Contemporary Analysis

Data Science

Nate Watson

Recent Posts

Nate Watson

2018 NCAA bracket picks using Machine Learning.

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:

Nate Watson

CAN turns 10!

Today is Contemporary Analysis (CAN)’s 10th birthday!!! Although we are not the company we started back in 2008—different logo, different owners, new leaders, new data scientists, even a new way to serve up data science,—we still have the best team in the region and now 10 years of wisdom of how to implement, build, and train data scientists as well. Let us help -- even if it is just a phone call for advice. Our goal is to help every company in the region use data science to be competitive in their niche.

Nate Watson

Contemporary Analysis Awarded Small Business of the Month

Recently, Contemporary Analysis (CAN) was presented with the Greater Omaha Chamber’s Small Business of the Month award. It means a lot to be recognized for the hard work the team has done over the last year improving how companies start and scale data science internally.

Nate Watson

Game of Throne Meets Data Science

Sometimes obsession breads genius. Fans of Game of Thrones have dedicated much time to tracking the deaths, births, twists, and turns of the previous seasons. Now that season 7 has arrived, there are some amazing maps of the story out there. We found one we particularly liked on Tableau Public.

Nate Watson

Building From Within

Our staff augmentation model proves that CAN believes in building a data analytics team from within. Our goal is to get a data scientists in every company in Omaha. We want to add value to every business team by training a data scientist with the latest tools of the industry and on the ground field experience.

Nate Watson

Happy Fourth of July from CAN

Cheers to happy and safe Fourth of July! For our celebration, we're sharing this Tableau visualization about the growth of our United States.

Nate Watson

Machine Learning Upset Prediction Project Proves its Value

At the beginning of the project, we set out to show how the 2017 NCAA College Basketball Tournament could be a proving ground for Machine Learning analysis. There are very few places in the world where we can use the same model to predict multiple outcomes in a short period of time, have a ready-made scorecard (Vegas), have the general public understand what we are trying to do, and have a chance to "beat" the algorithm with their own knowledge.

Nate Watson

2017 NCAA Tournament Elite Eight Upset Picks

We are doing better than anticipated even after the heartbreaker of a game last night between Wisconsin and Florida and are still ahead $165.

Nate Watson

March Machine Learning Mayhem

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”

 

Nate Watson

Predicting the upsets for the NCAA Men’s Basketball Tournament using machine learning

Contemporary Analysis (CAN) and Cabri Group and have teamed up to use Machine Learning to predict the upsets for the NCAA Men’s Basketball Tournament. By demonstrating the power of ML through our results, we believe more people can give direction to their ML projects.

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