Montana vs. UC Davis prediction favors UC Davis to win in college football Week 4 [2026]

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

Our leading predictive model gives UC Davis a 59.5% chance of beating Montana. Our predicted final score has UC Davis winning 32-28. According to our model, UC Davis (-2.5) has a 55.2% chance of covering the spread, while the 62.5-point over/under has a 52.5% chance of staying under.

Latest betting odds

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

• Spread: Montana +2.5 (-105) / UC Davis -2.5 (-115)
• Moneyline: Montana +122, UC Davis -147
• Total: Over/Under 62.5 (-111/-112)

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: UC Davis -2.5 @ -115 via Caesars
• Moneyline: UC Davis @ -147 via DraftKings
• Total: Under 62.5 @ -112 via DraftKings

According to our college football best bets, the top play to make on this game is UC Davis -2.5 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 game between UC Davis and Montana at UC Davis Health Stadium is scheduled to commence at 10:00 PM ET.

• Who: Montana vs. UC Davis
• Date: Saturday, September 26, 2026
• Time: 10:00 PM ET/7:00PM PT
• Venue: UC Davis Health Stadium

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Conclusion

We predict UC Davis, with a 59.5% win probability, will likely beat Montana 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.