Over the past decade, the role of analytics in professional sports has evolved into a mainstream affair. The recent film “ Moneyball ,” based on the bestselling 2003 book by Michael Lewis, provides a compelling insight into the role of statistical analysis in sports. It shows how the smart use of data helped the Oakland A’s, a struggling baseball team, top its regional baseball league during the 2002 season and win a record 20 consecutive games.
By carefully analyzing the strategies and players that produce wins on the field, the team was able to compete with rivals that boasted payrolls of at least triple the A’s. Its techniques quickly spread: The Boston Red Sox went on to win two World Series after adopting the same methods.
But “Moneyball” is merely the most high profile example of an explosion in data and analysis that has been underway in sports of all kinds. Formula 1 , for example, has long been renowned for its data intensity. Each racing car generates at least 1 gigabyte of data during every race, which is analyzed in great detail for insights into everything from comparative braking techniques to the performance impact of different fuel loads.
Chelsea, a major soccer club in the English Premier League, recently acknowledged to the Financial Times that it holds some 32 million data points from some 13,000 games. Players on England’s national cricket team receive a DVD full of match data after every match, allowing them to review anything from batting patterns to details on opponents’ strengths and weaknesses.
In sports, data collection and analysis is now simply part of the game, and often a bare minimum requirement in order to stay competitive. What has changed is that leading teams now compete not only for on-field talent, but also for the best analytical minds available.
Within business, the same holds true. According to research from IDC, there was a tenfold increase in data between 2005 and 2010 alone. This growth continues, along with an ever-widening range of data types, from social media and online video, to location-aware smartphones and other connected devices. To cope with this, a similar skills battle is underway in the business world, with organizations competing for the best data and analytics talent.
Beyond this, though, a more difficult transition is also underway, which relates to successfully using data to improve actual outcomes. Here again, sports is blazing the way. Consider that not everything that is measurable is necessarily meaningful.
In American football, for example, some statistics that have been trusted for years are now being challenged. In soccer , for example, previously valued stats such as “number of tackles” and “kilometers traveled per player” are now seen as far less useful than once believed. For instance, one of the greatest ever Italian defenders, Paolo Maldini, made very few tackles. The reason, as the FT described, is that Maldini used his positioning skills so effectively that he often did not need to tackle.
Similarly, there is also no correlation between total kilometers and winning. Instead, football’s quants have learned that distances run at top speed are far more important to victory than raw kilometers. This so-called “high-intensity output” is defined as a player’s ability to reach seven meters per second. It was not commonly measured back in 1999, but if it was, Juventus would likely never have sold Thierry Henry to Arsenal. The Frenchman, who went on to become Arsenal’s all time record goal scorer, reached seven meters per second just about every time he ran.
Such examples all highlight the ongoing evolution of analytics in sports, showing how the most successful teams and managers are those that filter through data to find and focus on the metrics that actually matter. For businesses trying to tackle the big data challenge, the same lesson will hold true. Companies are increasingly selecting all manner of data to help them analyze the behavior and purchasing habits of their customers or internal staff performance, but often the hidden metrics that really matter are missed.
One classic example is when firms remove a poorly selling, high-end product from their range, only to discover that this product’s mere presence was instrumental in driving far higher sales of the mid-range option. Another example might be internal: a manager might be pleased at lower levels of staff turnover, until it is determined that employee engagement has also fallen off a cliff, and employees are just clinging to their jobs through a tough job market.
As businesses grapple with new kinds of challenges, such as how they embed sustainability measures into their operations, this requires a sharp assessment of what actually matters —from energy used per square foot of retail, through to the volume of water required to create each product. Successful organisations will be the ones that can pick out the metrics that have a discernable effect on overall corporate performance. In business, as in sports, tracking the metrics that matter is fundamental to better performance.
_________________________ Jeanne G. Harris is an executive research fellow and director of research at Accenture’s Institute for High Performance Business. She is co-author with Tom Davenport and Robert Morison of “Analytics at Work: Smarter Decisions, Better Results,” and is co-author with Tom Davenport of “Competing on Analytics: The New Science of Winning.” She can be reached at firstname.lastname@example.org .
Jeanne G. Harris is an executive research fellow and director of research at Accenture’s Institute for High Performance Business. She is co-author with Tom Davenport and Robert Morison of “Analytics at Work: Smarter Decisions, Better Results,” and is co-author with Tom Davenport of “Competing on Analytics: The New Science of Winning.” She can be reached at email@example.com .