College Football (FBS)
NCAA Football predictions & analytics
College football has huge gaps in talent between programs and few games between conferences, which makes rating teams harder. EdgeIQ's college football projections use team ratings, schedule strength and recent form, and flag games where data is thin.
Upcoming NCAA Football games
What the NCAA Football model looks at
Team ratings
Built from results and margins, with extra care for connecting conferences through cross-conference games.
Strength of schedule
A win over a weak opponent counts for less than a win over a strong one.
Home field
Measured from history; it tends to be larger than in the NFL.
Recent form
Rolling margins from games played before kickoff only.
Known limitations
- Our own testing shows the model is overconfident on heavy favorites, so treat very high probabilities with caution.
- Roster turnover each year makes early-season projections less reliable.
Measured results for every model version are published in the Model Lab. Projections are estimates, never guarantees.
Related reading
- How Sports Prediction Models WorkA plain-English tour of a sports prediction model: the data it learns from, the features it builds, how it turns them into a probability, and how it is tested before anyone relies on it.
- What Does a 70% Win Probability Actually Mean?A 70% win probability is not a prediction that a team will win. It is a statement about frequency. Here is how to read it, how to check it, and the mistakes people make with it.
- Understanding Brier Score in Sports PredictionsThe Brier score measures how good probability forecasts are, not just whether picks were right. Worked examples, benchmarks and the common traps when comparing models.
- Why Sports Prediction Models Get Games WrongSome misses are the model working as intended; others reveal real weaknesses. How to tell the difference, with examples from EdgeIQ's own evaluation.