Using historical March Madness data from 1985 through 2016, we analyzed how frequently teams win based on dimensions like geography, tournament round, and year. We used a high-speed Snowflake data warehouse and connected Tableau dashboard to answer detailed questions and gleam insights into this years performance.
To make this happen, we first ingested and modeled the data into Snowflake using SnowSQL and a S3 bucket (ordinarily stored as a csv). From there, we created a live connection from Tableau to specify the Snowflake storage and data warehouse used to compute the report. Finally, we published the interactive March Madness dashboard to Tableau Online for easy interaction from all interested parties!
Here are some of the questions we explored, click the visualization to see the answers.
Which team has won the most NCAA tournament games since 1985?
Answer: Duke with 85 Wins
Which team has won the 2nd most National Championships?
Answer: Duke with 5 Championships
When was the last time Georgetown won more than 3 games in a tournament?
Answer: 2007 when they won 30 games
What team has won the most games when playing in the West region?
Answer: Arizona with 32 wins
Who won the tournament in 2000?
Answer: Michigan State
In 2007-2016, what team won the most Elite Eight games?
Answer: Kentucky with 4 wins
In 2000-2009, in the East, what team won the most Sweet 16 games?
Answer: North Carolina with 3 games
Although this project was just a bit of March Madness fun, it illustrates how your business can visualize and interact with key performance indicators. Whether your analyzing financials, marketing, or operations, by having a conversation with your data via your BI tool, you can answer your business questions and dramatically improve your team’s performance.
If your interested in learning more about how advanced data and analytics tools like Snowflake and Tableau can help your organization find new insights, contact us for a modern data architecture strategy session.
Greg Marsh is a Data Engineer Manager at Aptitive. In his role, Greg facilitates the discovery of business insights from data. From “Big” data like IoT streams or classic relational ERP information, Greg helps companies to unlock the power of their data.