In its most rudimentary form, athletes have been using data to measure performance since the invention of the stop watch. But for modern athletes, state-of-the art data capture and analytics systems are now being employed to improve training and performance in several key ways.
Data capture and analysis systems are transforming sport across the world by modelling behaviour and logging performance. Accelerometers, magnetometers and gyroscopes can gather vast quantities of information and, combined with GPS systems and video analysis, some systems can measure up to 1,000 data points per second.
Wearable sensors, for example, can record data such as speed, power, temperature, ground contact, cadence and vertical oscillation. Information on previous competitors and athletes can also be mined by way of comparison. This creates a reservoir of data on one individual athlete and how they are performing. The question then becomes, what do you do with it?
All this raw data can be fed into analytics engines to produce bespoke reports. It might start with identifying raw talent early on, continue into forming a training regime tailored to an individual athlete, and inform post competition performance analysis. At every step, data is now invaluable at the highest levels of professional athletics.
Professional sports teams and trainers bring in tech innovators to give them this data edge, with the latest discrete wearable technology able to capture every conceivable metric.
Elite HRV , launched in 2014, is a heart rate variability application (HRV) that measures the change in time between successive heartbeats. This can give insights into the nervous system, levels of stress and recovery activity. Using a chest or finger sensor, Elite HRV provides metrics and analysis for both amateurs and professionals, be they coaches, health practitioners, or researchers.
“Data capture in athletics has been around for a long time, from early blood testing results to modern biohacking methods,” says Vivek Menon, Chief Commercial Officer, Elite HRV.
“Many of these methods came from former eastern bloc nations looking to compete with smaller overall resources. These days, modern sensor technology and the sophisticated processing available on smartphones has democratised data capture from expensive labs into the hands of any curious athlete. It's now more about the coaching than the access to the data.
“Heart rate variability is commonly used amongst elite athletes to optimise training and recovery. It works by helping them to train at their maximum capacity without overdoing it. We have Olympic rowers that use HRV to optimise their workouts. Based on their morning HRV reading, they'll tailor the intensity of their training session. We also have other high-level athletes using HRV to optimise their sleep habits. Other elite teams use it to manage jetlag stress during in-season travel.”
At every step, data is now invaluable at the highest levels of professional athletics
A company called Stryd , meanwhile, has produced a system which attaches a data capture device to running shoes. This links with smart devices such as the Apple Watch without the need for a phone. Again, a key driver in this approach is the miniaturisation of data capture tech and the ability of high powered analytics software to produce digestible results. The firm works with a range of athletic professionals, from national Olympic teams to one-man coaching operations.
“Data is taking off because of a generational shift,” explains Angus Nelson at Stryd. “The younger generation grew up with smartphones and learned the importance of data. Now, this generation is taking their data knowledge to their athletic lives.”
“Imagine you are deep in a marathon. Your coach is at the finish line, but you need them now. You want to finish this race without blowing up and walking to the finish. Should you speed up? Should you slow down? Stryd tells you that you are safe to speed up. You run faster and cross the finish line in record time. We are a lot like a coach on your foot.
“VO2 max performance is one of the biggest drivers of human performance and hugely important for Olympic athletes. Typically, VO2 max requires a very expensive machine and can only be measured in a lab. Stryd's insight into VO2 max allows Olympic athletes to gauge this important metric anytime and anywhere.
“Stryd helped Olympic triathlete Ben Kanute in his training and racing during the Rio Summer Olympics. Now, Stryd is helping skiers up their performance for the PyeongChang Winter Games.”
As well as maximising the chances of taking home a gold, data analytics can also be used to pre-emptively deal with potential injuries by measuring, for example, asymmetries in movement – reducing the chances of critical injuries occurring before athletes have hit the track.
All this can give coaches a deeper, data-driven understanding of the current physical state of an athlete, and detect asymmetries as an early warning signal for injuries.
Using real-time analysis like this during training can reduce the risks of soft tissue injury over time, improving the long-term careers of athletes as well as maximining their potential for achieving success on the field in the near term.
“Injury prevention is hard but not impossible. They occur when you don't want them to.” adds Angus Nelson. “However, we learned that injuries give off very small warning signs in the foot swing motion before they happen. Stryd detects these warning signs with motion sensing technology. We tell you when there is elevated risk. Next, we determine what preventive action to take with targeted drills designed to strengthen the problem area.”
With access to historical data, analytics is increasingly used for predictive purposes.
“Heart rate variability has been well-researched as it relates to injury prevention,” says Vivek Menon. “To over-simplify, it indicates that the body is either a) under-recovered or maxing out recovery capacity. Without this information, chronic under-recovery could result in inadequate healing between stress loads (training, competition, certain lifestyle factors). Over time, this inadequate healing builds up to a point that a stress load results in injury.”
Data analysis in athletics was used during the 2016 Olympics and the trend seems certain to continue. In the future, advancements in computing power, sensor development and machine learning will likely yield greater and greater data sets to analyse. While as analytical programmes become more sophisticated, improved algorithms will be able to convert data into ever more sophisticated insights to help athletes maximise their performance.