Athletic performance data measures athletic output for every kind of athlete in every kind of sport. From Olympic pole vaulters to senior citizens tracking the laps they swim at the local pool, any measurement of exercise output generates athletic performance data.
This data can also measure non-human animal performance.
The main sources of this data are research studies and results from matches or competitions. Other data sources come from public datasets and population surveys.
Most commonly, however, this data comes from coaches and trainers working with their clients who want to improve their physical fitness or sport performance. These coaches use medical research, exercise science, and the data generated by their clients to create personalized training plans.
Family doctors as well as specialists often help craft personal training plans; many clinics welcome and can even incorporate wearable device, nutrition, sleep, and mood data that patients bring in and their recommendations for patients’ exercise programs.
Athletic performance data varies by sport and person. Whatever metrics are used for the sport are measured in athletic performance data. The information recorded about the individual athlete, meanwhile, can be static and dynamic. For example, most datasets specify sex, age, physical condition, and history of injury. Others, meanwhile, track stress levels, nutrition, average hours of sleep, and so on. Since users of this data primarily want to improve their own physical fitness and cannot devote most hours of the day to it like professional athletes, these datasets record a large number of personal attributes.
As noted above, most users of this data want to improve their overall physical fitness. Individuals may also want to overcome an injury or illness or improve their athletic ability in a particular sport.
However, professionals also track a wealth of athletic performance data—and they are increasingly hiring data scientists to analyze it. With the advance of technology generating massive amounts of granular data in real time, athletes and teams with millions of fans and millions of dollars in revenue on the line need accurate athletic performance analysis.
Athletes, coaches, trainers, and data scientists must take care that their athletic performance data uses a wide variety of sources with the greatest accuracy and comprehensiveness possible.
Datasets should also update as soon as athletes generate new data.
Finally, not all athletes—or people that want to improve their health—will find detailed datasets easy to understand. For this reason, many wearable tech devices market their products by highlighting easy-to-understand data visualizations set in a clean format.
Supersapiens, utilizing the Abbott Libre Sense Glucose Sport Biosensor, is the first over-the-counter energy management ecosystem that can give any athlete real-time visibility into their glucose levels, allowing them to fine-tune fueling strategies before, during, and after training and racing. This advancement in sports science stands to eliminate a major variable from race preparation and execution, helping athletes feel more confident on the start line, and ensuring that they have continuous guidance on how to fuel during an event.
AthleteMonitoring Data primarily consists of internal data from user biometrics and questionnaire feedback. Users can also import data from other sources, such as EMR documents.
AthleteMonitoring also stays up-to-date on disease, injury prevention and management, and sport data and best practices.
iPatientAxis Health Data offers technical and scientific solutions to make medical information available to patients
AnalyticsIQ HealthIQ is part of a consumer behavior data suite. It collects healthy behavior and biometric data without sacrificing privacy