Data is more than just power. It is supreme. At least, this is the message being put forward to executives today. Taking heed of this advice, organisations across all industry verticals are upgrading their data management systems, investing in new resources, and using their rich databases to streamline the practices of their departments. Everything from customer service and marketing, to manufacturing and product development is being infiltrated by this nascent phenomenon.
While the implementation of these technologies may be complex, the explanation for this boom is straightforward. Data is a tool to prepare for the future – it provides a basis for “informed” decisions, a platform to plan off, and a way to make reliable predictions. Indeed the message from experts is clear: organisations that fail to adapt and evolve to meet the emergence of big data, face the prospect of falling behind. As with any phenomenon, however, there are lessons to be learnt.
The way data is being used in sports is a poignant example. Moneyball, the popular book inspired by Oakland Atheletics manager Billy Beane, explained the core philosophy of the manager’s vision for the baseball team: using statistical analysis to maximise player acquisition and performance with a low budget.
How?
Through noticing trends. For example, while other teams focused their efforts on capturing the best talent from high schools with high batting averages, perfect physiques and excellent speed, Beane looked towards players that didn’t have these qualities. He knew the market overvalued such attributes. Rather, he focused on “on-base percentage (not batting average)” because through his data analysis he realised that this was a much better indicator of the true monetary value of a player. In essence, Beane’s tactical nous resided in his understanding of the market and how it functioned. With that data, he could navigate his way around the transfer market, avoiding high-priced talent and finding players that were undervalued but still good performers.
The Moneyball philosophy had huge ramifications for the sporting world. People started adopting variants of it in all sports – from soccer to basketball to football. Arguably the most noticeable application of Beane’s philosophy was by Andy Flower, the former England cricket coach. Flowers was known for his admiration of Beane’s work, and he too would use statistical analysis to not only determine who would be on the field but also what decisions players should make once they were selected. Just like Beane, Flowers enjoyed notable victories also.
Both Beane and Flower have stood by data analytics and the benefits it can bring. Yet, what is often untold is that data was both a virtue and a vice for both men. While the A’s reached four consecutive play-offs under Beane’s management, they also had a poor record after reaching this point. “My shit doesn’t work in the playoffs,” Beane remarked, “my job is to get us to the playoffs. What happens after that is luck.”
The same is true of Flower. His 5-0 defeat at the Ashes this year was one of England’s most disappointing performances to date. As commentators suggested, it was a classic case of overreliance on data, replacing intuition with numbers, and allowing data to dictate rather than inform. As Tim Wigmore commented on ESPN, “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.”
These instances of sports analytics are particularly relevant for organisations looking to add big data analytics to their existing operations. The example of Beane and Flower show how data does not have all the answers, and relying too heavily on it can have devastating effects. In the case of Flower in particular, data led to dogmatism, and consequently prevented him from seeing the bigger picture. Where data comes in handy is when we use it to measure our intuition for accuracy while also serving to “inform that same intuition so that our next “best guess” is more likely to succeed”.
(Image Credit: Freddie Jordan)
