Coastal Carolina vs. Marshall prediction: Why Marshall is favored to win in college football Week 6 [2026]

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

Our famous predictive model gives Marshall a 65.0% chance of beating Coastal Carolina. Our predicted final score has Marshall winning 31-26. According to our model, Marshall (-4.5) has a 51.4% chance of covering the spread, while the over/under total of 58.5 points has a 50.7% chance of staying under.

Latest betting odds

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

• Spread: Coastal Carolina +4.5 (-112) / Marshall -4.5 (-105)
• Moneyline: Coastal Carolina +161, Marshall -185
• Total: Over/Under 58.5 (-107/-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: Marshall -4.5 @ -105 via FanDuel
• Moneyline: Marshall @ -185 via DraftKings
• Total: Under 58.5 @ -112 via FanDuel

According to our best college football bets, the top play to make on this game is Marshall -4.5 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 game between Marshall and Coastal Carolina at Joan C. Edwards Stadium is scheduled to start at 7:00 PM ET.

• Who: Coastal Carolina vs. Marshall
• Date: Saturday, October 10, 2026
• Time: 7:00 PM ET/4:00PM PT
• Venue: Joan C. Edwards Stadium

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Conclusion

We predict Marshall, with a 65.0% win probability, will likely beat Coastal Carolina in Saturday's college football matchup.

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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.