Wisconsin vs. UCLA prediction favors UCLA to win in college football Week 7 [2026]

Using innovative machine learning and data analysis, we have re-simulated the result of Wisconsin vs. UCLA, forming part of our college football predictions coverage.

Our leading predictive model currently gives UCLA a 52.7% chance of defeating Wisconsin. Our predicted final score has UCLA winning 23-21. According to our model, Wisconsin (+3.5) has a 59.0% chance of covering the spread, while the over/under total of 43.5 points has a 54.2% 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: Wisconsin +3.5 (-160) / UCLA -3.5 (+150)
• Moneyline: Wisconsin +111, UCLA -113
• Total: Over/Under 43.5 (-101/-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 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: UCLA -3.5 @ 40¢ via Novig
• Moneyline: Wisconsin @ 48¢ via Novig
• Total: Over 43.5 @ -101 via bet365

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 UCLA and Wisconsin at Rose Bowl is scheduled to commence at 7:30 PM ET.

• Who: Wisconsin vs. UCLA
• Date: Saturday, October 17, 2026
• Time: 7:30 PM ET/4:30PM PT
• Venue: Rose Bowl

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Conclusion

We predict UCLA, with a 52.7% win probability, will likely beat Wisconsin 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.