Athletic Performance Data

What Is Athletic Performance Data?

Athletic performance data measures an athlete’s performance as well as their overall health. This includes their history of injuries, current mood, stress levels, and more.

Why Is It Important to Have Athletic Performance Data?

Athletic monitoring technology provides data that has proven effective in improving athletic performance and reducing injuries: see a sample of sources linked below for more detail. Further, due to the fact that much of the data is automatically generated by wearable technology, athletes can get immediate athletic feedback, even when far from their coaches.

The tech also monitors mood and stress levels to keep athletes engaged in the sport. For example, an athlete facing both work and family stress cannot maintain a high-intensity workout schedule; rather than quit, the data can recommend a less intensive program.

What Internal Data Should I Have for a Good Athletic Performance Model?

Internal data for a good athletic monitoring system is about the individual athlete. The data includes age, sex, past injuries, diet, sleep quality, and more. Wearable technology also collects information on granular, real-time athletic performance: speed, squat weight, ground contact, stride length, etc. Essentially, this refers to anything related to proper performance of the sport in question.

What External Data Is Essential for a Good Model?

The line between essential and useful is very blurred. For elite-level athletes, almost anything is essential. For non-professional athletes, however, most of this data is merely useful, if not unnecessary.

What is essential for all, however, will be peer-reviewed scientific data on subjects like nutrition, kinesiology, and injury rehabilitation.

What External Data May Prove Useful for a Good Model?

Data that non-professional athletes may find useful and that many professional athletes will find vital include coach notes, medical records, weight, drug test results, mental health screens, GPS data, and competitor data.

What Are the Main Challenges of this Use Case?

There are many challenges to this case. Most challenges result from the sheer amount of data and technology available. Coaches that manage sports teams must synthesize data about all their individual players. Then, they must use past data about rival team players to create potential game plays. Using the data effectively requires careful consideration.

For non-professional as well as professional athletes, there is also the tendency to focus on the collection of data more than really necessary. For example, an individual can purchase a ground contact sensor embedded in his shoe to tell him he slams his forefoot on the ground with too much force. Or can he use his ears. But perhaps a professional track-and-field star needs that sensor to get alerts to her mistake in real time.

Finally, there is the issue of privacy. This becomes especially relevant when athletic monitoring data programs connect to medical records or issue mental health screenings themselves. The athlete and coach must come to an agreement on the gathering and sharing of the data and the program developer must ensure the data is absolutely safe from breaches.

Interesting Case Studies and Blogs to Look Into

Intel: Running Faster, Jumping Further: How Big Data Analytics Is Cheering Athletes On
EdTech Magazine: Data Analytics Helps College Coaches and Athletes Optimize Training and Performance

Tangible Examples of Impact

ECG technology can read the signals emitted by our brain and transform them into digital code to measure cognitive performance. This data has shown how athletes’ brains are different. Sport is generally based on perception loops, he explained, where cognition plays a very important role in athletes’ ability to predict situations better. In fact, brain analysis can even indicate which athletes are going to perform best.

Swart interrupted here to comment that it is not advisable for an athlete to learn new things during the season, as it can affect their performance. Instead, neuroplasticity should be taught during breaks to make sure the athlete is in peak condition at the start of the season. For example, they could try to learn a new language or to play an instrument, which will make it easier for them to acquire new skills when they return to training.

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