Perhaps the most visible evidence of the competitive advantage that can be fueled by data analysis is LeBron James’ performance in the NBA playoffs this year and last, compared to previous lackluster post-season play by the Miami Heat superstar.
But King James’ less-than-stellar performance on the basketball court happened before he took a hard look at the analytics behind his play, notes Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, in a post in Harvard Business Review.
“Nothing makes serious competitors more open to analytics than losing,” the HBR article notes. “A basketball genius frustrated with his professional failings decided he wasn’t as good or as smart as he needed to be. James took a good hard look at the analytics … then he hired retired NBA legend [Hakeem] Olajuwon … to help remedy the analytically undeniable flaws and shortcomings of his game. [James] explicitly linked analytics to his personal [and] professional transformation.”
But in the world of sports, analytics are playing an even more critical role than helping players improve weaknesses: increasingly predictive analytics are being used to predict future performance. So, no matter how well a player performs in this year’s playoffs, decisions for next year will be made by predicting future results.
That’s according to another HBR post by Schrage, who posits the same approach can be used by Procter & Gamble or other companies to predict which marketing executive will come up with the best idea next quarter or which sales team will make its sales goals next year.
“Future potential matters (much) more than past performance,” according to Schrage. “That’s the new quantitative consensus reshaping professional sports worldwide. After looking hard at the numbers and algorithms, the smartest — and richest — general managers and franchises have made up their collective minds: They’re not paying a premium for yesterday.”
This next-generation “Moneyball” is more commonly being used to predict – taking into factors like age – which players are about to peak and which have past their prime.
“If you’re running Procter & Gamble, Unilever, Google, Exxon Mobil, or Ford, you have comparable concerns about making sure you’re getting the best possible returns from your talent and human capital investments,” according to Schrage.
“You should be concerned about the aging curves of your marketing people; you should want to know if your tech support folks will deliver better outcomes tomorrow than today; you should be predicting which sales teams will procure the most lucrative contracts with the minimum risks,” he adds. “Think of it as Six Sigma predictive analytics for talent.”
Still, it’s difficult to measure the role that personal loyalty plays in inspiring extra effort that leads to better results, the post notes.
“The classic response, of course, is to insist that, ultimately, these decisions come down to human judgments rather than computational dictatorships,” the post concludes. “But that’s exactly why acknowledging the current trend is so important: The leaderships of the richest and (ostensibly) best-managed franchises in sports have effectively declared that the costs of preserving past values are too high relative to the potential for tomorrow’s performance.”
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