pic: OPRs compared via standard deviation distribution - 2008-2015

[Ian Curtis]

Originally Posted by Caleb Sykes

I'm curious to know what use you have in mind for these plots.

I suppose if you think your team is in the x percentile, you could target a score, but doing this effectively would require knowing both the average score and the standard deviation of scores early in the build season. I'm impressed enough with teams that predict the average score reasonably well early on, I don't even know any teams that predict standard deviations early on.

My rule of thumb is to estimate how many points I think 3 small children could score in a regulation length match. Or, ask a student what they think the average score will be and divide it by (5-n) *, where n is years of FRC experience.

This is an interesting metric to look at games with though. What is the ideal shape of this curve?

For example, you probably don't want a really sharp tail at the low end (bottom 20%). By this metric, Lunacy was terrible (make sense, if you didn't reliable move the field cleaning Roomba made a better alliance partner). Why is Overdrive the next worse? Penalties?

You definitely want some slope (where would the fun be in identical robots?), but where should the best top out? I think it's interesting the top tier was most limited in a game without explicit limits (or could top tier teams fill all the trailers?).


*The constant is somewhat team dependent, I think for some teams this constant is much closer to 4, and for other teams significantly higher.