As March Madness kicks off with Selection Sunday, basketball fanatics will use a variety of methods to choose their winning teams and fill in their brackets. With a bit of guessing and some luck, “bracketeers” can be a part of the month-long competition, earning bragging rights within their social and professional circles. It may surprise some bracketology fans to know that sophisticated mathematical modeling that is used to increase the odds of having a perfect – or even good – bracket is the same technique used to solve the most complex challenges in both industry and government. 

At the University of Illinois Urbana-Champaign, I teach students methods that they can apply to the study of analytics to college basketball brackets, the seeding process and team selection. In doing so, we break down the complexities of bracketology, such as what patterns help determine which seeds progress in the tournament. The material demonstrates the real-world relevance of operations research and data analytics in an engaging framework.

The study of bracketology is only one small sector within the larger discipline of operations research and data analytics. Practitioners in this field work at universities, federal agencies, data analytics centers for companies like Uber and Facebook and of course, professional sports teams.


The capabilities wrought by data analytics and operations research methods – which are designed to analyze massive quantities of information to find patterns and generate scenarios to their greatest efficiency – have enormous impacts in society, whether we can perceive them or not. Famously, UPS used operations research and data analytics to create the UPS On-Road Integrated Optimization and Navigation (ORION) system, which determines the order of packages for drivers. The project, when fully deployed, will save UPS $300-400 million annually.

Even more crucial, data analytics and operations research applications transcend to programs within the federal government. For example, the Center for Disease Control and Prevention (CDC) collaborated with Kid Risk Inc. to develop analytical models to evaluate the global risks, benefits and costs of polio eradication policy choices. With the use of operations research and management science tools, the CDC and Kid Risk collaboration combatted polio in multiple ways, including:

1. Encouraging a more rapid response to outbreaks

2. Reaffirming polio eradication was a better strategy than merely controlling outbreaks

3. Supporting global policies to discontinue the administration of oral poliovirus vaccine. Ultimately, these data-driven insights supported policies that intensified efforts to increase population immunity in India.

I have even had the opportunity to work with the federal government to determine innovative solutions to our country’s most pressing problems. From 2001 through 2009, my team at the University of Illinois at Urbana-Champaign developed the foundation to what has become TSA’s PreCheck system. Knowing that high-level screening at airports for all passengers is extremely inefficient, both in terms of cost and time, we analyzed what factors contributed to the threat level of a passenger to measure the impact of screening some passengers through an expedited process versus others who are deemed a higher risk and need to be screened more thoroughly.


Tools used in March Madness bracketology are the same tools used to tackle formidable national and international issues. From bike-sharing optimization to the fight to eradicate polio in India, operations research and data analytics target multiple variables in complex problems and synthesizes them into a solution, maximizing efficiency and cost savings. All you need is the data.

As we begin March Madness and millions of people fill out their brackets, we should appreciate the fact that the same mathematical modeling that is used to increase our chances of picking the winners is also used to save lives, save money and solve problems in some of our nation’s greatest challenges.

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