Towson vs. Sacred Heart prediction favors Towson to win in college football Week 10 [2026]

Using powerful machine learning and data, we have re-simulated the outcome of Towson vs. Sacred Heart, forming part of our college football predictions coverage.

Our leading predictive model currently gives Towson a 51.4% chance of beating Sacred Heart. Our predicted final score has Towson winning 26-25. According to our model, Towson (+1.5) has a 55.3% chance of covering the spread, while the over/under total of 50.5 points has a 52.0% 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: Towson +1.5 (-120) / Sacred Heart -1.5 (+106)
• Moneyline: Towson -106, Sacred Heart +102
• Total: Over/Under 50.5 (-108/-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: Towson +1.5 @ 55¢ via Novig
• Moneyline: Towson @ -106 via DraftKings
• Total: Over 50.5 @ -108 via BetRivers

According to our best college football bets, the top play to make on this game is Towson +1.5 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 Sacred Heart and Towson at Campus Field is scheduled to commence at 12:00 PM ET.

• Who: Towson vs. Sacred Heart
• Date: Saturday, November 7, 2026
• Time: 12:00 PM ET/9:00 AM PT
• Venue: Campus Field

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Conclusion

We predict Towson, with a 51.4% win probability, will likely beat Sacred Heart 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.