There was a time when the only tool you needed to effectively train for endurance events, like cycling and running, was a stopwatch with a second hand. Now workout files contain speed, cadence, elevation, heartrate, ground contact time, and power. You can use this data to answer, or at least give some insight into, questions like:
Big data and analytics have become such an integral part of sports that top teams like the British cycling team Ineos Grenadiers (Team Ineos) have a chief data scientist. The role of the chief data scientist is, in part, to provide insights to guide equipment selection and racing strategy. Do the aerodynamic advantages of deep carbon fiber rims outweigh their weight disadvantage? Knowing that a cyclist has only so many kJ of energy to expend in a ride, where are the most effective parts of the course to apply them?
I sat down with the inimitable Tim Cusick of WKO. Tim is the WKO product lead and coach for several world, national, and state champions in cycling. Tim comes to cycling with a background in big data, analytics, and machine learning. WKO is an “analytics engine” of sorts for cyclists and runners who are training and racing with power meters. Tim was kind enough to field a few questions about how those factors are affecting endurance sports.
Dave Krenik: Tim, would you inform our readers about what WKO is and how it leverages analytics to help athletes?
Tim Cusick: WKO is a robust analytical engine specifically designed for endurance athletes. It was devised to evolve the paradigm of performance data analysis (the process of breaking complex data into smaller parts in order to gain a better understanding of it) into true performance analytics (the discovery, interpretation, and communication of meaningful patterns in data). Analytics is multidisciplinary and uses mathematics, statistics, models, and descriptive techniques, but at the time, the existing programs out there lacked most of these elements, so we had to start from scratch. First, we worked with Dr. Andy Coggan to develop a highly accurate model of the human power-duration relationship. Once we had an accurate model, we needed to build a system that would allow for a multidisciplinary analysis with the ability to add math and statistics. WKO is the realization of these goals.
How does it leverage analytics to help athletes? Well, that answer has three parts. First, the use of advanced analytics allows historical performance data to be diagnostic. One of the hardest things for a coach or self-coached athlete to do is to truly understand any particular athlete’s needs. WKO’s analytical capability provides a 360-degree view of an athlete from both a performance and a physiological standpoint, which significantly improves the ability to diagnose that athlete’s needs, thereby creating more effective training strategies.
Second, WKO gives us the ability to track training efficacy by increasing the timeliness and amount of analytics of the dose-response relationship of training stimuli and adaptation. This enables faster insights and training adjustments to ensure results.
Finally, analytics solves problems. All athletes train with a plan until something goes wrong or doesn’t work. WKO’s analytics supply deep insights into athletes’ performing physiology, and these insights lead to better solutions during crisis or change in training.
Dave Krenik: Here at NetApp, we promote the notion of getting value from all of one’s data. How does WKO use all of one’s training and racing data to improve the athlete?
Tim Cusick: Modern performance data gathering and analytics give a significant advantage in endurance sport training. How? Proper data analysis and deployment allow us to improve the individualization and specificity of training. In WKO5, the use of a robust human performance model and comprehensive metrics utilizes the exercising athlete’s own data to help us better understand their physiology, which in turn allows us to better utilize that knowledge to improve training individualization and specificity. Furthermore, with this knowledge, we can use predictive analytics to better determine the quality of the dose-response impulse of such training, improving pace and quality of adjustments.
Dave Krenik: Where do you see analytics and/or machine learning taking endurance athletics? Catching cheaters? Automated training programs?
Tim Cusick: I think the future is really in the evolution of analytics. If we look at how big data has led the way, we see a three-step evolution in data analytics: descriptive to predictive to prescriptive. What does this mean? Descriptive analytics can be called reports, and their function is to gain insights from historical data with reporting, scorecards, and charts. Simply put, you review the data in well-compiled reports, find patterns on your own, and make decisions. Predictive analytics is the analysis of a variable (or set of variables) that can be measured for an individual to predict future behavior—or, more simply put, a solution.
I think WKO was at the forefront of this evolution, moving from data analysis in well-formatted reports to using power analytics to create solutions. Our future goals are to continue to push the envelope and develop the third step—prescriptive analytics. This involves using predictive analytics to recommend decisions using optimization and target results, thus evolving from descriptive to predictive to prescriptive. If we think about it from the standpoint of what an athlete gets from his or her data, the evolution is from reports to solutions to decisions, which is an exciting benefit for athletes.
To address the specific question about catching cheaters, I don’t believe that is on the horizon, because you need to be 100% sure of the quality of the data coming in, which is the core challenge. An athlete with a faulty data-gathering device, power meter, or heart rate monitor might be labeled a cheater when, in reality, it was the device that malfunctioned.
Dave Krenik: How might all this impact the coaching profession?
Tim Cusick: I have a simple answer here. Give a good coach an advanced tool, and you make a better coach. Give a poor coach an advanced tool, and you create confusion and misunderstanding. The point here is that as data evolves, so must coaches. These advanced tools can revolutionize the coaching business by improving your athletes’ performance, but only if you invest in the learning and use the data to remove bias.
Dave Krenik: Thanks Tim—much appreciated.
Whether you’re looking to modify a training plan or deciding to go through with an acquisition, you want the value of meaningful insight gleaned from all of your data to make an informed decision. In order to get all of your data, you need to know where it lives. Sound easy? Try it. Not only do you need to know the “where,” you also need to classify your data. What if instead of all your data, you need only a portion of it? How do you know which portion to get? Is it stored on the appropriate media? Not so simple, eh?
With NetApp® ONTAP® data management software, you’re prepared for both planned and unplanned downtime. Also, the elimination of unnecessary copies can save you up to 50% TCO. In your data center and/or in the cloud, take a look at NetApp big data solutions for yourself and see how NetApp and our partners can help you to easily make better-informed decisions.
Dave has been bringing solutions to market under various monikers (alliances, business development, solution marketing) for more than 15 years. Before entering the world of tech, he enjoyed a 15-year stint in the wine business.
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