Campbell vs. Hampton prediction favors Campbell to win in college football Week 4 [2026]

Using innovative machine learning and data, we have re-simulated the result of Campbell vs. Hampton, forming part of our college football predictions coverage.

Our famous predictive model currently gives Campbell a 90.7% chance of defeating Hampton. Our predicted final score has Campbell winning 37-17. According to our model, Hampton (+21.5) has a 55.6% chance of covering the spread, while the over/under total of 54.5 points has an equal 50-50 chance of hitting.

Latest betting odds

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

• Spread: Campbell -21.5 (-115) / Hampton +21.5 (-115)
• Moneyline: Campbell -1250, Hampton +740
• Total: Over/Under 54.5 (-106/-109)

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 +21.5 @ -115 via bet365
• Moneyline: Campbell @ -1250 via Caesars
• Total: Over 54.5 @ -106 via Caesars

According to our college football best bets, the top play to make on this game is Hampton +21.5 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 matchup between Hampton and Campbell at Armstrong Stadium is scheduled to start at 6:00 PM ET.

• Who: Campbell vs. Hampton
• Date: Saturday, September 26, 2026
• Time: 6:00 PM ET/3:00PM PT
• Venue: Armstrong Stadium

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Conclusion

We predict Campbell, with a 90.7% 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.