Follow Datanami:
March 3, 2014

When Data Analytics Goes Horribly Wrong: A Sporting Example

Alex Woodie

Ever since the Oakland A’s started playing Moneyball in the early 2000s, professional sports teams have been clamoring to implement data analytics to obtain a competitive advantage. While predictive analytics and other forms of statistical analysis are more or less par for the course in many sports leagues these days, an embarrassing loss by the English national cricket team stands as a stark reminder that data analytics is no substitute for experience and wisdom.

Cricket is a national pastime England, where it was invented more than 500 years ago. The UK’s national team has been widely regarded as one of the best cricket teams in the world. But when the national cricket team lost 5-0 to the national Australian team in January, fans demanded an accounting.

In an illuminating piece on ESPN Cricinfo, titled “The perils of data-driven cricket,” author Tim Wigmore lays the blame for England’s lost direction squarely on the team’s former head coach Andy Flower’s decision to replicate Moneyball-style data analytics–to the detriment of everything else.

“Flower’s reign, for the most part, showed the virtues of using it smartly. But cricket data is affected by the unpredictability of human beings and so constantly fluctuates,” Wigmore writes. “By the last embers of Flower’s rule, England seemed not empowered by data but inhibited by it, as instinct, spontaneity and joy seeped from their cricket.”

The downfall was most evident in England’s rigid adherence to computer simulation models created by the team’s head analyst, Nathan “Numbers” Leamon. Those models advised Flowers to make certain moves with certain players. Instead of trusting old-school cricket wisdom gained by experience on the field, Flowers trusted the computer, and he paid with his job–and England’s national pride.

Data is a complement to intuition and judgment, not a replacement for them, says Wigmore. The authors of the book Big Data, Kenneth Cukier and Viktor Mayer-Schonberger’s, may have had it right when they said: “Big data exacerbates a very old problem: relying on the numbers when they are far more fallible than we think,” he says.

“For all his triumphs as England coach, and there were many, Flower ultimately got the balance between trusting people and numbers wrong,” Wigmore writes. “He was in good company. In the brave new world, those who thrive will not be those who use data most–but those who use it most smartly.”

Related Items:

How Businesses Can Apply the Analytic Lessons Learned in Sports

Moneyball Meets Marketing as Ad Research Game Changes 

Giving Big Data a Sporting Chance