Cricket ball.[/caption] Over at ReadWrite, Matt Asay makes an interesting point about the limits of so-called “Big Data”: although sports is often heralded as one of the most exploitable arenas for data analytics (thanks in no small part to the popularity of “Moneyball”), teams that slavishly rely on software and datasets to improve their performances still risk losing big, thanks to a slew of intangible factors. “Data complements decisions, but shouldn't rule them, because data is never truly objective,” Asay wrote. “Choosing which data to collect is a human judgment—so, too, are the questions we ask of it.” Asay pointed out the recent example of English cricket coach Andy Flower, who’d read “Moneyball” and enjoyed it so much that he took its key lesson—that with a little analytical derring-do, any team can pick the right players to advance its cause further than otherwise—and applied it to his own players. Even as he filtered and sorted and compared data, however, he failed to take into account such factors as intuition and what ESPN called “flair—either in the office or on the cricket field.” In one example, Flower’s team ignored how cricket players train, which led to playing-field ruination when they toured Australia. “The selection of three beanpole quick bowlers to tour Australia was rooted in data that showed such bowlers were most likely to thrive in Australia,” as ESPN recounted. “The ECB looked at the characteristics of the best quick bowlers—delayed delivery, braced front leg and so on, and then tried to coach those virtues into their own players, seemingly not [realizing] it was too late; you can't change those things once bowlers are more than about 15.” Flower isn’t the only example of an overreliance on data; executives and entrepreneurs of all persuasions and industries have been known to bet far too much on a particular dataset or analytics-produced insight, only to fail to see another angle or twist before it was far too late. It’s an old lesson: data can take you far, but often a right judgment comes down to human skill, intuition, experience, or some combination thereof. Image: Brian A Jackson/Shutterstock.com 'Moneyball' and the Limits of Data
[caption id="attachment_17871" align="aligncenter" width="500"] Cricket ball.[/caption] Over at ReadWrite, Matt Asay makes an interesting point about the limits of so-called “Big Data”: although sports is often heralded as one of the most exploitable arenas for data analytics (thanks in no small part to the popularity of “Moneyball”), teams that slavishly rely on software and datasets to improve their performances still risk losing big, thanks to a slew of intangible factors. “Data complements decisions, but shouldn't rule them, because data is never truly objective,” Asay wrote. “Choosing which data to collect is a human judgment—so, too, are the questions we ask of it.” Asay pointed out the recent example of English cricket coach Andy Flower, who’d read “Moneyball” and enjoyed it so much that he took its key lesson—that with a little analytical derring-do, any team can pick the right players to advance its cause further than otherwise—and applied it to his own players. Even as he filtered and sorted and compared data, however, he failed to take into account such factors as intuition and what ESPN called “flair—either in the office or on the cricket field.” In one example, Flower’s team ignored how cricket players train, which led to playing-field ruination when they toured Australia. “The selection of three beanpole quick bowlers to tour Australia was rooted in data that showed such bowlers were most likely to thrive in Australia,” as ESPN recounted. “The ECB looked at the characteristics of the best quick bowlers—delayed delivery, braced front leg and so on, and then tried to coach those virtues into their own players, seemingly not [realizing] it was too late; you can't change those things once bowlers are more than about 15.” Flower isn’t the only example of an overreliance on data; executives and entrepreneurs of all persuasions and industries have been known to bet far too much on a particular dataset or analytics-produced insight, only to fail to see another angle or twist before it was far too late. It’s an old lesson: data can take you far, but often a right judgment comes down to human skill, intuition, experience, or some combination thereof. Image: Brian A Jackson/Shutterstock.com
Cricket ball.[/caption] Over at ReadWrite, Matt Asay makes an interesting point about the limits of so-called “Big Data”: although sports is often heralded as one of the most exploitable arenas for data analytics (thanks in no small part to the popularity of “Moneyball”), teams that slavishly rely on software and datasets to improve their performances still risk losing big, thanks to a slew of intangible factors. “Data complements decisions, but shouldn't rule them, because data is never truly objective,” Asay wrote. “Choosing which data to collect is a human judgment—so, too, are the questions we ask of it.” Asay pointed out the recent example of English cricket coach Andy Flower, who’d read “Moneyball” and enjoyed it so much that he took its key lesson—that with a little analytical derring-do, any team can pick the right players to advance its cause further than otherwise—and applied it to his own players. Even as he filtered and sorted and compared data, however, he failed to take into account such factors as intuition and what ESPN called “flair—either in the office or on the cricket field.” In one example, Flower’s team ignored how cricket players train, which led to playing-field ruination when they toured Australia. “The selection of three beanpole quick bowlers to tour Australia was rooted in data that showed such bowlers were most likely to thrive in Australia,” as ESPN recounted. “The ECB looked at the characteristics of the best quick bowlers—delayed delivery, braced front leg and so on, and then tried to coach those virtues into their own players, seemingly not [realizing] it was too late; you can't change those things once bowlers are more than about 15.” Flower isn’t the only example of an overreliance on data; executives and entrepreneurs of all persuasions and industries have been known to bet far too much on a particular dataset or analytics-produced insight, only to fail to see another angle or twist before it was far too late. It’s an old lesson: data can take you far, but often a right judgment comes down to human skill, intuition, experience, or some combination thereof. Image: Brian A Jackson/Shutterstock.com 