Contemporary Analysis (CAN) and Cabri Group and have teamed up again to use Machine Learning to predict the 2018 NCAA Men’s Basketball Tournament. This is different than last year as we are picking the entire 2018 bracket instead of just upsets.

Historically, only 26% of tournament's games end in an upset (this includes games from all rounds). That's 17 out of 64 games. Last year we did really good. Only failing to predict 3 upsets and getting 50% of our predictions right. We are going to need to improve a bunch to win that 1M/year for life from Berkshire Hathaway--including that wee bit about having to work for Berkshire Hathaway to be eligible. This year we added far more variables and used an ensemble model. Will we be perfect? Probably not. Here is the problem with using Machine Learning to try and predict a perfect bracket:

Last week, incumbent Jean Stothert won re-election in the 2017 Omaha Mayoral Election, defeating challenger Health Mello by a 53-to-47 percent margin. Let’s take a closer look at how the Republican fared in the polls over her Democratic challenger.

Here at CAN our free-time is spent researching the latest trends in and facts about data science. In a skim of Tableau Public, we found this fascinating visualization about women in tech. Tableau Public is a platform to post data visualizations made with Tableau. You don't have to be a data expert to share a visualization, you just have to be excited about data.

Machine Learning and the NCAA Men’s Basketball Tournament Methodology

 <<This article is meant to be the technical document following the above article. Please read the following article before continuing.>>

“The past may not be the best predictor of the future, but it is really the only tool we have”

The Tableau data visualization above, found at Tableau Public, shows the "Top 100 Songs of All Time Lyrics". Click here to hover over each square and see what words were used in which lyrics. Tableau is a software that converts data into graphs, charts, and images.

Investing in employees means more than just treating them well by giving them benefits and a flexible schedule. It means putting time and resources into individuals who have potential for greatness, but may need a little guidance.

Visualizing data can make something easier to understand and perhaps keep you awake.  Most students have learned that the cure for insomnia is take a difficult concept, like the philosophical concept of determinism, and explain it with words alone. In case you don’t know, Determinism is defined as “events within a given paradigm are bound by causality in such a way that any state of an object or event is determined by prior states.” Asleep yet? But what if you explain that concept with a picture?

Notice how the explanation become more interesting and relevant with a visual aid? I’m sure you had the same reaction as me. Even though I might not care what determinism means, the picture piqued my curiosity and drew me in to explore the topic than I would have otherwise. The illustration makes a hard concept easy to grasp. Download our eBook, "Dashboards: Take a closer look at your data".

Visualizing a concept has an amazing affect on the human mind.

The visual world is extraordinarily complex.  For example a quick scan of my desk reveals hand-written notes, dry erase markers and USB thumb-drive.  While I recognize these objects rapidly, I experience them at a basic visual perceptual level long before I can label or describe them.  This low level of perception is what is called preattentive processing, or visual processing that occurs without deliberate attention.  Preattentive processing can be used to create dashboards that easily communicate extraordinary amount of information per pixel and need very little effort to understand. Download our eBook, "Dashboards: Take a closer look at your data".

At CAN, as needed we use the visualization software Tableau to create reports and dashboards for our clients.   Also, because Tableau is capable of handling large amounts of data very quickly, we’ve started using it to explore data visually during the data discovery stage of each project.  We use Tableau to check the quality of data, find outliers, and get a sense of the properties of a data set, such as dispersion, central tendency, clustering, etc., before we apply statistical analysis or build predictive models.  A Tableau feature, especially useful for exploring data, are Reference Lines.

This blog post explains a few ways that CAN uses Tableau to explore a data set.

We invest a lot of time and energy communicating our research, because unless we can effectively communicate our findings they are useless.  When the goal is to communicate the most valuable information with the least amount of ink that can be understood with the least amount of effort.  For your reference, our major influences are Deirdre McCloskey on writing, Stephen Few on dashboard design, and Edward Tufte on data visualization.