The Fall Of Giants

Our new iteration of the Giant Killers upset prediction model carries warning signs for five elite teams.

- Peter Keating

Decisions, decisions. For 11 years, as we have prospected for March Madness Cinderellas, our Giant Killers project has followed one principle: Isolate the statistical elements common to big favorites and deep underdogs from the past, so as to screen new contestants for those traits to predict the future. Works pretty well, but now—thanks to math professors Liz Bouzarth, John Harris and Kevin Hutson, our Giant Killer colleagues at Furman University—we’ve added a wrinkle, one that reflects more intuitively how humans make tough choices. It’s called decision tree analysis, and it allows us to look at all the Giant vs. Killer contests in our database (which dates to 2007), then ask a series of questions to split the games into purer and purer groups of matchups with similar outcomes—upsets and non-upsets. “In creating the decision tree, we rely on the power of statistics to help identify the things we wish we could identify by eye—natural groupings of complicated data,” Bouzarth says.

For example, in 2014, 12-seed Stephen F. Austin was a strong underdog, playing at a slow tempo with a good shooting touch, facing a Giant in VCU that didn’t dominate the offensive boards. Which meant SFA-VCU “branched” to a final “leaf” shared by a bunch of notable killings, such as Davidson over Georgetown in 2008. Lo and behold, SFA sprang an upset. On the other hand, the next season, Buffalo was a fast-paced 12-seed playing a favorite that crushed the offensive boards. In our decision tree, that path ends with Giants going 26–0! (Buffalo lost.)

This year, in advance of Selection Sunday, we applied decision tree analysis to supplement our metrics and see where the top Giants’ branches could lead. We’ll go out on a limb and claim that the following five cases are particularly interesting.

1. VILLANOVA