Update: June 2019
We are currently developing an open source version of the stride variability toolkit (written in R).
It is hoped that this will allow practitioners and researchers to replicate published research with their own data and investigate the usefulness of the tool in their own settings
The BioAlchemy Stride variability toolkit represents a new way to analyse athlete tracking data collected from any game or training session
This toolkit gives applied sport scientists the ability to analyse the quality of an athlete's movement by examining accelerometer data step by step.
It provides a unique method for sport scientists to analyse the physical effects of a match or training session by examining the variability of an athlete's within-step accelerations
The toolkit also includes the ability to compare the average step acceleration patterns from different sessions, which allows investigations of the influence of an intervention or injury on an athlete's stride acceleration pattern.
Here is a quick youtube video demonstrating this feature:
email for more information
what is included?
The toolkit is made up of a program to take GPS and accelerometer data exported from an athlete tracking system, an excel spreadsheet with embedded macros for analysis and reporting, and a program to review step waveforms from particular sessions.
Much of the process is automated, and is geared towards sport scientists in the applied environment who have limited time to prepare and analyse data.
What does it measure?
The toolkit takes the accelerometer curve from selected steps during a training session or game, then compares those curves to determine how variable they are.
It does this by identifying periods of straight line high speed running (or periods of high stride rate) from the training session or game, and extracts the acceleration curves from individual steps within those sections. Those curves are then compared against each other to determine how variable the step acceleration pattern is.
In addition, the average acceleration curve from a session is recorded for future analysis, allowing comparison of the step acceleration patterns before and after an intervention or injury.
why look at variability?
Looking at the variability of step accelerations provides valuable information on athlete condition.
Variations from an individual's 'healthy' amount of variation in step accelerations could be an early indicator of a change in the physical state in the athlete which may eventually lead to a reduction in performance or injury
What sports can it be used for?
The toolkit was developed using Australian Football data, but has since been tested using football (soccer), rugby, basketball and American football
In addition, the sport specific elements of the toolkit (such as identifying matched sections of running from games and training sessions) can be tailored to your situation.