Michael Jordan is widely acclaimed as 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 that 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.
Managers often have to make decisions about things like a young Michael Jordan. Many new innovations, market opportunities and employees show great potential, but we can only pick a few. In other words, we need to use the data we have in order to make predictions about the future. How we do that can mean the difference between success and failure.
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, many firms like Google GOOG -0.26% give prospective employees aptitude tests and 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 between five percentage points either way, which seems pretty good.
Of course, one out of twenty times (i.e. 5%) we’d go down in history as the jackass who overlooked Michael Jordan because he missed a few free throws, so we’d probably want to improve our accuracy if we could.
Can He Play?
Another approach would be to have experts watch Michael Jordan play. This would seem to be a solid, common sense approach, but it is far from foolproof because even experts have their own biases.
For instance, many scouts overlooked Jeremy Linbecause, although he was a very effective player, he didn’t look impressive or do anything amazingly athletic.
In fact, as Philip Tetlock, showed in his 20 year long 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 “feel right.”
Okay, Let’s Kill Off The Experts….
So we have a dilemma. Controlled research is expensive and far from foolproof, while experts, being human, are subject to cognitive bias and substituting one question (i.e. the actions of 150 million voters strewn across a continent), for another (the “vibrations” generated by 30,000 people at a rally in Ohio).
There is another way: big data. Google flu trendstracks the search terms of millions of people. Obama’s team ran 62,000 simulations per night throughout the campaign. Companies likeFacebook FB -1.02% 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 theWeb of Things, a new global neurological system made up of dirt cheap sensors and massive server farms embedded in everything from smartphones to delivery trucks to medical devices. We can imagine in the future, basketball players will be monitored through chips in their shoes.
The point isn’t to be “right” anymore, but to be less wrong over time and technology is enabling us to collect and process millions of data points. The age of controlled studies and small samples is giving way to the analysis of real world data in real time.
The Real Secret of Michael Jordan’s Sucsess
In 1997, Deep Blue, IBM IBM +0.07%’s chess playing computer bested Garry Kasparov, the world’s best 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 racingagainst the machines at all, we’re creating withthem. 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 technology improves so do we. An Olympic champion runner of 100 years ago wouldn’t match the times of a talented high schooler of 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 aspect that can’t be detected through statistical data or expert theories: He had an intense desire to be the best basketball 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.
The Michael Jordans of the future will have tools that he didn’t, like motion capture, computer simulations and a plethora of data to aid their deliberate practice, just as business leaders of the future 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 better selves.
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