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

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

Our famous predictive model currently gives Butler a 68.8% chance of beating Marist. Our predicted final score has Butler winning 26-20. According to our model, Marist (+6.5) has a 52.2% chance of covering the spread, while the over/under total of 44.5 points has a 56.0% 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: Butler -6.5 (-113) / Marist +6.5 (+111)
• Moneyline: Butler -228, Marist +223
• Total: Over/Under 44.5 (-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: Marist +6.5 @ 48¢ via Novig
• Moneyline: Marist @ 31¢ via Novig
• Total: Over 44.5 @ -110 via BetRivers

According to our college football best bets, the top play to make on this game is Marist +6.5 at the spread.

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 Marist and Butler at Tenney Stadium at Leonidoff Field is scheduled to start at 12:00 PM ET.

• Who: Butler vs. Marist
• Date: Saturday, November 14, 2026
• Time: 12:00 PM ET/9:00 AM PT
• Venue: Tenney Stadium at Leonidoff Field

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Conclusion

We predict Butler, with a 68.8% win probability, will likely beat Marist 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.