Hampton vs. Towson prediction: Why Towson is favored to win in college football Week 9 [2026]

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

Our independent predictive model gives Towson a 90.2% chance of beating Hampton. Our predicted final score has Towson winning 36-18. According to our model, Hampton (+18.5) has a 51.2% chance of covering the spread, while the over/under total of 56.5 points has a 53.5% 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: Hampton +18.5 (-110) / Towson -18.5 (-112)
• Moneyline: Hampton +641, Towson -669
• Total: Over/Under 56.5 (+117/-147)

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: Hampton +18.5 @ -110 via DraftKings
• Moneyline: Towson @ 87¢ via Novig
• Total: Over 56.5 @ 46¢ via Novig

According to our college football best bets, the top play to make on this game is Towson to win.

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 Towson and Hampton at Minnegan Field at Johnny Unitas Stadium is scheduled to begin at 1:00 PM ET.

• Who: Hampton vs. Towson
• Date: Saturday, October 31, 2026
• Time: 1:00 PM ET/10:00 AM PT
• Venue: Minnegan Field at Johnny Unitas Stadium

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Conclusion

We predict Towson, with a 90.2% win probability, will likely beat Hampton 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.