Rice vs. Fresno State prediction favors Fresno State to win in college football Week 4 [2026]

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

Our famous predictive model gives Fresno State an 82.5% chance of beating Rice. Our predicted final score has Fresno State winning 28-16. According to our model, Rice (+13.5) has a 51.3% chance of covering the spread, while the 43.5-point over/under has a 54.1% 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: Rice +13.5 (-115) / Fresno State -13.5 (+100)
• Moneyline: Rice +430, Fresno State -530
• Total: Over/Under 43.5 (-110/-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 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: Fresno State -13.5 @ +100 via FanDuel
• Moneyline: Rice @ +430 via BetRivers
• Total: Over 43.5 @ -110 via FanDuel

According to our college football best bets, the top play to make on this game is Over 43.5 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 game between Fresno State and Rice at Valley Children's Stadium is scheduled to begin at 10:00 PM ET.

• Who: Rice vs. Fresno State
• Date: Saturday, September 26, 2026
• Time: 10:00 PM ET/7:00PM PT
• Venue: Valley Children's Stadium

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Conclusion

We predict Fresno State, with an 82.5% win probability, will likely beat Rice 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.