Why Cinderella is hard to pick in March madness & commodity markets

In 1997, I watched as the unlikely #4 Arizona team knocked off Kentucky in the championship game of the NCAA Tournament and I was forever hooked on the magic of March.

Think about a few of the crazy moments the NCAA tournament has brought us. In 2010, the Butler Bulldogs made the wildly unlikely run to the National Championship game where they lost by 2 to Duke. Florida Gulf Coast’s 2012 run and their Dunk City super powers. In 2013, Louisville’s Kevin Ware’s grotesquely broken leg as he collapsed to the ground after defending a 3 point shot. (I watched it again while writing this and yeah, still gives me chills.)

And a million more inspiring stories about players, and coaches, and teams that find greatness during the tournament. It’s called March Madness because anything can happen.

Which means, not only can the unexpected underdogs make a Cinderella run deep into March, but the inverse is true also — sometimes the titan teams that are “supposed to win” do not. My team lost by 1 point in a Sweet 16 game after being up by THIRTEEN points with 3 minutes left and the strong favorite to win. Don’t worry, I’m obviously over it.

The unpredictability of March may be what makes it fun, but it’s also why only 18 people correctly picked the Sweet Sixteen in 2017 out of 18.8 MILLION completed brackets.

Said differently, March Madness is a volatile situation. Which makes it hard to predict outcomes.

And some of the same decision fallacies that impact the most engaged basketball fans while filling out their brackets for March Madness also impact individuals responsible for buying and selling commodities that move with volatile markets.

What are some of those decision making fallacies that humans have to overcome?

Hot hand fallacy. This is the sometimes fallacious belief a person who experiences success with a random event has a greater probability of further success in additional attempts.
Selection bias. This is the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed.
Survivorship bias. This is the logical error of concentrating on the people or things that made it past some selection process and overlooking those that did not, typically because of their lack of visibility. This can lead to false conclusions in several different ways.
Texas sharpshooter fallacy. This is an informal fallacy which is committed when differences in data are ignored, but similarities are stressed. From this reasoning, a false conclusion is inferred.

Not only can these fallacies trip up even the most ESPN-tethered & insightful March Madness fans, they can trip up the most experienced individuals who are buying and selling commodities.

That’s why predictive analytics are entering the scene in a powerful way in both sports and across the commodities value chain, because analytics enable people to glean insights they wouldn’t otherwise be able to find.

Download this white paper to look at some specific ways technologies around predictive analytics are changing how sports organizations recruit, manage, and coach…and what it means for you in the commodities business.

This article was originally published on Meatingplace.