Today we introduced improved support for runners in strive.ai we eluded to here. We’ve made the following changes: Removed the Run Pace threshold type Introduced Pace Best Efforts for 400, 800, 1600, 5K and 10K distances (aged over 8 weeks
Reader beware, this is a nerd post! One of the most common types of machine learning is used to detect outliers in a dataset. Today we introduced several “anomaly detection” algorithms into the beta that assess your activities, power thresholds and segment efforts. Why do we do
Admittedly, we’ve started out with a focus on cycling with power. We attempted to mix in run pace thresholds using the same time duration concept [1, 5, 20, 60] as we do for heart rate and power. We learned
Last week we introduced our first performance centric feature in the app called Power Factors. It analyzes the peak thresholds over the last 8 weeks to identify what variables are having the most positive and negative impact on your results. We
After opening the beta to ~100 athletes we started to see some issues with the bike classifier failing to handle certain situations. For example, many of the higher end athletes have several bikes equipped with power with surprisingly similar riding characteristics. We