California vs. UNLV prediction favors UNLV to win in college football Week 5 [2026]

Using trusted machine learning and data, we have re-simulated the result of California vs. UNLV, forming part of our college football predictions coverage.

Our famous predictive model currently gives UNLV a 51.7% chance of defeating California. Our predicted final score has UNLV winning 26-25. According to our model, UNLV (+0.5) has a 55.5% chance of covering the spread, while the 52.5-point over/under has a 51.8% 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: California -0.5 (-110) / UNLV +0.5 (-112)
• Moneyline: California -102, UNLV -111
• Total: Over/Under 52.5 (-105/-109)

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

Model picks and best bet

After matching our probabilities against the best available 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: UNLV +0.5 @ -112 via BetRivers
• Moneyline: UNLV @ -111 via FanDuel
• Total: Over 52.5 @ -105 via bet365

According to our college football best bets, the top play to make on this game is UNLV +0.5 at the spread.

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 matchup between UNLV and California at Allegiant Stadium is scheduled to commence at 3:30 PM ET.

• Who: California vs. UNLV
• Date: Saturday, October 3, 2026
• Time: 3:30 PM ET/12:30PM PT
• Venue: Allegiant Stadium

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Conclusion

We predict UNLV, with a 51.7% win probability, will likely beat California 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.