Central Conn vs. Montana State prediction: Why Montana State is favored to win in college football Week 3 [2026]

Using powerful machine learning and data analysis, we have re-simulated the outcome of Central Conn vs. Montana State, forming part of our college football predictions coverage.

Our independent predictive model gives Montana State a 99.1% chance of beating Central Connecticut State. Our predicted final score has Montana State winning 49-9. According to our model, Montana State (-38.5) has a 53.6% chance of covering the spread, while the 59.5-point over/under has a 52.4% chance of staying under.

Latest betting odds

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

• Spread: Central Conn +38.5 (-110) / Montana State -38.5 (-110)
• Moneyline: Central Conn , Montana State

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:

• Moneyline: Under @ -105 via Bet365

According to our college football best bets, the top play to make on this game is Montana State -38.5.

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 Montana State and Central Conn at Bobcat Stadium is scheduled to start at 3:00 PM ET.

• Who: Central Connecticut State vs. Montana State
• Date: Saturday, September 19, 2026
• Time: 3:00 PM ET/12:00PM PT
• Venue: Bobcat Stadium

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Conclusion

We predict Montana State, with a 99.1% win probability, will likely beat Central Connecticut State 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.