My friend Perry and I sat in the wooden lobby of an apartment building in Brooklyn today, when a debate familiar to any sports fan came up. “Hey,” I said. “Did you see the archery final at the Olympics today?” “Archery is at the Olympics? That’s not even a sport.” The sport/not a sport debate always seems to come around in full force during the Olympics, when events like shooting and ping pong take center stage for a brief moment every four years. But in watching the myriad competitions that make up these Olympic games, it’s important to remember that while they may not all be sports, thanks to the emergence of big data they are all a science. Simon Williams proved as much in his presentation at the Strata Online Conference last week in London, the site of the 2012 Olympics. As the Chief Executive and Co-Founder of data firm QuantumBlack, Williams has worked to design race strategy engines for some of the biggest names in Formula 1 racing. “Everybody knows about the race on the F1 track, but there’s another race going on – the innovation arms race,” Williams said in the presentation. “The fantastic plan that you’ve arranged starts falling apart at the first corner. After that first corner, it’s all about determining which course of action is appropriate to undertake.” According to Williams, having real-time, comprehensive data can help make those decisions better and, most importantly, faster. Obvious data points like speed, gas usage, wear on the tires can help a team tend to its drivers and vehicles, but where Williams saw a real breakthrough was in inferred data – taking two seemingly disparate sets of information and combining them into a useful whole. In the case of F1 racing, that means knowing even more about what your opponents are doing. “In addition to GPS and other data, teams use the TV broadcast feed to determine the engine setting of opponents,” Williams said. “This is valuable intelligence, [with which] teams can predict shifts in their competitor’s strategies and take steps to counteract them.” F1 racing is not an Olympic sport, but similarly innovative uses of data can apply to activities that are. Wired’s Mark McClusky says as much in his piece on the increasing amount of science and data analysis present in training this generation’s Olympians. “The challenges of the 21st century are very different,” McClusky writes. “At this point, the easy improvements have all been made. Margins of victory are going to be smaller, and the tools that help athletes win will increasingly be found not in the weight room but in the lab. Many sports will begin to resemble auto racing, where wins are determined by a combination of driving skill and technology.” This technology has already begun to impact sports like cycling. The Perfect Athlete video shows how intensely the laboratory has become a part of the sport.
There are other places where data is playing a big role as well. American hurdler Lolo Jones, for example, trains along with a team of 22 scientists and data analysts. Their techniques include using slow-motion video cameras, impact sensors in her shoes, and nutritionists to make sure her body is functioning at its absolute peak. It’s their job to break down every twitch of every muscle in Jones’ form, streamlining her motion to bring as fast a time as possible. The result of all these new, data-driven training methods hasn’t yet been shown on the biggest stage (Jones doesn’t hurdle until next week). However, her times have steadily improved, and she has become one of the most focused-upon athletes in the Unites States’ Olympic party. There are real, everyday takeaways from both these scenarios that have nothing specifically to do with athletics. In fact, they may help you in your own quest to use data more effectively. Lolo Jones and her team prove big data’s ability to comprehensively break down every single segment of a given action. It doesn’t have to be a run – you can apply the same principles to, say, your shipping methods. Analyze every category you can and make as many small improvements as possible, and the end product will inevitably improve. Williams’ Formula 1 systems look through an entirely different lens. Instead of focusing inward, Williams’ strategies instead show the benefits of analyzing your competition. By knowing more about what your competitors are doing, you in turn can make informed decisions about how to take your data venture forward. And whether your venture is a sport or not, that’s bound to be beneficial.