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СКАЧАТЬ with other companies seeking similar success. The approach is intuitively appealing, which explains why the authors of these studies have sold millions of books.

      Unfortunately, this approach comes with an inherent problem. Some of the companies were lucky, which means that there are no reliable lessons to learn from their successes. Michael Raynor and Mumtaz Ahmed at Deloitte Consulting teamed up with Andrew Henderson at the University of Texas to sort out how skill and luck contribute to the way that companies perform. First, the researchers studied over twenty thousand companies from 1965–2005 to understand the patterns of performance, including what you would expect to see as the result of luck. They concluded that there were more companies that sustained superior performance than luck alone could explain.

      Next, they examined the 288 companies that were featured in thirteen popular books on high performance and tested them to see how many were truly great. Of the companies they were able to categorize, they found that fewer than 25 percent could confidently be called superior performers. Raynor, Ahmed, and Henderson write, “Our results show that it is easy to be fooled by randomness, and we suspect that a number of the firms that are identified as sustained superior performers based on 5-year or 10-year windows may be random walkers rather than the possessors of exceptional resources.”²⁸

      The authors of those how-to studies found success and interpreted it to create lessons that they could peddle to a credulous audience. Yet only a small percentage of the companies they identified were truly excellent. Most were simply the beneficiaries of luck. At the end of the day, the advice for management is based on little more than patterns stitched together out of chance occurrences. You have to untangle skill and luck to know what lessons you can take from history. Where skill is the dominant force, history is a useful teacher. For example, by well-established methods, you can train yourself to play music, speak a language, or compete in athletic games such as tennis and golf. Where luck is the dominant force, however, history is a poor teacher.

      At the heart of making this distinction lies the issue of feedback. On the skill side of the continuum, feedback is clear and accurate, because there is a close relationship between cause and effect. Feedback on the luck side is often misleading because cause and effect are poorly correlated in the short run. Good decisions can lead to failure, and bad decisions can lead to success. Further, many of the activities that involve lots of luck have changing characteristics. The stock market is a great example. What worked in the past may not work in the future.

      An understanding of where an activity is on the luck-skill continuum also allows you to estimate the likely rate of reversion to the mean. Any activity that combines skill and luck will eventually revert to the mean. This means that you should expect a result that is above or below average to be followed by one that is closer to the average. Recall Charlie, the student who knew eighty out of one hundred facts but was tested on only twenty of them. If he scored a 90 on the first test because the teacher happened to select mostly questions he could answer, you would expect the score on the second test to be closer to 80, as his good luck would be unlikely to last.²⁹

      The important point is that the expected rate of reversion to the mean is a function of the relative contributions of skill and luck to a particular event. If what happens is mostly the result of skill, then reversion toward the mean is scant and slow. If you're a highly skilled NBA player making free-throw shots, your shooting percentage will stand well above the average most of the time. Sometimes your performance will move back toward the average, but not by very much. If the outcome is mostly due to luck, reversion to the mean will be pronounced and quick. If you're playing roulette and win five times, you're better off leaving the table, because you can be sure you're going lose as the number of plays increases. These concepts are important and are often overlooked in business, sports, and investing, not to mention in the casino.

      Take another example from sports. Tennis is largely a game of skill. Top professional men players hit in excess of six hundred shots during a best-of-five set match, providing plenty of opportunity for skill to shine through (large sample). As a consequence, the ranking of the best tennis players tends to persist from year to year. For instance, Roger Federer, one of the greatest players of all time, spent a total of 288 weeks—longer than five years—in the number-one spot. A look at the four top-rated players at the end of 2010 reveals that they were the same as at the end of 2009, with the only difference being that the top two players swapped spots. The same four players appeared in 2011. Reversion to the mean is muted because skill exerts the most powerful influence over who wins.

      Baseball is another story. Even though its professional players are extremely skillful, baseball is a sport that involves a lot of luck. A pitcher can throw well but fail to get supporting runs from his teammates and thereby lose a game. A batter can put a ball into play and a slight difference in trajectory will determine whether it's a hit or an out. Over a long, 162-game season, the best teams in baseball rarely win more than 60 percent of their games, as reversion to the mean powerfully drives the outcomes back toward the average. In sharp contrast to tennis, baseball has a lot of randomness. Only the New York Yankees were one of the top four teams in 2009, 2010, and 2011 (based on wins), and they made it by a slim margin in 2010. Because there are nine defensive players on the field at any given time, and each player's performance fluctuates, one player's skill can easily be canceled out by another's mistake, driving the whole system back toward the average. So no matter how skillful the individual players, a system like this tends to look and behave much more like a game of chance than tennis does.

      Naturally, for any particular individual or organization skill will change over time. The performance of a great athlete fades with age and a company's competitive advantage eventually gets whittled away. But from period to period, a sense of the ratio of skill to luck is of great value in anticipating the rate of reversion to the mean.

      Interactions Vary, but the Lessons Remain

      Some of the interactions featured in this book are focused on the individual, including cognitive tasks (music), physical tasks (gymnastics), or tasks in which an individual interacts with a system (the lottery). These activities tend to have a high degree of independence, which means that whatever happens next is not influenced by what happened in the past. In those cases, the skill of the players tends to dictate the results.

      Still other activities have one person or entity competing against a few others. A company launching a new product amid a handful of rivals is one example. So is a team competing in a league, or even the performance of a player on a team. In these instances, what happened in the past does influence the future, a process known as path dependence.

      Finally, there are cases in which one person competes with a crowd. Examples include betting on sports and investing, where an individual pits his or her skill against the collective skill of the crowd. History shows us that crowds can be wise or whimsical.

      So far, I have depicted events as if they follow distributions that are known. For example, de Moivre's equation applies to events that follow a normal, or bell-shaped, distribution but doesn't apply in cases where some events are extreme outliers. The real world is messy, and there are myriad distributions that depart from the simple bell curve, as we will see. But if we approach these activities properly, the effort of untangling skill and luck will yield insights into how to assess past events and anticipate the future.

      Limits of the Methods

      Nassim Taleb offers a useful way to figure out where statistical tools are likely to work and where they fail. He introduces a 2×2 matrix, where the rows distinguish between activities that can have extreme variation and those that have a narrower range of possibilities.³⁰ The narrow distributions are the ones that de Moivre's equation handles superbly. The distribution of stature is a classic example, as the ratio between the tallest and shortest human on record is only 5:1. But extreme variation is a lot more difficult to deal with. For example, the distribution of wealth has extreme outcomes. The net worth of Bill Gates, in excess of $50 СКАЧАТЬ