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Big Data Analytics Drives New Athletic Advantage
Technology Category
- Analytics & Modeling - Big Data Analytics
Applicable Functions
- Product Research & Development
Services
- Data Science Services
The Challenge
The 2012 U.S. Women’s Olympic cycling team was looking for a competitive edge after a disappointing finish in the World Championships. They turned to Olympic cyclist Sky Christopherson, who had used the quantified-self movement in his training to break a world record. Christopherson established an experimental project to help the team record and analyze relevant data that could reveal actionable insights for optimizing their athletic performance. The team faced the challenge of recording relevant data, integrating it, analyzing it, and visualizing all of these data points to reveal insights they could incorporate into training. The sheer amount of data, and with each device producing different types of data (often in unstructured formats) meant that traditional database and business intelligence technologies were not an option.
About The Customer
The customer in this case study is the 2012 U.S. Women’s Olympic cycling team. After a disappointing finish in the 2012 World Championships, the team was looking for a competitive edge. They turned to Olympic cyclist Sky Christopherson, who had used the quantified-self movement in his training to break a world record. Christopherson established an experimental project to help the team record and analyze relevant data that could reveal actionable insights for optimizing their athletic performance. The team was faced with the challenge of recording relevant data, integrating it, analyzing it, and visualizing all of these data points to reveal insights they could incorporate into training.
The Solution
The team turned to Datameer’s big data analytics application, which sits on top of Hadoop, an unlimited storage and computing platform that can take in any amount of data and any data format. This allowed the team to easily ingest, join, analyze and visualize how all of the sensor and device data was interconnected. The team was able to learn how daily routines and behaviors could be adjusted to naturally maximize human performance. For example, patterns revealed how room temperature affected the number of minutes spent in deep sleep, a state where bodies naturally release testosterone and human growth hormone. One insight his team uncovered was related to the athletes’ circadian rhythms.
Operational Impact
Quantitative Benefit
