Harvard vs. Brown prediction favors Harvard to win in college football Week 4 [2026]

Using innovative machine learning and data, we have re-simulated the outcome of Harvard vs. Brown, forming part of our college football predictions coverage.

Our leading predictive model gives Harvard an 80.3% chance of defeating Brown. Our predicted final score has Harvard winning 29-17. According to our model, Brown (+13.5) has a 55.1% chance of covering the spread, while the over/under total of 44.5 points has a 56.0% chance of going over.

Latest betting odds

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

• Spread: Harvard -13.5 (-110) / Brown +13.5 (-110)
• Moneyline: Harvard -553, Brown +400
• Total: Over/Under 44.5 (-115/-107)

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 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: Brown +13.5 @ -110 via Caesars
• Moneyline: Brown @ +400 via bet365
• Total: Over 44.5 @ -115 via Caesars

According to our college football best bets, the top play to make on this game is Brown +13.5 at the spread.

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

Game info & kickoff details

Friday's college football action between Brown and Harvard at Centreville Bank Stadium is scheduled to begin at 7:00 PM ET.

• Who: Harvard vs. Brown
• Date: Friday, September 25, 2026
• Time: 7:00 PM ET/4:00PM PT
• Venue: Centreville Bank Stadium

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Conclusion

We predict Harvard, with an 80.3% win probability, will likely beat Brown in Friday'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.