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.
DATA TYPE | DESCRIPTION | UTILITY | STORAGE & ACCESS |
SPARTA METRICS
|
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. |
NORMALIZED DATA
|
T-scores |
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. |
RAW DATA
|
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. |
TIME SERIES DATA
|
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. |
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.