A winning team: What business can learn from sports teams use of analytics
Moving beyond Moneyball
Published 05:35, 08 June 12
The eyes of the world will be on London with this summer’s Olympics, all eagerly waiting to see which individuals and teams will take their place on the winners’ podium.
There will be no end of discussion around the technology and analytical skills being employed behind the scenes to help the games run smoothly. But how is this same capability being used by teams to support their gold medal ambitions and help them gain a competitive edge? And what can businesses learn from their approach?
Over the past decade, the role of analytics in professional sport has become so embedded within sporting culture, it has translated across to popular culture. The recent Moneyball film, based on the bestselling 2003 book by Michael Lewis, tells the engaging story of the use and impact of statistical analysis in sport. The Oakland As, a struggling American baseball team, used sophisticated analytical models to inform their training and management, leading them to top their regional baseball league during the 2002 season: winning a record 20 consecutive games.
The Oakland A’s story is partly so enthralling and has been so popular in retelling because of the “giant killer” aspirational aspect of their achievements. However, the reality behind the Hollywood gloss is the rather less glamorous but the rather more useful evidence that intelligent use of analytics has serious impact on a team’s ability to be competitive.
Through collecting information and data and intelligently analysing the strategies and players that lead to wins on the field, the team was able to gain a competitive edge in comparison with rivals boasting a payroll of players worth triple the amount of their own. This approach was quickly adopted by progressive teams including the Boston Red Sox, who went on to win two World Series utilising this technique. This same analytical modelling was adopted by St. Louis Cardinals, the current World Champions.
There has been an explosion of activity in this area across the whole sporting community. Some sports are well known for their data intensive approach - Formula 1 has long been known for its collection and smart use of data. Any F1 fan will be used to the images of the technical teams gazing at screens - their eyes trained on the information being relayed to them in order for them to adapt their strategy throughout the race. In fact, one gigabyte of data is produced by each F1 car during every race enabling the team to gain evidence based insights into everything from the performance impact of different fuel loads to comparative braking techniques.
Football may be more associated with chanting from the terraces and superstar players, than with data collection and analysis, yet Chelsea, recently acknowledged within a Financial Times (FT) feature on the role of analytics in football that it holds 32 million data points from 13,000 games. Cricket is also in on the data game with players on England’s national cricket team being sent a DVD devoted to match data following each match, enabling them to review their own and the team’s performance in great detail - from batting patterns, to specific information relating to the strengths and weaknesses of their opponents.
In the sporting world, the collection and analysis of data is now a fundamental part of the game, and is often the bare minimum to staying competitive. The extent to which analytics has become an essential and valuable commodity is demonstrated by leading teams competing not only for the most talented sportspeople, but also for the best analytical minds available. While there may not be open trials or a transfer window for data scientists, the competition for talent is equally fierce. The same is true of the business world.
A skills battle is taking place in the business community, with organisations competing not only against each other but now for the best data and analytics talent as well.
How are these experts changing a team’s focus and identifying where to invest time and energy? The world of sport is trailblazing here as well where it has been accepted for some time that not everything that is measurable is necessarily meaningful. In football, for example, statistics are now being challenged that have previously been trusted and relied upon for many years.
The real power behind analytical work is using data to improve actual outcomes. Football’s data scientists have learned that distances run at top speed have a far greater impact on victory than the basic number of kilometres covered throughout a match. This so-called high-intensity output is defined as a player’s ability to reach seven meters per second.
This knowledge applied retrospectively highlights how teams have made decisions around the value of players against a flawed evidence base. In 1999 this was not yet known and consequently not yet measured. It is likely that Juventus would never have sold Thierry Henry to Arsenal with the current levels of understanding. Henry went on to become Arsenal’s all time record goal scorer and reached seven metres per second just about every time he ran.
For businesses addressing the challenges of big data, the same lessons learnt by the sporting world will also prove to have measureable currency. Companies are increasingly selecting a wide range of structured and unstructured data to analyse the behaviour and purchasing habits of their customers or staff performance, but often the hidden metrics that have real impact can be missed.
Henry’s value would now be recognised and utilised to help build a strong and winning team based on the current understanding of output against impact. The key to success for businesses will be identifying the metrics that have a tangible impact on overall business performance.
 A football revolution, Simon Kuper, Financial Times, 17 June 2011
Posted by Jeanne G. Harris
Jeanne G. Harris is an executive research fellow and director of research at Accenture’s Institute for High Performance Business where she leads research in the areas of information, technology, and strategy. She is co-author with Tom Davenport and Robert Morison of Analytics at work: Smarter decisions, better results, 2010, and is co-author with Tom Davenport of Competing on Analytics: The New Science of Winning, 2007, both published by Harvard Business Review Press. She can be reached at email@example.com.