A couple weekends ago, the Illinois football team lost to Duke at home, 31–27. My Hinsley model immediately became much less optimistic about Illinois making the College Football Playoff. But it became more optimistic about the offensive line and our new quarterback. That sounds contradictory, but it's exactly what I wanted to happen.

For the past 15 years, we've worked with people whose job is to make judgments about uncertain futures: intelligence and government analysts, foreign-policy researchers, investors, and corporate strategists. This year I decided to try an experiment. I took the methodology we've developed for that kind of work and applied it to assessing the fortunes of the University of Illinois football team.

During the Cuban Missile Crisis, American intelligence analysts had to consider competing explanations and revise their assessments as new evidence arrived. The analytical problem is surprisingly general. There are several possible futures, a huge amount of imperfect information, and a smaller number of things that actually help distinguish among them.

For more than a decade, Cultivate ran a prediction market for the U.S. Intelligence Community. That led us to develop Continuous Probabilistic Foresight, or CPF, the methodology at the heart of Hinsley, our AI/human hybrid analysis platform. CPF starts with a strategic question and maps the range of plausible outcomes as scenarios.