Big data analytics’ impact has been far-reaching across an array of industries, concepts, and communities worldwide. Impacted sectors range from the obvious (scientific research, healthcare) to the unexpected, benefiting mutually from enhanced data collecting, monitoring, and analyzing.
The athletics world may fall into the latter category, as most sports, at both pro and amateur levels, have started to recognize the capabilities of data analytics. These benefits may range from simple app-based logging systems to full-fledged video analyses of competitions.
Here are a few ways in which big data has impacted sports performance.
Most athletes utilize some type of mobile app or similar technology to enhance or monitor their performance. For example, there are a variety of apps that track and sort data for distance runners and cyclists focused on logging training runs and rides. Many of these apps have democratized the concept of data analytics, implementing easily accessible entry interfaces, deep storage capabilities, and even sharing opportunities rooted in social media (Strava, for example) to connect athletes with one another based on exchanged data. These apps have made data analytics much simpler for athletes concerned with keeping performance data for future reference.
Big data’s tracking of performance stats, however, goes far beyond that offered by simple logging technologies. Take IBM’s SlamTracker tool, which has been used during tennis tournaments to track “real-time scores, statistics, and point-by-point analysis of a match.”
Other prominent examples of this data monitoring can be found in basketball, American football, soccer, and baseball. Statistical information pertaining to a single NFL player, for instance, has become incredibly narrow in its trajectories, year-by-year comparisons, and most importantly its vast array of statistical categories. This information has become instrumental in everything from enhanced team scouting to additional projections in fantasy football leagues.
Big data has essentially transcended what it means to provide a “personalized” athletic experience. Performance data is being heightened to new levels thanks to the increasing intelligence of smart devices. Smart yoga mats, for example, are now able to provide data and general feedback on postures and overall performance, giving users added insight to proper biomechanics that could significantly impact their health and progress. This personalization is being broadened further thanks to an increased emphasis on other important athletic variables, including nutrition and sleep.
These notions hint at even more potential for data’s place in sports performance analytics, and they will likely define many of its future implications as they develop.