One of the core features of strive.ai is branded Factors. In layman’s terms, Factors are variables (X) impacting a target variable (Y). The Factors we rolled out first are variables impacting your Threshold (heart-rate, bike power and run power). A specific example is how does temperature impact your 5 minute heart rate? For most of us it’s likely that higher temperatures lead to higher heart rate readings. You probably don’t need machine learning analysis to determine this!
In technical terms strive.ai’s Factors are the coefficients for a regression that fits all of variables we collect from your Strava activities (e.g. temperature, cadence, altitude, activity type, gradient, velocity, heart rate, weight, FTP, etc) as X against a target threshold (e.g. 1 minute bike power) variable Y. The coefficients can be perceived as a weights and each one has two noteworthy attributes:
- Magnitude: The larger the coefficient the bigger influence it has on the target variable. The magnitude is displayed in the center of the donut graphic to give you a sense of how much force the variable is putting on your threshold. We only display Factors that are material to the outcome (meaning they have a weight > 1).
- Direction: Coefficients can have a positive or negative impact on a target variable (Y). Positive direction variables are displayed in the donut in green sections while negative variables are displayed in red sections. We have two shades of red and green to visually give a sense of magnitude.
Using the simple example of how does temperature impact your 5 minute heart rate? For me it has a weight of ~2 in the positive direction meaning higher temp leads to higher heart rate over 5 minutes. Some of the negative forces include FTP which also makes sense since the more fit I am (greater FTP) the lower my heart rate is likely going to be when doing hard efforts.
Another way to think of Factors is feature selection in machine learning terms. The plan is to offer Factors across a wide spectrum of analysis across strive.ai. Today we do it for your Thresholds and we’re currently working on Factors that drive Segment Performance target variables (e.g. how does wind speed and direction impact your ability to hit a PR on a segment)?
Another interesting thing is we include Activity Type in the Threshold Factors to see which activity types are pulling out your best performances. For example for me, Run is a positive driver on 20 minute heart rate while Ride tends to be a negative force on heart rate over 20 minutes.
Here is a list of planned Factors as we march toward coming out of Beta:
- Threshold Factors
- Segment Performance Factors
- Bio Marker Factors (blood glucose, blood oxygen, and muscle oxygen)
- Body Geometry Factors (height, weight, BMI, fat %, hydration)
- Bike Fit Factors (stack, reach, etc)
- Weather Factors (temperature, pressure, humidity, etc)
- Training Peaks Factors (IF, TSS, NP)
As we collect more and more data we’ll add more and more factors. Our plan is to make the UI much easier to navigate in a mobile app allowing you to drill into a factor to see it mapped against the target variable to a visual sense of the relationship. Right now we just show the biggest positive and negative variable.