We capture your email address used in your Strava profile for our communications. We’ve noticed a fair number of users have invalid email addresses in their Strava profile. You also need to make sure the email account has added firstname.lastname@example.org to it’s safe or approved senders list to make sure our emails don’t get filed as JUNK:)
When we introduce the mobile app version of the solution we’ll likely move away from email to notifications.
This is pretty common at the start given we only pull over 8 weeks of history when you’re approved as a strive.ai user. We’re capped at 30K hits against Strava / day and we’ve been routinely hitting 18-20K so we’re capping it at 8 weeks for now. This history gets the machine learning started but doesn’t sufficiently allow it the accuracy out of the gate. Every 2 weeks, we bring over additional activities and the algorithm gets better over time.
We have seen some athletes with multiple road bikes or MTB bikes which is difficult for the algorithm(s) to classify given the rides are often very similar. We have a solution to this problem but can’t deploy it without help from Garmin or Strava as the key data elements to this solution aren’t currently available in the Strava API (sensor identifiers).
Give the process some time and let us know if it’s still not accurate after a month or two. You can also turn off notifications for this should you get sufficiently annoyed with it:)
One cool feature we’re adding now is an accuracy test of each predictor in the system. Each time we train a new predictor we’ll automatically test it and if it doesn’t achieve some threshold of accuracy we won’t use it. This will prevent users from getting frequent Gear alerts until the system is good at predicting accurately.
Some have asked why focus on a bike prediction. Like other data elements we’re capturing in the beta, gear_id is a foundational element for more interesting algorithms coming down the road. For example, predicting when your tires or chain need to be replaced or predicting if you could top 10 a segment at a certain power will more than likely depend on knowing what bike was used to complete a segment in the past.