The Success Equation. Michael J. Mauboussin

have little to directly say about that or other issues of love, health, and happiness. We need to define the activity we're talking about and what measures we need to use to evaluate that activity effectively.

      In his book The Theory of Gambling and Statistical Logic, Richard Epstein, a game theorist trained in physics, notes that there is no way to assure that you'll succeed if you participate in an activity that combines skill and luck. But he does say, “It is gratifying to rationalize that we would rather lose intelligently than win ignorantly.”⁸ Luck may or may not smile on us, but if we stick to a good process for making decisions, then we can learn to accept the outcomes of our decisions with equanimity.

      CHAPTER 1

      SKILL, LUCK, AND THREE EASY LESSONS

      LET ME START with a story that is likely to be familiar to you.

      One of the greatest computer programmers of all time grew up near Seattle, Washington. He saw an upstart company, Intel, making computers on a chip and was among the first people to see the potential of these so-called microcomputers. He dedicated himself to writing software for the new device and, by one account, “He wrote the software that set off the personal computer revolution.”¹

      In the mid 1970s, he founded a company to sell software for microcomputers. In the early history of the company, “the atmosphere was zany,” and “people came to work barefoot, in shorts,” and “anyone in a suit was a visitor.”² But the company was soon highly profitable, and by 1981 its operating system had a dominant share of the market for personal computers that used Intel microprocessors.

      For all of its early triumphs, the company's watershed moment came when IBM visited in the summer of 1980 to discuss an operating system for its new PC. After some negotiation, the two companies struck a deal. In August 1981, retailers offered the company's software alongside the brand new IBM PC, and the company's fate was sealed. The rest is history, as they say.

      In case this story's not familiar, here's the ending. This pioneer of computer technology entered a biker bar in Monterey, California, on July 8, 1994, wearing motorcycle leathers and Harley-Davidson patches. What happened next is unclear, but he suffered a traumatic blow to the head from either a fight or a fall. He left under his own power but died three days later from the injury, complicated by his chronic alcoholism. He was fifty-two years old. He is buried in Seattle and has an etching of a floppy disk on his tombstone. His name is Gary Kildall.³

      You'd be excused for thinking that the first part of the story is about Bill Gates, the multibillionaire founder of Microsoft. And it is certainly tantalizing to ask whether Gary Kildall could have been Bill Gates, who at one point was the world's richest man. But the fact is that Bill Gates made astute decisions that positioned Microsoft to prevail over Kildall's company, Digital Research, at crucial moments in the development of the PC industry.

      When IBM executives first approached Microsoft about supplying an operating system for the company's new PC, Gates actually referred them to Digital Research. There are conflicting accounts of what happened at the meeting, but it's fairly clear that Kildall didn't see the significance of the IBM deal in the way that Gates did.

      IBM struck a deal with Gates for a lookalike of Kildall's product, CP/M-86, that Gates had acquired. Once it was tweaked for the IBM PC, Microsoft renamed it PC-DOS and shipped it. After some wrangling by Kildall, IBM did agree to ship CP/M-86 as an alternative operating system. IBM also set the prices for the products. No operating system was included with the IBM PC, and everyone who bought a PC had to purchase an operating system. PC-DOS cost $40. CP/M-86 cost $240. Guess which won.

      But IBM wasn't the direct source of Microsoft's fortune. Gates did cut a deal with IBM. But he also kept the right to license PC-DOS to other companies. When the market for IBM PC clones took off, Microsoft rocketed away from the competition and ultimately enjoyed a huge competitive advantage.

      When asked how much of his success he would attribute to luck, Gates allowed that it played “an immense role.” In particular, Microsoft was launched at an ideal time: “Our timing in setting up the first software company aimed at personal computers was essential to our success,” he noted. “The timing wasn't entirely luck, but without great luck it wouldn't have happened.”⁴

      Defining Skill and Luck

      The first step in untangling skill and luck is to define the terms. This is not a simple task and can quickly devolve into heated philosophical debates.⁵ We can avoid those, since pragmatic definitions are all that we need to think clearly about the past, present, and future results of our actions and to improve the way we make the decisions that lead to those actions.

      But first things first. Before we can speak of skill and luck, we have to settle on the specific activity we're talking about. We can analyze what athletes, executives, or investors do. We just have to be clear on what elements of performance we are considering. Next, we want to agree on the measures of performance. For athletes, it is winning games. For executives, it is developing strategies that create value. The benefit of measurement is that it allows us to assign specific values to skill and luck.

      Now we can turn to definitions.

      Luck

      Let's start with luck.

      We can probably do a bit better than the average dictionary that defines luck as “events or circumstances that work for or against an individual.”⁶ This is a good starting point, but we can be a little more specific. Luck is a chance occurrence that affects a person or a group (e.g., a sports team or a company). Luck can be good or bad. Furthermore, if it is reasonable to assume that another outcome was possible, then a certain amount of luck is involved. In this sense, luck is out of one's control and unpredictable.⁷

      For example, suppose that a teacher asks her students to learn one hundred facts. One student—call him Charlie—memorizes eighty of those facts, figuring that he'll always score 80 and earn a grade of B. Charlie likes this course, but his life doesn't depend on it, as long as he doesn't get a C. In this school, if you get a C, you fail the class. So a B is good enough for Charlie. Think of his strategy as employing true skill, because luck plays no role in how Charlie will perform on the test. He either knows the answer or he doesn't. And he can predict the consequences of his efforts.

      However, instead of asking the class for all hundred facts, the devious teacher writes her test by randomly selecting twenty pieces of information out of the one hundred. Charlie's entire score is now dependent on which of those twenty facts match the ones he memorized. If you look at his predicament statistically, he has about a two-thirds chance of scoring somewhere between 75 and 85 percent. A grade of 85 would be okay, a high B. But a grade of 75 would not. Making matters worse, he has about a 30 percent chance of scoring either 90 or higher or 70 or lower. Suddenly, his perfect knowledge of those eighty facts can't shield him from luck.

      His performance on the test is beginning to seem like a crap shoot. He'd be fine with scoring 90, of course, but he'll be doomed if he scores 70 or lower. In theory, Charlie could score zero if the teacher, just by chance, chose only the twenty facts that he failed to memorize. He could also score 100 if she chose none of those twenty. But the probabilities of those two extremes are vanishingly small. So Charlie's skill can easily be measured as 80 percent of perfect under one set of conditions. But under a second set of conditions, his score can vary wildly. Moreover, under the second set of conditions, measuring his skill in any meaningful way based solely on his score is much more difficult.

      The second set of conditions introduces the element of luck into the process. And it satisfies the definition as I've stated it so far.

       The grade affects the student.

       It is either good or bad (he scores either above or below 80).

       It is reasonable to assume that another result was possible if only the teacher had selected different questions.

      Standardized tests scores, including the SAT Reasoning Test used for admission to college in the United States, reflect the influence of luck in the same way. That's why the admissions officers who