Automated Activity & Data Aggregation - VECTOR 8

Overview

OpenField will automatically find - or create - the most appropriate activity to associate with data uploaded to the Cloud from Vector 8 devices, either via the Vector 8 Dock or after an OpenField Console sync.

Unlike Vector 7 workflows, where device data was always downloaded to OpenField Console and aggregated into an activity before being uploaded to the Cloud, Vector 8 workflows upload device data (in the form of raw files) directly to the Cloud via the Vector 8 Dock, independently of any activity created or live data collected in OpenField Console.

As such, the sequence of activity aggregation will depend on which happens first: whether OpenField Console syncs a new activity to the Cloud, or whether raw file(s) from the devices are uploaded via the Vector 8 Dock.
 

When a Device’s Raw File is Uploaded to the Cloud via the Vector 8 Dock

How to change Automated Activity Aggregation method

The method of automated activity aggregation used for your account can be selected in the 'My Account' settings page in OpenField Cloud.

By default, the by time method will be enabled.

To change the method of automated activity aggregation used by your account:

1. Log in to OpenField Cloud
    
2. Select 'Settings' in the top menu bar, then select the 'My Account' page
    
3. Select the desired method of activity aggregation, either by time or by team
   
   
4. Select 'Update' in the bottom right of the screen to save your selection
   
   

How each Automated Activity Aggregation Method works

There are currently two methods of Automated Activity Aggregation, by time (default), and by team

• By time - Any raw file uploaded to the cloud for an account (across all Vector 8 docks registered with the account), that contain data over a similar time frame will aggregate athletes into the same 'Auto Created Activity'
   
  • In the example below, 53 Vector 8 devices recorded data within a similar time frame, and so all athletes associated were added to the same 'Auto Created Activity'
    
    
     
• By team - Raw files uploaded to the cloud for an account (across all Vector 8 docks registered with the account), that contain data over a similar time frame will aggregate athletes into team specific 'Auto Created Activities' 
   
  • In the example below, Vector 8 devices have recorded data over a similar time period for multiple Athletes that are associated with an 'U16' Team, an 'U18' Team, and an 'U20' Team, and so 3 separate Auto Created Activities are created, one for each team, with only athletes added to that team added to the relevant activity, such as only 3 athletes in the 'U18' Activity:
    
    
    
    
     

Note that if desired, athletes can be manually moved to a new or existing (overlapping) activity - see Move Athletes to New or Existing Activities
 

Detailed Automated Activity aggregation method logic
 

1. OpenField will look for any existing activity in the Cloud for the account that:
   • Is within 1 hour of the raw file start or end time AND
   • Has the athlete mapped to the raw file’s device id added to at least one period
      
2. If OpenField finds a matching activity in step 1, the activity will be reprocessed to aggregate data from the uploaded raw file and combine live and post-session data as needed.
    
3. If OpenField does not find a matching activity in step 1, the data will be aggregated into an appropriate* automatically created activity (an ‘Auto Created Activity’) that covers the time span of the raw file.
    
   • The athlete that is mapped to the device id of the raw file will be added to a period named ‘Auto Created Period’.
      
   • *The Automated Activity Aggregation method enabled on the account will determine whether which 'Auto Created Activity' athlete data will associated with
      

When OpenField Console or Vector Live iPad app Syncs up a New Activity

1. OpenField will check for any existing 'Auto Created Activity' that overlaps in time with the activity uploaded by Console.
    
2. If an overlapping ‘Auto Created Activity’ is found, any athletes that are added to the period in the ‘Auto Created Activity’ that also are added to at least one period in the ‘Console-Created’ activity, will be removed from the ‘Auto Created Activity’.
    
   • This video recording demonstrates this functionality with an example where:
     • 26 athletes have been automatically aggregated into an 'Auto Created Activity' as a result of raw files uploading to Cloud via the Vector 8 Dock.
     • OpenField Console syncs up a 'Console-Created' activity, which was created live that contains 8 athletes in at least one period.
     • After the sync has completed, the 8 athletes that are in the  'Console-Created' activity are removed from the concurrent 'Auto Created Activity'.
   • Both the ‘Console-Created’ activity and ‘Auto Created Activity’ will be reprocessed to aggregate data based on changes to the activity(s), and combine live and post-session data as needed.
   • Athletes are removed from the concurrent 'Auto Created Activity' because it is assumed that the 'Console-Created' activity includes more context and manual edits to enrich reporting. This also prevents duplication of processed data in longitudinal reports, where athletes might otherwise appear with data in overlapping activities.
      
3. If no overlapping ‘Auto Created Activity’ is found, then no changes are made to existing activities.
   
   

Live and Post Data Aggregation

When OpenField Console syncs, it uploads any data collected live along with any new activities created on Console.

When raw files are uploaded to the Cloud via the Vector 8 dock, the live and post-session device data is ‘combined’ or ‘aggregated’ in the Cloud.

This occurs regardless of whether raw files are uploaded before or after a Console sync.

The main purpose of this live and post data aggregation is to:

• Persist LPS positional data, as this is only stored locally in OpenField Console and is not recorded on the device raw file.
   
  • Therefore both positional-metrics can be reported on in combination with post-processed metrics, such as IMA’s or sport specific events.
     
• Ensure the most accurate data set is used for reporting, for example:
   
  • Discarding any lossy live GPS data and using GPS data recorded on the device raw file
  • Providing post-processed metrics such as IMA's or sport specific events