Iowa State vs. Baylor prediction favors Baylor to win in college football Week 10 [2026]

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

Our independent predictive model gives Baylor a 76.8% chance of defeating Iowa State. Our predicted final score has Baylor winning 30-21. According to our model, Iowa State (+9.5) has a 50.5% chance of covering the spread, while the 52.5-point over/under has a 52.9% 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: Iowa State +9.5 (+102) / Baylor -9.5 (-104)
• Moneyline: Iowa State +308, Baylor -317
• Total: Over/Under 52.5 (+117/-127)

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: Iowa State +9.5 @ 50¢ via Novig
• Moneyline: Baylor @ 76¢ via Novig
• Total: Over 52.5 @ 46¢ via Novig

According to our best college football bets, the top play to make on this game is Over 52.5 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 matchup between Baylor and Iowa State at McLane Stadium is scheduled to begin at 12:00 PM ET.

• Who: Iowa State vs. Baylor
• Date: Saturday, November 7, 2026
• Time: 12:00 PM ET/9:00 AM PT
• Venue: McLane Stadium

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Conclusion

We predict Baylor, with a 76.8% win probability, will likely beat Iowa 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.