Toledo vs. Ball State prediction favors Toledo to win Week 5 MACtion clash

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

Our leading predictive model gives Toledo a 94.0% chance of beating Ball State. Our predicted final score has Toledo winning 36-16. According to our model, Ball State (+20.5) has a 51.6% chance of covering the spread, while the over/under total of 52.5 points has a 51.3% 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: Toledo -20.5 (-109) / Ball State +20.5 (-105)
• Moneyline: Toledo -1200, Ball State +1050
• Total: Over/Under 52.5 (-109/-108)

All odds are correct at the time of publication and are subject to change.

Model picks and best bet

After matching our probabilities 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: Ball State +20.5 @ -105 via FanDuel
• Moneyline: Toledo @ -1200 via DraftKings
• Total: Over 52.5 @ -109 via BetRivers

According to our college football best bets, the top play to make on this game is Ball State +20.5 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 action between Ball State and Toledo at Scheumann Stadium is scheduled to start at 2:00 PM ET.

• Who: Toledo vs. Ball State
• Date: Saturday, October 3, 2026
• Time: 2:00 PM ET/11:00AM PT
• Venue: Scheumann Stadium

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Conclusion

We predict Toledo, with a 94.0% win probability, will likely beat Ball 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.