Stanford vs. Duke prediction favors Duke to win in college football Week 3 [2026]

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

Our famous predictive model currently gives Duke a 77.2% chance of defeating Stanford. Our predicted final score has Duke winning 30-20. According to our model, Stanford (+10.5) has a 51.9% chance of covering the spread, while the 50.5-point over/under has a 53.6% 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: Stanford +10.5 (-115) / Duke -10.5 (-104)
• Moneyline: Stanford +310, Duke -375
• Total: Over/Under 50.5 (-112/-105)

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 available 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: Stanford +10.5 @ -115 via bet365
• Moneyline: Stanford @ +310 via bet365
• Total: Over 50.5 @ -112 via BetRivers

According to our college football best bets, the top play to make on this game is Over 50.5 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 game between Duke and Stanford at Brooks Field at Wallace Wade Stadium is scheduled to start at 4:00 PM ET.

• Who: Stanford vs. Duke
• Date: Saturday, September 19, 2026
• Time: 4:00 PM ET/1:00PM PT
• Venue: Brooks Field at Wallace Wade Stadium

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Conclusion

We predict Duke, with a 77.2% win probability, will likely beat Stanford 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.