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Untangling skill and luck in sports and business, redux
We’ve come across another good piece on decision-making and the limits of decision models.
In The Great Analytics Rankings, ESPN “unleashed (its) experts and an army of researchers” to look across the four major sports and assess each of the 122 professional teams on how much of their approach is predicated on analytics.
They ranked teams both within each sport and across the entire field of 122, and then took a look at the sport with the most developed methodologies – baseball – to ask, “do clubs that prioritize analytics win more?“
Their conclusion: there’s just a slight correlation between more analytics and more success. It remains tough to eliminate the usefulness of having more money than other clubs, and with technology and best practices so widely disseminated and articulated (in baseball, at least) the early Moneyball advantages may have been arbitraged away.
All this isn’t to say analytics are a nonfactor. Of course they’re important. For one thing, you have to keep up with the rest of the sport, even if the advantages to be gained are small. A lot of small advantages can add up. Look at the Astros’ signing of Collin McHugh, a nondescript pitcher waived by the pitching-poor Rockies. The Astros studied the PITCHF/x data on McHugh and saw a curveball with a good spin rate and took a chance on him. As Business Week reported:
The Astros’ analysts noticed that McHugh had a world-class curveball. Most curves spin at about 1,500 times per minute; McHugh’s spins 2,000 times. The more spin, the more the ball moves during the pitch — and the more likely batters are to miss it. Houston snapped him up. “We identified him as someone whose surface statistics might not indicate his true value,” says David Stearns, the team’s 29-year-old assistant general manager.
It gets a little more interesting with sports earlier in the process than baseball; e.g., the NHL, in the midst of its own “analytical awakening,” with concepts such as “Corsi” and “Fenwick” bringing together schools old and new. Only one NHL team cracked the Top 10 of ESPN’s rankings: the Chicago Blackhawks.
While Joel Quenneville is an old-school coach, the Blackhawks use analytics to find players who might be undervalued elsewhere but fit exactly what Quenneville and the Blackhawks try to do on the ice systematically. It’s been a great combination, with Bowman and Quenneville teaming up to win two Stanley Cups.
“I don’t claim to have the answers — we have a formula that works for us,” Bowman said. “We’re always trying to expand and add a new component each year that we do a little more with.”
When it comes to untangling skill and luck in sports and business, big data may help make accurate predictions or guide knotty optimization choices or help avoid common biases, but it doesn’t control events and can be undone by cluster luck. Models are useful in predicting things we cannot control, but for those in the midst of the game – players or entrepreneurs – the results have to be achieved, not just predicted.