Holy Cross vs. Harvard prediction favors Harvard to win in college football Week 7 [2026]

Using powerful machine learning and data, we have re-simulated the result of Holy Cross vs. Harvard, forming part of our college football predictions coverage.

Our leading predictive model currently gives Harvard an 88.9% chance of beating Holy Cross. Our predicted final score has Harvard winning 31-15. According to our model, Harvard (-15.5) has a 52.0% chance of covering the spread, while the over/under total of 45.5 points has a 54.3% 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: Holy Cross +15.5 (-110) / Harvard -15.5 (-110)
• Moneyline: Holy Cross +525, Harvard -750
• Total: Over/Under 45.5 (-104/-115)

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: Harvard -15.5 @ -110 via bet365
• Moneyline: Harvard @ -750 via DraftKings
• Total: Over 45.5 @ -104 via DraftKings

According to our college football best bets, the top play to make on this game is Over 45.5 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 Harvard and Holy Cross at Harvard Stadium is scheduled to start at 1:00 PM ET.

• Who: Holy Cross vs. Harvard
• Date: Saturday, October 17, 2026
• Time: 1:00 PM ET/10:00AM PT
• Venue: Harvard Stadium

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Conclusion

We predict Harvard, with an 88.9% win probability, will likely beat Holy Cross 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.