College Bowl ATS Picks

Our predictive models remove subjective bias from the prediction process. They use extensive historical data on game results, team stats, and betting odds to make college football ATS picks.

This approach has many benefits. For example, the opinions of college football writers, coaches, and other well publicized "experts" often influence the perception of teams in ways that do no accurately reflect a 100% objective analysis of their performance. Our data-driven algorithms, on the other hand, don't care what the college football writers think; they only incorporate facts.

However, math model ATS picks also have some limitations. Certain game factors (such as recent injuries, coaching changes, or potential inclement weather) are difficult if not impossible to model effectively with hard data, and are not considered by our algorithms. Highly uncommon point spreads also can pose difficulties for our Similar Games models, which incorporate historical betting line information. Lastly, it typically takes several weeks after each college football season begins for our math models to make a decent assessment of the relative performance levels of all teams.

In the end, math model college football ATS picks provide a smart, systematic foundation for an overall picking strategy, but a little subjectivity is still required to optimize prediction effectiveness. More than anything, look out for recent injury situations or personnel/coaching changes that can artificially skew model predictions.