Quote:
Originally Posted by dardeshna
During the beginning of the season when working on strategy and prototyping, we try to predict possible robot cycle times and average points pet match. Generally, at the end of the season, we look back on these estimated values and laugh, knowing that they were totally unrealistic.
How do other teams predict predict cycle times and average number of points scored per match? Is there a rule of thumb people like to use?
Cheers,
Devin
|
I typically worry less about predicting absolute scores and prefer to focus on analyzing the impact that doing actions at different rates has on the outcomes. For example - 2014 if you could pass about as fast as you could acquire a ball there was a distinct advantage but if it took you a lot longer there was less advantage. By finding how long you CAN take to do things before they become less valuable you can drive your strategic design.
Another good example is when you have multiple goals with differing point values (such as 2013 or 2016, but I only have the 2013 model built) Obviously hitting the 2 point goals was easier so your accuracy was increased, but we needed to figure out how much different the accuracy had to be to make up the point difference. This is another case where you're not looking exactly for a raw cycle speed but instead looking at points where the plot of scores reaches a local maximum.
I've recently started using an online tool called guesstimate for building these models. It's reasonably easy. Here are links to
2013,
2014, and
2015. Mind you, these are not really complete models, they were built to tell me what I needed to know about the game based on our discussions. Other folks may have different needs.