Cornell vs. Colgate prediction: Why Colgate is favored to win in college football Week 3 [2026]

Using powerful machine learning and data, we have re-simulated the result of Cornell vs. Colgate, forming part of our college football predictions coverage.

Our famous predictive model gives Colgate an 83.7% chance of defeating Cornell. Our predicted final score has Colgate winning 32-19. According to our model, Colgate (-12.5) has a 51.0% chance of covering the spread, while the 50.5-point over/under has a 52.4% 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: Cornell +12.5 (-107) / Colgate -12.5 (-110)
• Moneyline: Cornell +384, Colgate -500
• Total: Over/Under 50.5 (-110/-110)

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: Colgate -12.5 @ -110 via bet365
• Moneyline: Colgate @ -500 via bet365
• Total: Over 50.5 @ -110 via bet365

According to our best college football bets, the top play to make on this game is Colgate 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 matchup between Colgate and Cornell at Crown Field at Andy Kerr Stadium is scheduled to commence at 1:00 PM ET.

• Who: Cornell vs. Colgate
• Date: Saturday, September 19, 2026
• Time: 1:00 PM ET/10:00AM PT
• Venue: Crown Field at Andy Kerr Stadium

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Conclusion

We predict Colgate, with an 83.7% 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.