Exploring The Best NCAA March Madness Brackets

Background

A perfect NCAA March Madness bracket requires predicting all 63 total games played in the March Madness tournament correctly. To date, out of approximately 2,000,000,000 verified brackets that have been completed and millions will be completed this year. Is it possible to get find a perfect bracket among the 9,223,372,036,854,775,808 possible combinations? (If it's going to be possible, we need a lot of help!)

Here at the University of Illinois, Professor Sheldon Jacobson has been working on this problem for the past fifteen years! Today, his BracketOdds model is among the best in the world and has generated brackets that have scored better than or tied the top ESPN-submitted bracket in recent years.

For the first time, we have access to the model and we're going to try and find the perfect bracket and explore every bracket generated by the best model in the world! On our quest to find a perfect bracket, we generated over 200,000,000 men's and women's brackets using this model and you can search them all here!

• Read More from The Office of Data Science Research: "Illini to the Final Four... of Brackets"

This page is currently displaying the men's tournament brackets:

• View our brackets for the NCAA March Madness Women's Tournament

BracketOdds vs. The World

At the end of the tournament, we evaluated every bracket we generated to answer two related questions:

• How did our pool perform versus the total field of ESPN brackets?
• What was the absolute best bracket we produced?

Outperforming ESPN in Equal Number of Brackets

This year, users submitted a record 26,567,887 brackets on ESPN. While our initial work generated over 200,000,000 brackets, we limited our first analysis to the first 26,567,887 generated to ensure a fair, equal-sized comparison.

In this set, bracket #15,458,212 correctly predicted 57 of the 63 games and scored 1,840 points. To put this in perspective, the top-performing bracket on ESPN scored 1,810. Below is our championship bracket from this matched-size sample:

Best Overall Bracket

Broadening our analysis to our full data set -- a total of 200,995,836 unique brackets -- we found that bracket #131,799,411 was our top performer. It achieved a score of 1,850, which is 10 points higher than the best bracket from our initial 26.6 million ESPN-sized sample:

The full breakdown of our top brackets summarized:

Our "Lucky Brackets" Submitted to ESPN

Among our generated pool, we found that there were exactly three specific brackets that the BracketOdds model produced independently three times. We designated these as our "lucky brackets" and, at the suggestion of alumna Michelle Ru, submitted them to the ESPN Tournament Challenge.

One of these lucky brackets placed in the 99.95th percentile of all ESPN entries, scoring 1,620 points and ranking 12,744 out of 26,567,887. While the other two did not perform as well, the strength of the BracketOdds model lies in its ability to provide diverse predictions based on varying tournament outcomes. The following shows our three original "lucky brackets":

Nerding Out With Every Bracket

In the interface below, click any number of teams you want to lock in to see all brackets that include your selection.

• You do NOT need to select all teams -- just the ones you want to filter!
• See the brackets that match your selection below.

Round 1 - East Region

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Round 1 - South Region

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Round 1 - West Region

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Round 1 - Midwest Region

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Bracket Results

Based on your selections, we'll search from every bracket generated using the following pattern:

The Hunt for the Perfect Bracket

All of our brackets are locked in (and also available on GitHub). We are now evaluating the results:

Acknowledgements

The model used to generate brackets found is based off of the 2020 Journal of Quantitative Analysis in Sports pape "Models for generating NCAA men’s basketball tournament bracket pools". A huge thanks to Professor Sheldon Jacobson for encouraging this crazy idea with his BracketOdds model over lunch and to Professor Ian Ludden for sharing this paper, the source code for the model, and the model weights with us and his continued work on this at Rose Hulman.