FIX THE PITCH

×

Fix the Pitch

How to craft compelling, dazzling pitchbooks. Thoughts, ideas, and inspiration to help construct advanced financial analysis, build stunning data visualizations and tips for mastering client meetings.

Fix your pitchbook


Join the Fix the Pitch newsletter

Popular tags


March Madness predictions: Kansas most likely to win

Eric RattnerEric Rattner

March is here, and flowers are blooming; so too are the hopes and dreams of fans of the 68 schools participating in this year’s NCAA Tournament: Will this be the year their college basketball team becomes the Cinderella story that dominates water cooler discussions around the country?

It’s March Madness time. For the next month, office productivity will drop as millions of computer monitors are repurposed from examining spreadsheets and building presentations to live streaming games.

The joy of March Madness lies in the nearly limitless possibilities offered by its large-field bracket format and the resultant parity between sports nuts and non-sports fans alike brought about by its inherent randomness. Whether or not you know the Spartans reside in East Lansing Michigan, not ancient Greece, you have an equal shot at correctly picking a crazy upset and earning bragging rights as your friends’ and coworkers’ brackets get busted.

It’s expected that 40 million people will fill out brackets this year. Some will follow expert advice, some will base selections on team mascot cuteness, and others will simply follow the seeds. But for those of us already drawn to sports analytics, March Madness offers an opportunity to double down on analysis, dig into data, and take a quantitative approach to understanding the tournament’s dynamic, which is exactly what we did at Pellucid.

Enjoy

How it was made:

We sifted through a wealth of advanced metrics to specify a statistical model for predicting the outcome of a matchup between any two Division I teams. Using the factors shown in the chart above on a team-by-team basis, the margin of victory for every Division I game played this season was regressed against the differences in these metrics for the two competing teams in those contests. In doing so, we were able to explain over 50% of variance in observed margins of victory, as measured by the final regression’s adjusted R-squared.

The model was applied dynamically to this year’s field and all possible potential matchups, simulating the entire tourney a million times and counting how often each team advanced to each round (with the probability of ultimately winning it all shown on the last axis of our visualization). Our analysis determined the most likely result to be Kansas edging out Michigan State in the finals.

For juicier upset and our full set of picks, check out Pellucid’s “maximum-likelihood” bracket.

How it was calculated:

There are only so many ways to quantitatively predict the outcomes of sporting contests, so most team rating systems produce very similar rankings. The significant correlations among these systems makes it difficult to include them all in a multivariable regression. As such, we focused on metrics that assess differing components of team success, the combination of which holistically summarize a team’s strength and likelihood of winning a given game.

For additional insight baked into the data we haven’t shown (for example, our simulation shows Kentucky getting screwed over by the draw), email me at eric@pellucid.com.

Pellucid blends technology and design to create beautiful, client-ready pitchbook content. Take a demo at www.pellucid.com.

Eric Rattner
Author

Eric Rattner

Investment banking lifer and native New Yorker. Broadway and movie buff and aspiring soccer star. Building innovative, beautiful charts filled with really smart data analysis.

Comments