Dartmouth vs. Cornell prediction: Why Dartmouth is favored to win in college football Week 11 [2026]

Using innovative machine learning and data, we have re-simulated the outcome of Dartmouth vs. Cornell, forming part of our college football predictions coverage.

Our independent predictive model gives Dartmouth a 56.3% chance of defeating Cornell. Our predicted final score has Dartmouth winning 24-22. According to our model, Dartmouth (-1.5) has a 52.9% chance of covering the spread, while the over/under total of 45.5 points has a 53.9% 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: Dartmouth -1.5 (-108) / Cornell +1.5 (+100)
• Moneyline: Dartmouth -115, Cornell +111
• Total: Over/Under 45.5 (-104/-104)

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: Dartmouth -1.5 @ -108 via BetRivers
• Moneyline: Dartmouth @ 53¢ via Novig
• Total: Over 45.5 @ 51¢ via Novig

According to our college football best bets, the top play to make on this game is Over 45.5 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 action between Cornell and Dartmouth at Schoellkopf Field is scheduled to start at 1:00 PM ET.

• Who: Dartmouth vs. Cornell
• Date: Saturday, November 14, 2026
• Time: 1:00 PM ET/10:00 AM PT
• Venue: Schoellkopf Field

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Conclusion

We predict Dartmouth, with a 56.3% win probability, will likely beat Cornell 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.