UMass vs. Buffalo prediction favors Buffalo to win Week 7 MACtion clash

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

Our famous predictive model currently gives Buffalo a 72.8% chance of beating Massachusetts. Our predicted final score has Buffalo winning 25-18. According to our model, Massachusetts (+7.5) has a 52.7% chance of covering the spread, while the over/under total of 42.5 points has a 55.1% 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: UMass +7.5 (-113) / Buffalo -7.5 (-105)
• Moneyline: UMass +240, Buffalo -280
• Total: Over/Under 42.5 (-106/-112)

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: Massachusetts +7.5 @ -113 via BetRivers
• Moneyline: Buffalo @ -280 via Caesars
• Total: Over 42.5 @ -106 via Caesars

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 Buffalo and UMass at Broadview Stadium is scheduled to commence at 1:00 PM ET.

• Who: Massachusetts vs. Buffalo
• Date: Saturday, October 17, 2026
• Time: 1:00 PM ET/10:00AM PT
• Venue: Broadview Stadium

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Conclusion

We predict Buffalo, with a 72.8% win probability, will likely beat Massachusetts 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.