The Success Equation. Michael J. Mauboussin

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      Source: www.olympicgamesmarathon.com and analysis by author.

      The two-jar model shows that luck can overwhelm skill in the short term if the variance of the distribution of luck is larger than the variance of the distribution of skill. In other words, if everyone gets better at something, luck plays a more important role in determining who wins. Let's return to that model now.

      The Ingredients of an Outlier

      Note that the extreme values in the two-jar model are −7 and 7. The only way to get those values is to combine the worst skill with the worst luck or the best skill with the best luck. Since the poorest performers generally die off in a competitive environment, we'll concentrate on the best. The basic argument is easy to summarize: great success combines skill with a lot of luck. You can't get there by relying on either skill or luck alone. You need both.

      This is one of the central themes in Malcolm Gladwell's book, Outliers. As one of his examples, Gladwell tells the story of Bill Joy, the billionaire cofounder of Sun Microsystems, who is now a partner in the venture capital firm Kleiner Perkins. Joy was always exceptionally bright. He scored a perfect 800 on the math section of the SAT and entered the University of Michigan at the age of sixteen. To his good fortune, Michigan had one of the few computers in the country that had a keyboard and screen. Everywhere else, people who wanted to use a computer had to feed punched cards into the machine to get it to do anything (or more likely, wait for a technician to do it). Joy spent an enormous amount of time learning to write programs in college, giving him an edge when he entered the PhD program for computer science at the University of California, Berkeley. By the time he had completed his studies at Berkeley, he had about ten thousand hours of practice in writing computer code.¹² But it was the combination of his skill and good luck that allowed him to start a software company and accrue his substantial net worth. He could have been just as smart and gone to a college that had no interactive computers. To succeed, Joy needed to draw winning numbers from both jars.

      Gladwell argues that the lore of success too often dwells on an individual's personal qualities, focusing on how grit and talent paved the way to the top. But a closer examination always reveals the substantial role that luck played. If history is written by the winners, history is also written about the winners, because we like to see clear cause and effect. Luck is boring as the driving force in a story. So when talking about success, we tend to place too much emphasis on skill and not enough on luck. Luck is there, though, if you look. A full account of these stories of success shows, as Gladwell puts it, that “outliers reached their lofty status through a combination of ability, opportunity, and utter arbitrary advantage.”¹³ This is precisely what the two-jar model demonstrates.

      Outliers show up in another way. Let's return to Stephen Jay Gould, baseball, and the 1941 season. Not only was that the year that Ted Williams hit .406, it was the year that Joe DiMaggio got a hit in fifty-six straight games. Of the two feats, DiMaggio's streak is considered the more inviolable.¹⁴ While no player has broached a .400 batting average since Williams did, George Brett (.390 in 1980) and Rod Carew (.388 in 1977) weren't far off. The closest that anyone has approached to DiMaggio's streak was in 1978, when Pete Rose hit safely in forty-four games, only 80 percent of DiMaggio's record.

      “Long streaks are, and must be, a matter of extraordinary luck imposed on great skill,” wrote Gould.¹⁵ That's exactly how you generate a long streak with the two jars. Here's a way to think about it: Say you draw once from the jar representing skill and then draw repeatedly from the other. The only way to have a sustained streak of success is to start with a high value for skill and then be lucky enough to pull high numbers from then on to represent your good luck. As Gould emphasizes, “Long hitting streaks happen to the greatest players because their general chance of getting a hit is so much higher than average.”¹⁶ For instance, the probability that a .300 hitter gets three hits in a row is 2.7 percent (= .3³) while the probability that a .200 hitter gets three hits in a row is 0.8 percent (= .2³). Good luck alone doesn't carry the day. While not all great hitters have streaks, all of the records for the longest streaks are held by great hitters. As a testament to this point, the players who have enjoyed streaks of hits in thirty or more consecutive games have a mean batting average of .303, well above the league's long-term average.¹⁷

      Naturally, this principle applies well beyond baseball. In other sports, as well as the worlds of business and investing, long winning streaks always meld skill and luck. Luck does generate streaks by itself, and it's easy to confuse streaks due solely to luck with streaks that combine skill and luck. But when there are differences in the level of skill in a field, the long winning streaks go to the most skillful players.

      Reversion to the Mean and the James-Stein Estimator

      Using the two jars also provides a useful way to think about reversion to the mean, the idea that an outcome that is far from the average will be followed by an outcome that is closer to the average. Consider the top four combinations (−3 skill, 4 luck; 3 skill, 0 luck; 0 skill, 4 luck; and 3 skill, 4 luck) which sum to 15. Of the total of 15, skill contributes 3 (−3, 3, 0, 3) and luck contributes 12 (4, 0, 4, 4). Now, let's say you hold on to the numbers representing skill. Your skill remains the same over the course of this exercise. Now you return the numbers representing luck to the jar and draw a new set of numbers. What would you expect the new sum to be? Since your level of skill remains unchanged at 3 and the expected value of luck is zero, the expected value of the new outcome is 3. That is the idea behind reversion to the mean.

      We can do the same exercise for the bottom four outcomes (−3 skill, −4 luck; 0 skill, −4 luck; −3 skill, 0 luck; and 3 skill, −4 luck). They add up to −15, and the contribution from skill alone is −3. Here, also, with a new draw the expected value for luck is zero, so the total goes from −15 to an expected value of −3. In both cases, skill remains the same but the large contributions from either good luck or bad luck shrink toward zero.

      While most people seem to understand the idea of reversion to the mean, using the jars and the continuum between luck and skill can add an important dimension to this thinking. In the two-jar exercise, you draw only once from the jar representing skill; after that, your level of skill is assumed to remain the same. This is an unrealistic assumption over a long period of time but very reasonable for the short term. You then draw from the jar representing luck, record your value, and return the number to the jar. As you draw again and again, your scores reflect stable skill and variations in luck. In this form of the exercise, your skill ultimately determines whether you wind up a winner or a loser.

      The position of the activity on the continuum defines how rapidly your score goes toward an average value, that is, the rate of reversion to the mean. Say, for example, that an activity relies entirely on skill and involves no luck. That means the number you draw for skill will always be added to zero, which represents luck. So each score will simply be your skill. Since the value doesn't change, there is no reversion to the mean. Marion Tinsley, the greatest player of checkers, could win all day long, and luck played no part in it. He was simply better than everyone else.

      Now assume that the jar representing skill is filled with zeros, and that your score is determined solely by luck; that is, the outcomes will be dictated solely by luck and the expected value of every incremental draw for skill will be the same: zero. So every subsequent outcome has an expected value that represents complete reversion to the mean. In activities that are all skill, there is no reversion to the mean. In activities that are all luck, there is complete reversion to the mean. So if you can place an activity on the luck-skill continuum, you have a sound starting point for anticipating the rate of reversion to the mean.

      In real life, we don't know for sure how skill and luck contribute to the results when we make decisions. We can only observe what happens. But we can be СКАЧАТЬ