What types of data does Sparta provide?

Sparta Science provides a fully comprehensive data model and machine learning ecosystem. Four levels of data are available to Sparta customers: Sparta Metrics, Normalized Data, Raw Data, and Time-Series Data.

Sparta Science provides a fully comprehensive data model and machine learning ecosystem that facilitates multi-level, multi-purpose capabilities and engagement. This enables full transparency, trust and overall, a unique level of data democratization.

To start, movement data is taken from its source (forces), and standardized with built-in error-detection and reliability software automations. Sparta is the only solution that provides this objective filtering as standard, which means the data and insights can be trusted and scaled.


From there, Sparta can provide the trusted data required, as well as the ability to leverage machine learning and artificial intelligence capabilities. The four types of data and their utility and accessibility can be seen in the table below.






Proprietary Metrics

Actionable machine learning derived metrics for performance and injury risk.

Available for real-time viewing and download within Sparta Cloud. Calculations are proprietary and not shared externally.





Raw data is contextualized by comparing against a global database and specific cohorts. This enables ranking, comparisons, and threshold development for awareness and decision-making.

Available for real-time viewing and download within Sparta Cloud.




Calculated Metrics

Calculated from time series data to help explain the size and strength of force plate data signals.

Available for real-time viewing and download within Sparta Cloud.




Every data point along the force-time curve

Can be used to calculate any metric from an assessment performed on the force plate and by researchers and data scientists for independent study.

Stored on the system’s back-end— accessible post data collection in the desired format via the Sparta Team.

First and foremost, Sparta is driven by providing the most reliable, valid and actionable metrics that facilitate real-time decision making in operational settings. This is where the Sparta Metrics and Normalized Data are best placed to provide instant value and engagement because they can easily be communicated and improved through targeted interventions. An example could be that in 60-seconds, an individual can understand their movement risks and performance in comparison to their peers via their Injury Risk and Sparta Scores, while at the same time this information can be shared with multiple clinical and management stakeholders for aligned awareness and decision making. 

While real-time actionability should always be the end-goal, the option to interact with data for a range of research, education and customized problem-solving needs can also be an important capability. This is where the Time-Series and Raw Data are valuable. What should however be noted is that these types of data generally require researchers and applied scientists to interpret, analyze and communicate meaning, and while important for learning and innovation, this data is less actionable and engaging to all stakeholders. An example could be exporting a large, longitudinal dataset and sharing this with a research and development partner to analyze and explore a range of bespoke questions.
Sparta is constantly reviewing millions of data points at all levels of its data model both internally to refine methods, and strategically alongside its industry and research and development partners. Leveraging its aggregated global database and a constantly evolving feedback loop of machine learning techniques, Sparta continues to search and explore new data science approaches and metrics to improve movement health and performance outcomes.
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