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The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing Hardcover – November 6, 2012
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"I just finished reading the most enjoyable business book I've read in a long time - although relegating The Success Equation, by Legg Mason Chief Investment Strategist Michael J. Mauboussin to this category would be a grave disservice, significantly understating its sophistication and selling short its achievement." -- David A. Shaywitz on Forbes.com
About the Author
He is the author of two previous books, Think Twice: Harnessing the Power of Counterintuition and More Than You Know: Finding Financial Wisdom in Unconventional Places and is coauthor, with Alfred Rappaport, of Expectations Investing: Reading Stock Prices for Better Returns.
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It was well done if somewhat different from my expectations. The first 2/3 of the book are essentially a gentle introduction to statistics using examples from sports (sabermetrics), business strategy and investing, on the apparent assumption that most readers will be familiar with at least some of these contexts. I was not expecting this amount of the book to consist of explaining statistical analysis. I did not object as it was exceptionally lucidly written and he does an exceptional job of weaving the statistical points into the three fields he is using for context. The research he references is recent and there is quite a lot of it cited.
The last third was more of the traditional "business book", making specific recommendations about how to use what you've just learned to become more intelligent about pursuing success. There are, however, no ground-breaking insights in here, the potential buyer should note.
The book was shorter than I thought. About 20% of the pages are appendices, etc. As it is lucidly written, it was easy to plow through the roughly 220 pages of actual text. There were several really memorable anecdotes he inserts to illustrate points.
I did, however, feel that several points were not well made. It is, I think, a common problem when writing about statistics - certainly journalists prove that nearly every time they write about a study in some field. It is really hard to keep the discipline of being precise and correct about what are always estimates and tendencies. The last example of this in the book is fresh in my mind so I will use it as the example - he says that "reversion to the mean" means that if you give a test on each of two consecutive weeks, the highest scorers on the first test will tend to get lower grades on the second, and conversely for the lowest scorers. I think this is clearly not true, except where the class is at a fairly even skill level to begin with, which is rare. In my experience, in any more diversified class, students tend to repeat their performance level fairly often and don't revert to the mean. 4.0 students tend to score highly most of the time!
Also, he gets very sloppy discussing the "Matthew effect", which is a name for the phenomenon that "the rich get richer and the poor get poorer". He says this is all attributable to "luck" but does not use any of the statistical methods he discusses earlier to identify luck separately from skill to demonstrate his claim. His examples were poor as well: he cites the victory of VHS over Betamax and Windows OS over unspecific competition which I assume was Apple. He says the competing products were substantially similar and the resulting market share was "out of all proportion" to the cost of producing the marginal product. Obviously those examples are poor because in both cases the winning product was itself superseded or its market share cut into within a few years. Neither is a case of "the rich get richer" or "winner take all" unless you stopped measuring wins in the 90's. It was "winner wins for a while" - until it didn't. And I would disagree that the only relevant distinction is product quality and everything else is "luck". That is what business strategy is all about. MS had a better strategy initially than Apple. Apple was all about making clear it was the anti-IBM and going it alone. Unfortunately the marketplace was not as anti-IBM as Apple thought, and also MS's decision to stick with software turned out to create opportunities for lots of other businesses to step in and share and bear costs of constructing the marginal product while Apple had to fund and build a whole new computer to sell one additional copy of its software. So I don't see that as just plain "luck" at all.
But, having got that off my chest, I thought it was a pretty well written book, although like most business books, it is far better explaining the past than solving for the future.
A key motivation of The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing is the fact that sports offer a rich vein of skill-versus-luck analysis. Sports data, combined with statistical analysis and behavioral psychology, contribute valuably to the study of business management and investing.
The Success Equation covers several compelling themes, including the luck-versus-skill continuum, the importance of sample size, mean reversion and dynamic processes, and key lessons in improving the art of guesswork.
The luck-versus-skill continuum ranges from all luck (e.g., roulette) to all skill (e.g., running marathons). Between these extremes, hockey has a large luck component whereas basketball primarily involves skill. The analysis of this topic draws heavily on athletics because of the vast and detailed records generated by sports and the many practical lessons that can be extracted from them. Recognizing where an activity lies on the luck-versus-skill continuum can shape strategy, hone skills, help in dealing with uncertainty, and improve performance in numerous ways that are relevant to investing.
Sample size is key to understanding luck versus skill. Investors should
consider the sample size (if an activity is controlled mainly by luck, a small sample will not do),
understand history, which helps in skill-based activities more than in luck-based activities, and
categorize events by simple/complex payoff and narrow/extreme outcome.
The complex/extreme combination resembles the “black swans” highlighted in Nassim Taleb’s work. Mauboussin notes that most financial blowups have resulted from naively applying statistical methods in a world of black swans.
Mean reversion and dynamic processes are related. Understanding the mean-reversion process is critical in investing because one can do all the right things but still underperform until the odds catch up. Just as important, however, is recognizing a dynamic process. A change in the process revises the odds and shifts mean reversion to a new mean.
The book ends with 10 key ways to improve one’s chances at guesswork. In addition to the three ways discussed earlier, they include learning from feedback, looking for ways in which situations may interact, and knowing one’s limitations.
Mauboussin’s writing style makes for a highly enjoyable and comprehensible read. Sports-based examples are used effectively throughout. Most readers will readily recognize the differing natures of the various sports and grasp how an understanding of those differences translates into the analysis of investment and business problems. Also commendable is Mauboussin’s conscientious provision of academic references. Some 60 pages, about 20% of the total, are devoted to notes and citations that amplify points and offer background and further reading.
The Success Equation makes a valuable contribution to the literature on decision making when both luck and skill are involved. It is thoroughly researched and well written. I highly recommend the book to anyone interested in making better investment choices.