As in the moments following the 2016 US election, win probabilities took center stage in public discourse after New England’s comeback victory in the Super Bowl over Atlanta.

Unfortunately, not everyone was enamored.

While it’s tempting to deride conclusions like Pete’s, it’s also too easy of a way out. And, to be honest,  I share a small piece of his frustration, because there’s a lingering secret behind win probability models:

Essentially, they’re all wrong.

But win probabilities models can still be useful.

To examine more deeply, I’ll compare 6 independently created win probability models using projections from Super Bowl 51. Lessons learned can help us better understand how these models operate.  Additionally, I’ll provide one example of how to check a win probability model’s accuracy, and share some guidelines for how we should better disseminate win probability information.

So, what…