Sheldon Jacobson: March Madness and Advanced Analytics


March Madness begins on March 15. Blue blood programs like Villanova, Duke and Kansas are tournament locks. Mid-majors like Loyola-Chicago and Murray State are hoping to create upheaval and move into the second weekend. Virginia Commonwealth and BYU are just hoping for a spot in the Big Dance.

College basketball fans, and many others, participate in the tournament by completing brackets. With 64 teams in the main draw, once the top four have been played, there are 9,223,372,036,854,775,808 possible ways to go.

Most people cannot imagine the magnitude of this number, over 9.2 quintillion. If every person in the United States filled 900 slices per second, it would take about a year to cover each slice. Calculations like this give insight into how advanced analytics can be used to provide insight into numbers beyond anyone’s imagination.

Not all of these slices have the same probability of occurring. Is there a way to parse and identify the likely number of parentheses? Advanced analysis can also be useful here. Just like when we are in a grocery store and want to check out, we are looking for the lane with the shortest, fastest moving queue. It’s advanced analytical thinking, though we rarely recognize it as such. This same thinking can be used to reduce the number of possible parentheses to the number of reasonably plausible parentheses, although this number is still quite large.

Historical data since 1985 shows that it makes sense to pick highly ranked teams to reach the Final Four. What is the ideal combination of seeds? How about two No. 1 seeds, a No. 2 seed and another team seed between No. 3 and No. 12? Advanced parsing encourages the data to talk, and when constructing parentheses, listening to the data makes sense. It’s the same type of data-driven thinking used to calculate credit scores, which assess the likelihood of a loan being repaid. A good credit rating means you are likely to be a good credit risk. Using historical data to identify trends can tip the scales in your favor.

But what about upheavals? They are what make games fun to watch and attract the most attention. When a #12 seeded team defeats a #5 seeded team, how can this be predicted? Disruptions are guaranteed. The hardest part is knowing which teams they will be on. It’s about making informed decisions under conditions of uncertainty, an important part of advanced analytics and a challenge we all face every day.

Take for example the weather forecast. It gives “chances” of rain and snow, but these weather events may never occur. Nothing is certain and absolute, yet we must make decisions to prepare for the unexpected. Looking for clues to help inform such decisions is like looking for team stats that might predict an upset. Difficult? Yes. Possible? Certainly. Coping and reacting during the pandemic shows how difficult it can be to make decisions and choices with incomplete information.

Although the advanced analytics are obviously not visible directly on the basketball court during March Madness, every fan and spectator can use such reflection to fill their parentheses and perhaps enjoy the games.

Data analysis and decision making under uncertainty are in the crosshairs of advanced analytics. Each upheaval is an opportunity to reveal the unexpected. Each team confrontation is an opportunity to demonstrate advanced analytical knowledge.

Advanced analytics is ubiquitous in our lives, but many rarely interpret it as such. With March Madness, advanced analytics are fully displayed for everyone, young and old, to use to fill in a parenthesis. When the tournament begins, advanced analytics may not explain why March Madness captures everyone’s attention, but it’s certainly at work when we put pen to paper in our office pool.


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