But their measures and intuition often failed to single out players who were effective but didn’t look the role. Most scouts had been around the game nearly all their lives and had developed an intuitive sense of a player’s potential and of which statistics mattered most. Businesses continue to use the wrong statistics.īefore the A’s adopted the methods Lewis describes, the team relied on the opinion of talent scouts, who assessed players primarily by looking at their ability to run, throw, field, hit, and hit with power. The book was published nearly a decade ago, and its business implications have been thoroughly dissected. Moneyball, the best seller by Michael Lewis, describes how the Oakland Athletics used carefully chosen statistics to build a winning baseball team on the cheap.
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And it will show you how to choose the best statistics for your business goals. This article will reveal how this mistake permeates businesses-probably even yours-driving poor decisions and undermining performance. As a result, our strategic and resource allocation decisions didn’t support that goal. The statistic we relied on to assess our performance-revenues-was disconnected from our overall objective of profitability. What happened? We made a mistake that’s exceedingly common in business: We measured the wrong thing. Indeed, customers in the middle of the pack, which didn’t demand substantial resources, were more profitable than the giant we fawned over. The results were striking and counterintuitive: Our largest customer was among our least profitable. So we estimated the cost we incurred servicing each major client. Part of my charge was to understand the division’s profitability by customer. We were committed to keeping the 800-pound gorilla happy. We shuttled our researchers to visit with its analysts and portfolio managers, dedicated capital to ensure that its trades were executed smoothly, and recognized its importance in the allocation of IPOs. While we had hundreds of clients, one mutual fund company was our largest. I was in the equity division, which generated fees and commissions by catering to investment managers and sought to maximize revenues by providing high-quality research, responsive trading, and coveted initial public offerings.
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The drivers of value creation change, and so must your statistics.Ībout a dozen years ago, when I was working for a large financial services firm, one of the senior executives asked me to take on a project to better understand the company’s profitability. You must also regularly reevaluate your metrics. To choose the right statistics, you must define your governing objective, assess the financial and nonfinancial drivers of that objective, and figure out which employee activities support those drivers. They have two defining characteristics: They are persistent, showing that the outcome of a given action at one time will be similar to the outcome of the same action at another time, and they are predictive-that is, there is a causal relationship between the action the statistic measures and the desired outcome. The most useful statistics reliably reveal cause and effect. Yet executives cling to those metrics because they are overconfident in their intuition, they misattribute the causes of events, and they do not escape the pull of the status quo. Theory and empirical research show only a shaky connection between value creation and two of the most popular performance measures: earnings per share (EPS) growth and sales growth. They had been measuring the wrong thing, and executives may be making the same mistake. But as Michael Lewis describes in Moneyball, the Oakland Athletics discovered that the metric the team’s scouts used to choose players had nothing to do with whether those players would score runs. When it comes to assessing performance, business executives can be a lot like old-time baseball scouts, who have been around so long that they’ve developed a gut feel for which statistics matter most.