![]() But there is no denying the vast increase in the range and depth of information that’s routinely captured about how we behave, and the new kinds of analysis that this enables. As a piece of business jargon, and even more so as an invocation of coming disruption, the term has quickly grown tiresome. Predictive statistical analysis, harnessed to big data, appears poised to alter the way millions of people are hired and assessed. What would seem at first glance to be nothing but a memorable tale about baseball may turn out to be the opening chapter of a much larger story about jobs. In the years that followed, team after team began to use detailed predictive models to assess players’ potential and monetary value, and the early adopters, by and large, gained a measurable competitive edge over their more hidebound peers. The team’s success, in turn, launched a revolution. Only the mighty Yankees, who had spent three times as much on player salaries, won as many games. The A’s, a small-market team with a paltry budget, ripped off the longest winning streak in American League history and rolled up 103 wins for the season. What happened next has become baseball lore. The previous year, Beane had turned his back on his scouts and had instead entrusted player-acquisition decisions to mathematical models developed by a young, Harvard-trained statistical wizard on his staff. ![]() In 2003, thanks to Michael Lewis and his best seller Moneyball, the general manager of the Oakland A’s, Billy Beane, became a star.
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