Colgate vs. Richmond prediction: Why Richmond is favored to win in college football Week 8 [2026]

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

Our famous predictive model gives Richmond a 54.8% chance of defeating Colgate. Our predicted final score has Richmond winning 22-20. According to our model, Colgate (+3.5) has a 57.7% chance of covering the spread, while the 41.5-point over/under has a 55.8% chance of going over.

Latest betting odds

We have researched the best college football odds in America for this game, which are listed here:

• Spread: Colgate +3.5 (-125) / Richmond -3.5 (+113)
• Moneyline: Colgate +113, Richmond -113
• Total: Over/Under 41.5 (-110/-111)

All odds are correct at the time of publication and are subject to change.

Model picks and best bet

After matching our probabilities 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 +3.5 @ 56¢ via Novig
• Moneyline: Richmond @ 53¢ via Novig
• Total: Over 41.5 @ -110 via bet365

According to our college football best bets, the top play to make on this game is Over 41.5 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 Richmond and Colgate at E. Claiborne Robins Stadium is scheduled to begin at 2:00 PM ET.

• Who: Colgate vs. Richmond
• Date: Saturday, October 24, 2026
• Time: 2:00 PM ET/11:00 AM PT
• Venue: E. Claiborne Robins Stadium

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Conclusion

We predict Richmond, with a 54.8% win probability, will likely beat Colgate 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.