Michael Jordan is considered to be the best basketball player ever. With 6 NBA titles, 5 MVP awards and 15 All-Star game appearances, it’s hard to argue that anybody has ever dominated a sport like he did.
Yet he wasn’t always so great. He didn’t make his high school varsity team as a sophomore and wasn’t the first player picked in the NBA draft (he was third). He showed promise as a young player, but then again so do a lot of people who never amount to much.
We often have to make decisions about things like a young Michael Jordan. Many new innovations, opportunities and employees show great potential, but we can only pick a few. So we need to use data in order to make predictions about the future. How we do that can mean the difference between success and failure, so we better get it right.
A Simple Test
Probably the simplest way to assess a young Michael Jordan would be to test his ability. The NFL regularly does so in its famed scouting combine, just like firms like Google give prospective employees aptitude tests and most businesses regularly commission research to evaluate new market opportunities.
So we might want to test a young Michael by asking him to shoot free throws. We’d want to control conditions, so we’d make sure that the gym was at a standard temperature, had adequate lighting and so on. We’d also want to make sure that we gave him enough tries so that a few lucky or unlucky shots wouldn’t overly affect our judgment.
The average NBA free throw percentage is 75%, so a sample size of 100 should give us a reasonable assessment. Statistically speaking, we could be 95% sure that the error would be within a 10% range, which seems pretty good.
Of course, one out of twenty times (i.e. the 5% that the confidence interval doesn’t cover) we’d go down in history as the jackass who overlooked Michael Jordan because he missed a few free throws. Of course, we’d say that we were just being “data driven” and “running the numbers,” but we’d still be a jackass and everyone would know it.
Can He Play?
We could also have experts watch Michael play. This would seem to be a solid, common sense approach. No fancy numbers, just people who know the game. Yet experts have their own biases. Many scouts overlooked Jeremy Lin, although he was a very effective player, just because he didn’t look impressive or do anything amazingly athletic.
In fact, as Philip Tetlock found in his 20 year study of political pundits, expert predictions are often no better than flipping a coin. The problem is that when people have to make long term predictions, they lack regular feedback and so aren’t able to learn from mistakes. The result is that they end up substituting one question for another.
To see what I mean, take a look at what longtime pundit Peggy Noonan, who has closely observed countless races, wrote one day before the 2012 Presidential election.
Romney’s crowds are building—28,000 in Morrisville, Pa., last night; 30,000 in West Chester, Ohio, Friday. It isn’t only a triumph of advance planning: People came, they got through security and waited for hours in the cold. His rallies look like rallies now…
…All the vibrations are right… Something is roaring back…
Is it possible this whole thing is playing out before our eyes and we’re not really noticing because we’re too busy looking at data on paper instead of what’s in front of us? Maybe that’s the real distortion of the polls this year: They left us discounting the world around us.
And there is Obama, out there seeming tired and wan, showing up through sheer self discipline.
To her, the “vibrations” felt right for Romney, but not for Obama and that was enough reason to ignore the data. In much the same way, scouts ignored Jeremy Lin because he didn’t look like a basketball superstar is supposed to and business executives plunge into deals because they gut tells them to.
Millions of dollars can be lost just because someone had the wrong thing for lunch.
Okay, Let’s Kill Off The Experts….
So we have a dilemma. Controlled research is costly, backward looking and prone to error, while experts are subject to cognitive biases and substituting one question for another (e.g. mistaking the “vibrations” among 30,000 people at a rally in rural Ohio for an accurate barometer of how 150 million voters strewn across a continent will act).
There is another way. A relatively new set of techniques collectively known as big data, which are able to collect and analyze massive amounts of information in real time, updating predictions as the facts on the ground change.
Google flu trends, for instance, tracks the search terms of millions of people and can predict epidemics better than doctors can.. Obama’s team ran 62,000 simulations per night throughout the campaign. Companies like Facebook and Amazon run thousands of experiments among millions of consumers to determine what will drive them to act.
These big data methods are being augmented by the Web of Things, a new global neurological system made up of cheap sensors embedded into everything from smartphones to delivery trucks to medical devices. We can imagine that in the future athletes will be monitored through wearable sensors plugged into algorithms.
The Real Secret of Michael Jordan’s Success
In 1997, IBM’s chess playing computer Deep Blue bested Garry Kasparov, the world’s greatest human player. In 2011, another IBM computer, Watson, beat two all-time champions on the game show Jeopardy. In the race against the machines, we appear to be losing and it’s scary.
However, the truth is that we’re not really racing against the machines at all, we’re creating with them. In 2005, a new type of chess match was held and the winner was not a grand master or a supercomputer, but a pair of talented amateurs running three separate simulations at once.
As our technology improves so do we. The best times of an Olympic champion runner of 100 years ago wouldn’t be a match for a talented high schooler today. Our intelligence is improving at a rate of 3% a decade, making a gifted individual of a few generations ago merely average now.
And that brings us to the true secret to Michael Jordan’s success, the thing that can’t be detected through statistical data or expert theories. He had an intense desire to be the very best player in the world. He didn’t just spend years of endless toil, he engaged in deliberate practice, training to improve the weakest, most uncomfortable areas of his play.
Future Michael Jordans will have tools that he didn’t, like motion capture, computer simulations and a plethora of data to aid their deliberate practice, just as future business leaders will not be the ones with the most far reaching vision, but those that can best navigate the Bayesian process of curating thousands of simulated strategies.
The promise of our technological future lies in our own better selves.
image credit: caretoplaysports.com
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Greg Satell is an internationally recognized authority on Digital Strategy and Innovation. He consults and speaks in the areas of digital innovation, innovation management, digital marketing and publishing, as well as offshore web and app development. His blog is Digital Tonto and you can follow him on Twitter.