03-14-2014 11:40 AM
As you may have heard, billionaire philanthropist Warren Buffett and Cleveland Cavaliers owner Dan Gilbert have teamed up to offer $1 billion to anyone who can create a perfect NCAA March Madness bracket. “Wow,” you might say. “How hard can it be to create a perfect bracket? I could really use a billion bucks!” Well, the answer is “really, really, unbelievably hard.” So hard that in the history of March Madness, no one has ever done it. For you math lovers out there, the odds are supposedly 1 in 9.2 quintillion. And what if someone is actually able to create this magical winning bracket?
"I will invite him or her to be my guest at the final game and be there with a check in my pocket, but I will not be cheering for him or her to win," Buffett said, jokingly. "I may even give them a little investment advice.”
I wanted to share how I used SAS Enterprise Miner with the Data Mining community as it related to the mania around March Madness. I heavily used data mining techniques with SAS Enterprise Miner and SAS Rapid Predictive Modeler to get customers to be comfortable with data mining techniques via March Madness. These are the steps I took to pull in the data to be analyzed.
Through my research, I’ve also compiled a list of some helpful (and some not-so-helpful) factors for selection. Here’s what’s been successful in the past:
And here’s what doesn’t work:
The lesson for athletic directors? Schedule less cupcakes and more top 50 RPI teams
In addition to SAS products, here are the other sources I tapped:
Let's submit a community bracket to prove that SAS has the best modelers around! Entries must be complete prior to the start of the NCAA tournament.
03-14-2014 11:54 AM
Hi Reeza, That's exactly why we wanted to open up this discussion for ALL members! Traditionally, entries must be completed prior to the NCAA tournament. The community brackets/predictions can be submitted through the tournament. I believe Kathy originally used some historical data of each team and other factors listed above. This discussion is just for fun, and a great way to use your Data Mining skills.