UCLA vs. Maryland prediction: Why UCLA is favored to win in college football Week 4 [2026]

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

Our independent predictive model currently gives UCLA a 52.8% chance of beating Maryland. Our predicted final score has UCLA winning 28-27. According to our model, the oddsmakers have got it right, and UCLA and Maryland each have a 50% chance of covering the spread, while the 56.5-point over/under has a 50.7% chance of staying under.

Latest betting odds

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

• Spread: UCLA -1.5 (-110) / Maryland +1.5 (-106)
• Moneyline: UCLA -120, Maryland +104
• Total: Over/Under 56.5 (-102/-110)

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: Maryland +1.5 @ -106 via FanDuel
• Moneyline: UCLA @ -120 via bet365
• Total: Over 56.5 @ -102 via Caesars

According to our college football best bets, the top play to make on this game is Over 56.5 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 action between Maryland and UCLA at SECU Stadium is scheduled to begin at 1:30 PM ET.

• Who: UCLA vs. Maryland
• Date: Saturday, September 26, 2026
• Time: 1:30 PM ET/10:30AM PT
• Venue: SECU Stadium

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Conclusion

We predict UCLA, with a 52.8% win probability, will likely beat Maryland 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.