Tulane vs. Charlotte prediction: Why Tulane is favored to win in college football Week 9 [2026]

Using innovative machine learning and data, we have re-simulated the outcome of Tulane vs. Charlotte, forming part of our college football predictions coverage.

Our famous predictive model currently gives Tulane a 73.2% chance of defeating Charlotte. Our predicted final score has Tulane winning 28-20. According to our model, Charlotte (+8.5) has a 52.9% chance of covering the spread, while the 47.5-point over/under has a 53.8% 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: Tulane -8.5 (-109) / Charlotte +8.5 (-107)
• Moneyline: Tulane -308, Charlotte +300
• Total: Over/Under 47.5 (-105/-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: Charlotte +8.5 @ -107 via Caesars
• Moneyline: Charlotte @ 25¢ via Novig
• Total: Over 47.5 @ -105 via DraftKings

According to our best college football bets, the top play to make on this game is Over 47.5 total points.

These picks are based on our probabilities matched against the implied probabilities from the current available odds.

Game info & kickoff details

Friday's college football matchup between Charlotte and Tulane at Jerry Richardson Stadium is scheduled to commence at 7:00 PM ET.

• Who: Tulane vs. Charlotte
• Date: Friday, October 30, 2026
• Time: 7:00 PM ET/4:00 PM PT
• Venue: Jerry Richardson Stadium

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Conclusion

We predict Tulane, with a 73.2% win probability, will likely beat Charlotte in Friday'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.