How Big Data Fails To Make Big Plays In Sports

Ever since Moneyball, the sports intelligentsia have rushed to find ways to implement Big Data to maximize the performance of their teams. From soccer to baseball, data is heralded as the key to winning games and, ultimately, trophies.

Except that it isn’t. Not completely, anyway.

The more data we collect, the harder it is to filter signal from noise, according to renowned statistician Nate Silver. One sport where this truth recently became evident is cricket.

Seeking Answers In Bats And Bowl-outs

Cricket, unfamiliar to most Americans, is popular throughout the former British Empire. England recently suffered a humiliating defeat to Australia, losing 5-0. While The Daily Telegraph lists five reasons England lost, there really is only one reason: England cricket coach Andy Flowers relied too much on Big Data.

After the release of Moneyball in 2011, Flowers turned slavishly to data to determine who to play and how to play. As told by ESPN’s Tim Wigmore:

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….[W]itness Alastair Cook’s insistence on having a cover sweeper regardless of the match situation. Going back to 2011, consider England’s approach to tying down Sachin Tendulkar in the home series against India: they relied obsessively on drawing Tendulkar outside his off stump in the early part of his innings rather than let him get his runs on the on side, an adherence to the result of a computer simulator plan created by their team analyst, Nathan “Numbers” Leamon.

Such devotion to numbers affected not only decisions on the field, but also who should take the field in the first place:

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. 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 realising it was too late; you can’t change those things once bowlers are more than about 15. It did not matter how many boxes Steven Finn, Boyd Rankin and Chris Tremlett ticked in theory when they were utterly bereft of fitness and form in practice.

Over-analysis of data can lead coaches to strategize based on past performance rather than the position and player needs of the current game. For example, a new study suggests the so-called “hot hand” fallacy is actually true: A player with a “hot shooting hand seems to enter an ethereal zone, an inexplicably heightened state of ability in which he is unstoppable.”

The Sport Of Business

Big data’s impact on sports—in this instance, cricket—isn’t isolated to strategy. Wigmore’s ESPN piece argues that “Flower ultimately got the balance between trusting people and numbers wrong. 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.”

This truism exists in business as it does in sport, and ultimately, people must make decisions similarly across both verticals. Data complements decisions, but shouldn’t rule them, because data is never truly objective. Choosing which data to collect is a human judgment—so, too, are the questions we ask of it.

Still, data need not always be subservient to human intuition. At my company, for example, we recently found through extensive A/B testing that our best guesses as to which email subject lines would be most effective were way off. We therefore calibrated our email campaign to match the data, not our intuition.

This is where data comes in handy: measuring one’s intuition for accuracy. But it also serves to inform that same intuition, so that our next “best guess” is more likely to succeed.

In the case of England’s cricket team, rather than respond to data, coach Flowers was bowled over by it, sticking to data even when it clearly wasn’t paying off in wins. In sport or business, that’s what we call “a losing strategy.”

Image courtesy of Wikimedia Commons.