Duke Wins NCAA March Madness Tournament

Marcus McCoy

April 30, 2015

I wanted to take this opportunity to illustrate how cause and effect can help dissect an event outside of the normal industry problems root cause analysis is typically applied to.  I’m a casual college basketball fan, and like most casual fans we tend to pay more attention during the final March Madness NCAA tournament.  I enjoy filling out a bracket on who I think might when the game matchups, and of course I enjoy the bragging rights that come with a more successful bracket than colleagues and/or friends.  I took this year’s final four games between Duke, Wisconsin, Michigan State, and Kentucky and used a cause and effect chart to look at the games and tried to understand why teams won and/or lost.  The attached chart is not an all-inclusive analysis, but it does show how cause and effect can take the data (facts and causes) and show how the relationships impact the overall outcome (effects).  The analysis is supported with various statistics from reputable box score websites.  As with any analysis that is created by one person, I have a limited perspective on the causes and effects.  A more thorough analysis with a team of experts would yield a more accurate and complete analysis.  

Some observations:

  • When Wisconsin beat Kentucky in the Final Four matchup, Kentucky only attempted five 3-point shots and only had ten free-throw attempts.  I believe both of these figures are below the national average for Division I Men’s Basketball.
  • Also noted in Michigan State’s loss to Duke, MSU had twenty 3-point attempts and only made seven (35% make percentage).  
  • In the championship game, Duke had twice as many free-throw attempts as Wisconsin and made 80% of them.  Wisconsin fell victim to fewer free-throw attempts (like in Kentucky’s loss to Wisconsin) with only ten attempts.  Another statistic worth noting is Wisconsin’s 33% make-percentage for 3-point shots in the championship game.  Wisconsin’s junior forward Sam Decker went zero-for-six from 3-point range, which was not typical for the 2015 season.

It’s important to note that each of these games could be broken down into a more detailed game-specific analysis.  This would be particularly helpful for those teams that lost!

In conclusion, cause and effect can be a great tool for dissecting, assembling, and analyzing data from any event – not just the adverse events most of us apply RCA to in our normal work lives.