Richmond vs. Lafayette prediction favors Richmond to win in college football Week 5 [2026]

Using powerful machine learning and statistical analysis, we have re-simulated the result of Richmond vs. Lafayette, forming part of our college football predictions coverage.

Our independent predictive model currently gives Richmond a 70.4% chance of defeating Lafayette. Our predicted final score has Richmond winning 25-20. According to our model, Lafayette (+6.5) has a 52.7% chance of covering the spread, while the 42.5-point over/under has a 58.6% 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: Richmond -6.5 (-112) / Lafayette +6.5 (-105)
• Moneyline: Richmond -291, Lafayette +225
• Total: Over/Under 42.5 (-120/-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 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: Lafayette +6.5 @ -105 via bet365
• Moneyline: Lafayette @ +225 via bet365
• Total: Over 42.5 @ -120 via bet365

According to our college football best bets, the top play to make on this game is Over 42.5 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 Lafayette and Richmond at Fisher Stadium is scheduled to start at 12:30 PM ET.

• Who: Richmond vs. Lafayette
• Date: Saturday, October 3, 2026
• Time: 12:30 PM ET/9:30AM PT
• Venue: Fisher Stadium

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Conclusion

We predict Richmond, with a 70.4% win probability, will likely beat Lafayette 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.