# Pace Variability in GoldenCheetah

This post will only be relevant to you if you are a user of GoldenCheetah. If you are not, go check it out or maybe let this post convince you.

In Smashrun’s Pro plan, they provide you with a metric (and badges for it) called Pace Variability (or call it Speed Variability if you prefer). The way I understand this, they do the following: They look at the pace during your run in 10 second intervals. They then calculate the absolute average deviation over these intervals. Finally they divide this absolute average deviation by the average pace over the run. This ratio is the variability they use.

Interested by the idea, I decided to see if I could add it myself to GoldenCheetah. This seemed to be easy enough as a User Metric. In Preferences, go to Metrics -> Custom, and add one. Use the following meta information:

- Symbol:
`Pace_Variability`

- Name: Pace Variability
- Type: Total
- Precision: 10, Time checked. This is the size of the samples used by GoldenCheetah, we want to work with samples each 10 seconds long. Depending on your preference, you can change this value to be smaller or larger.
- Metric Units:
`%`

- Imperial Units:
`%`

- Conversion Factor:
`1.000`

- Conversion Sum:
`0.000`

Finally, we need some code to define the metric. The explanation of their language can be found on their GitHub wiki.

Program:

```
{
# initialise aggregating variables
init {
# Average_Speed is a built-in metric for the entire activity
avgspeed <- Average_Speed;
samplecount <- 0;
total_diff <- 0;
}
# For every sample, do the following
# (see "Precision" value to see how big samples are)
sample {
# SPEED is specific to the sample
diff <- fabs(avgspeed - SPEED);
total_diff <- total_diff + diff;
samplecount <- samplecount + 1;
}
# calculate metric value at end
value {
avgdeviation <- total_diff / samplecount;
avgdeviation / avgspeed * 100;
}
# Not sure what this one does
count { samplecount; }
}
```

After adding the metric, GoldenCheetah will require a restart for the new metric to take effect. After restarting, it will calculate this metric for every activity present. This part may take a while depending on how many activities you have. Finally, you are able to use this metric like you can any other built-in metric. For example to create graph or just to show the value somewhere.