WOT Calculator

[BJC]

To reiterate what was said before, giving specifications to different scores is a good idea. For example, Pushing may be determined by your wheels Coefficient of Friction. General Andymark Placation Wheels (1.4CoF) may be a 3 on a scale of 1-5 on which Omni or Mecanum wheels could be a 1 and Pneumatic wheels or Tank treads could be a 5.

The other thing I would like to reiterate is that by using this excel spreadsheet instead of having to do the Math several times; you can quickly change or eliminate the weights of criteria to see how different criteria will affect what type of drivetrain is optimal. It’s really quite fun to play around with.

Originally Posted by Joe Ross

Thanks for posting the spreadsheet.

Your welcome, the actual spreadsheet wasn't hard to make. Its just the sort of thing that is handy to have, but you never really get around to actully making.

[Joe Ross]

Originally Posted by BJC

To reiterate what was said before, giving specifications to different scores is a good idea. For example, Pushing may be determined by your wheels Coefficient of Friction. General Andymark Placation Wheels (1.4CoF) may be a 3 on a scale of 1-5 on which Omni or Mecanum wheels could be a 1 and Pneumatic wheels or Tank treads could be a 5.

Rather then use another subjective value to represent an objective measurement, you can actually use the measurement as your criteria. For example, if you multiply the CoF by 2.5, you get a value that scales very nicely within a typical range of 0.7CoF to 2.0Cof. The 0.7 gets a score of 1.75 and the 1.4 CoF gets a score of 3.5. This transforms the objective value to the same scale as everything else.

It is important to do the scaling. If you were comparing CoF to Weight, you wouldn't want a small weight change of 20 lbs to 22 lbs to swamp the large CoF change from 1.0 to 1.5. If you were to multiply the CoF by 2.5 as done previously and divide the weight by 5, you'd end up with the CoF change being worth 1.25 points in the score and the weight change being worth 0.4 points in the score.

It can also be misleading to do this however, because your scaling factor is still subjective. Be careful that just because your score now goes out to a few more decimal points, it isn't treated as more accurate.

You can even get fancier with the scales. For example, you could decide to treat everything with a CoF of < 1 as below normal, and not worth distinguishing. You could assign everything < 1 a score of 1, and then use a linear scale above that. You could also do something fancy like use an exponential function that has small changes in scores for CoF < 1 but larger changes above that. Again the warning about false precision applies.