Wagner vs. Duquesne prediction favors Duquesne to win in college football Week 8 [2026]

Using trusted machine learning and data, we have re-simulated the result of Wagner vs. Duquesne, forming part of our college football predictions coverage.

Our leading predictive model gives Duquesne an 82.3% chance of defeating Wagner. Our predicted final score has Duquesne winning 29-17. According to our model, Wagner (+13.5) has a 51.3% chance of covering the spread, while the over/under total of 46.5 points has a 52.2% 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: Wagner +13.5 (-115) / Duquesne -13.5 (-108)
• Moneyline: Wagner +441, Duquesne -456
• Total: Over/Under 46.5 (-102/-102)

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: Wagner +13.5 @ -115 via bet365
• Moneyline: Duquesne @ 82¢ via Novig
• Total: Over 46.5 @ 50¢ via Novig

According to our college football best bets, the top play to make on this game is Over 46.5 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 matchup between Duquesne and Wagner at Arthur J. Rooney Athletic Field is scheduled to begin at 12:00 PM ET.

• Who: Wagner vs. Duquesne
• Date: Saturday, October 24, 2026
• Time: 12:00 PM ET/9:00 AM PT
• Venue: Arthur J. Rooney Athletic Field

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Conclusion

We predict Duquesne, with an 82.3% win probability, will likely beat Wagner 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.