Say I want to calculate the velocity of two datapoints (A and A'), each having a score, and a time published (A' is a future version of A, and has a higher score). This would be
[A'(score) - A(score)] / [A'(time published) - A (time published)]
What I want to capture are trends with high velocities. This means I want a score going from 20 to 200 having higher weight than 8500 to 9000. So I thought I'd normalize this data by dividing the scores by a baseline.
Ex. if A(score) is 2, and A'(score) is 3, the baseline is 2, so in the formula above,
A'(score) - A(score) would be (3/2 - 2/2)
However, this means that when the numbers are this low, the velocities will be very high (since on the other hand
9000/8500 - 8500/8500
produces very low velocities, given that time difference is constant in this example only, however normally, time differences are variable).
Is there any way to reduce the impact of low starting scores WHILE at the same time allowing jumps from, say, 20 to 200 being significant? Thank you.