The 10 most snakebit CFB teams ... and a betting edge

Nick Tre. Smith/Icon Sportswire

With 58 seconds left in regulation at Heinz Field, Miami backup quarterback Jarren Williams found KJ Osborn along the left hashmark. He slipped a tackle and burst upfield, scoring from 32 yards out to give the Hurricanes a 16-12 "upset" win over Pat Narduzzi's Pittsburgh Panthers.

Why the quotation marks? Because if you were looking at the right numbers, it might not have been an upset at all. Those same numbers suggest SMU could be in some trouble this coming weekend, as well.

As with many tools in sports analytics, the idea of my second-order-wins concept originates with Bill James. In his early, self-published "Baseball Abstracts," James espoused the ideas that would become a standard of the analytics toolbox: Pythagorean wins.

The idea is pretty simple, really: By looking at runs scored and runs allowed, you can devise a pretty good look at what a team's win percentage should be. Compare it to what it actually is, and you end up with a list of teams that might be a bit lucky or unlucky over a given period of time. The idea can be used for any sport once you figure out the right coefficient to use.

Baseball Prospectus took this concept a bit further, developing what they called Second Order wins, a Pythagorean figure based on how many runs you should have scored and allowed (according to typical production factors such as hits, hits allowed, etc.). Like football itself, analytics are a copycat sport, so I long ago began exploring my own second-order wins concept.

You can obviously win with a low postgame win expectancy or lose with a high one, but over time, it tends to regress toward the mean. (Some coaches prove exceptions to this rule, but it takes a large sample to figure that out.)

If you add up a team's postgame win expectancies, you get what amounts to an average expected win total, or my version of second-order wins. Over the course of a full season, this paints a pretty clear picture of good or bad fortune. And fortune tends to right itself.

Last year, Army and Northwestern had second-order win totals of just 8.1 and 6.2, respectively, but went 11-2 and 9-5; this year, they've gone a combined 4-11. Kentucky, Georgia Southern, Texas, Notre Dame and Syracuse also overachieved their second-order win total by quite a bit last season; they are not overachieving this year.

So what can second-order wins tell us about teams we might be under- or overestimating heading into 2019's stretch run?