Monthly Archives: February 2023

The Eye Test Vs. Data Analytics

I went to a game last night just for fun.  The teams are irrelevant for the purpose of this conversation; suffice to say that both were middling, neither has a shot at the NCAA tournament and none of it has implications of national merit.  But man, it’s almost March and it’s college basketball, it was a blast!  15,590 fans just having a great time sending off the seniors and hoping for a shift in fortunes during the Conference Tournament.

The beauty of CBB is that anyone can be a Cinderella.  As I watched the event, the home team played the best game of their season.  They were balanced on offense, they shutdown the other team’s two main threats.  They rebounded well, controlled their turnovers and staved off multiple run attempts of their foe.  At one point they were up by 21 and I thought to myself, “Man, if they play like this, who knows, three good games in the conference tourney and they could slip into an automatic bid.” I was impressed and drinking the Kool-aid.

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Mad Max in the Final Four

It is often easier to eliminate Final Four candidates than to identify them. 

For instance, if you look back through the history of all Final Four participants for the last 35 years or so, there doesn’t seem to be a common thread in the data that can be pointed to as a sure fire indicator that a particular team is going to win it all come March. (At least not yet, finding that is our goal…)

What we can identify are teams that lack characteristics germane to all Final Four teams. One of the best indicators is how well a team does on the road. 

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Who’s your Bracket’s Daddy? Run a DNA Test!

Welcome to week 15 of the college basketball season. 15 is really not terribly significant, but two weeks ago was. So let’s go in the way back machine, speed it up to 86 mph and look at the AP standings from week 13. Then let’s figure out why this ranking is important.

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