Analytics in the Athlete Ecosystem

The Seattle Sounders are collecting and analyzing more data than ever before. Img credit: Grantland

Athletes’ behaviors on and off the field impact their fitness, fatigue, well-being, and performance. The most advanced teams have begun monitoring athletes in all spheres of their ecosystem – from training to recovery to performance and back. Who is at the cutting edge of this field and can they manage with the current state of analytics hardware and software solutions?

Today, teams like the Seattle Sounders are forced to use multiple hardware systems and analysis softwares and hire additional staff to strategize, coordinate, and manage data acquisition and analysis (read more from Noah Davis at Grantland). The objective is to collect the right data at the right times and make sense of it all in the context of each and every athlete with their unique needs for training, health, and performance.

The Sounders and others at their level, within the NBA and NCAA, acquire multimodal data ranging from heart rate variability, sleep quality, and central nervous system activity at rest to continuous heart rate, speed, and acceleration during training and performances. Each of these parameters is collected on a different piece of hardware and analyzed in a different software making this a logistical challenge as much as it is a mathematical one.

Beyond the logistical and mathematical challenges, limits in sample size and data collection standards minimize the utility of analysis results. How can coaches or athletes themselves make decisions about their training, health, or performance without sufficient benchmarks and norms to compare with their current and past physical states and performances?

There’s an opportunity here for an analytics system that combines data across multiple hardware devices and data modalities with the intelligence to collect the right data at the right times and make sense of it in the context of each and every athlete. Such a system would have broad applicability, in sports and beyond. It would enable users to determine the human factors that predict their health and performance outcomes, especially the most elusive factors including the physiological and psychological makeup of players and the team dynamics between them.

We believe these more elusive factors are missing from most health and performance prediction equations. Today, our aim is to quantify these factors in combination with more traditional factors like game stats in a single software so coaches and athletes can make earlier, more comprehensive, and more definitive decisions involved in optimizing athlete training, health, and performance.

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