San Diego vs. Butler prediction favors San Diego to win in college football Week 6 [2026]

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

Our leading predictive model gives San Diego a 59.2% chance of beating Butler. Our predicted final score has San Diego winning 29-27. According to our model, Butler (+2.5) has a 51.4% chance of covering the spread, while the over/under total of 57.5 points has a 50.6% 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: San Diego -2.5 (-115) / Butler +2.5 (-108)
• Moneyline: San Diego -160, Butler +145
• Total: Over/Under 57.5 (-118/-105)

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: Butler +2.5 @ -108 via DraftKings
• Moneyline: Butler @ +145 via bet365
• Total: Under 57.5 @ -105 via DraftKings

According to our best college football bets, the top play to make on this game is Butler 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 action between Butler and San Diego at Bud and Jackie Sellick Bowl is scheduled to start at 1:00 PM ET.

• Who: San Diego vs. Butler
• Date: Saturday, October 10, 2026
• Time: 1:00 PM ET/10:00AM PT
• Venue: Bud and Jackie Sellick Bowl

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Conclusion

We predict San Diego, with a 59.2% win probability, will likely beat Butler 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.