky at Neyland Stadium is scheduled to commence at 6:15 PM ET.

• Who: Kentucky vs. Tennessee
• Date: Saturday, November 7, 2026
• Time: 6:15 PM ET/3:15 PM PT
• Venue: Neyland Stadium

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Conclusion

We predict Tennessee, with a 66.8% win probability, will likely beat Kentucky in Saturday's college football matchup.

Additional tools and information for betting safely can be found in our Responsible Gambling hub.

This article's predictions and probabilities are generated by Dimers' proprietary college football prediction model. Our editorial team reviews the resulting content for accuracy and presentation before publication.