Turn to Analytics for a Better NCAA Tournament Bracket

There is nothing more important to do this week than to put together a solid bracket for the NCAA men’s basketball tournament. OK, that may be a bit of an overstatement, but if you consider the amount of time and energy spent at work this week on picking teams and watching games (and reading this blog), you’ll have to agree there’s more than a little truth to what I say.

But it’s not just you and me trying to figure out whether Bucknell is the new Butler in the 6 v. 11 game in the East regional. Two college professors and a third gentleman using SAS analytics correctly picked the 37 teams that received at-large bids.  (The other 31 teams earn bid through winning conference tournaments.)

They seem to have this down pretty good as their two-year total is 73 out of 74 in predicting the at-large bids. When you consider that one of this year’s at-large selections is Middle Tennessee State, well, kudos to those three analysts and to SAS.

But to the best of my knowledge, predicting who is going to be in the field isn’t going to make us any money. Predicting who actually wins the games is what earns you accolades and some cash.

First off, remember that no one is perfect. I’ve read estimates that the odds of correctly picking all 64 winning teams in your bracket is as high as one in 128 billion or as low as one in 35 billion. So let’s just forget about perfection, shall we. (By way of comparison, the odds of winning the Powerball lottery are 1 in 175 million and that usually pays a little higher than your office pool.)

So where to turn for help? Another of our favorite software vendors, SAP and its so-called “geek squad” are here to help. A visit to the SAP website gives you access to an NCAA basketball data set and a 30-day trial of SAP Visual Intelligence.

Users are then asked to share their analysis and do a little trash talking—what’s March Madness without some trash talking, after all—via Twitter using #SAPVisi.

I’m sure there are plenty of basketball fans shaking their heads at the thought of using technology tools to correctly pick the winning teams in a basketball tournament, but may I remind you there were plenty of insurance executives rolling their eyes not too long ago when the idea of using data to predict outcomes in claims or underwriting was first presented to them.

Predictive analytics doesn’t have the “eye test” or the “gut check” or even the biggest hindrance to winning your NCAA pool: fandom. But since my Xavier Musketeers didn’t make the tournament this year so my selections won’t be colored by anything as silly as school spirit. Like many a losing gambler, I’ll latch on to anything at this point.

About the Author
Robert Regis Hyle, PropertyCasualty360.com

Robert Regis Hyle, PropertyCasualty360.com

Robert Regis Hyle is editor-in-chief of Tech Decisions magazine and technology channel editor for PropertyCasualty360.com. He has spent over three decades as a journalist for a variety of business and regional news publications including a stint with a weekly newspaper that he owned and operated. He has been with Tech Decisions since the magazine’s inception in 1999 and has written articles on virtually every issue and trend facing insurance IT professionals. Prior to joining Tech Decisions, he spent two years as editor of a sister publication, The Ohio Underwriter, where he covered insurance topics for the agency and carrier markets. He has spoken on insurance technology issues at various industry conferences such as IASA and ACORD LOMA and on a number of web seminars. He is a graduate of Xavier University in Cincinnati with a degree in Communication Arts. Hyle may be reached at rhyle@sbmedia.com


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