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

Using trusted machine learning and statistical analysis, we have re-simulated the outcome of Yale vs. Columbia, forming part of our college football predictions coverage.

Our leading predictive model gives Yale a 76.3% chance of defeating Columbia. Our predicted final score has Yale winning 29-21. According to our model, Yale (-6.5) has a 56.3% chance of covering the spread, while the 49.5-point over/under 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: Yale -6.5 (-178) / Columbia +6.5 (+138)
• Moneyline: Yale -292, Columbia +285
• Total: Over/Under 49.5 (-150/+122)

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: Columbia +6.5 @ 42¢ via Novig
• Moneyline: Yale @ 75¢ via Novig
• Total: Under 49.5 @ 45¢ via Novig

According to our college football best bets, the top play to make on this game is Yale 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 Columbia and Yale at Robert K. Kraft Field at Lawrence A. Wien Stadium is scheduled to commence at 12:00 PM ET.

• Who: Yale vs. Columbia
• Date: Saturday, October 31, 2026
• Time: 12:00 PM ET/9:00 AM PT
• Venue: Robert K. Kraft Field at Lawrence A. Wien Stadium

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Conclusion

We predict Yale, with a 76.3% win probability, will likely beat Columbia 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.