How Businesses Can Apply the Analytic Lessons Learned in Sports
Professional sports teams have successfully used analytics since the Moneyball period of the early 2000s, when the Oakland Athletics put a superior product on the field at the Major League equivalent of rock-bottom prices. Today, as analytics become more common in business, attentive companies will take a few analytic lessons from their colleagues in the field of sports.
A’s general manager Billy Beane got the first-mover advantage in 2002 when he used a branch of statistics called sabermetrics to stock his team with relatively cheap but solid ballplayers that were languishing on other pro teams or in the minor leagues, or had yet to be drafted out of school.
Beane and his number-crunchers found that certain statistics, such as on base percentage, are better indicators of offensive success than, say, home runs. Beane used these insights to generate a big advantage for the A’s, who won 103 games–and had a record-setting and thrilling 20 game winning streak in August 2002–despite having the third-lowest payroll in the Major Leagues.
The Moneyball approach has since been adopted by many other teams in baseball, in addition to other sports, including football, soccer, and rugby. But it also has application outside of sports, argues Will Gatehouse, the big data lead for Accenture in the EALA (Europe, Africa, the Middle East and Latin America) region.
“Those companies that can learn from the cutting edge analytics in sports to reduce time to market with shorter product cycles, react early to changes in market conditions, or respond to telemetric or sensor feedback to improve operational efficiency will, like all successful teams, rise to the top of their game,” Gatehouse writes in a recent blog post.
Establishing the exact factors that can generate an edge, of course, is easier said than done. According to Gatehouse, some NFL teams are using uniforms equipped with biometric sensors to gather movement about their players’ movements, with the hope of improving performance. This approach may not fly in the workplace, but the trend toward adopting sensors to gather data is an inevitable one.
Gatehouse argues that, just as the A’s used analytics to identify top talent, businesses can use the technology in the human resources department. He says that companies ought to “look at the role of analytics to identify attributes that indicate success, and then use techniques seen in sports to search the reams of external data available to identify potential candidates,” he writes. “Once employed, the companies that are leading on analytics are increasingly using analytics to ensure high productivity, engagement and retention.”
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