Quote:
Originally Posted by Andrew Schreiber
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. |
How did you determine the different distributions and timing of various actions? Other folks have mentioned having humans simulate the action, so just curious.
Another thought that someone on our team brought up was the idea of numbers in the game manual hinting at how many cycles would be done per match. For example, a capture requires (required) 8 goals, which might point to an average of 3 high goals per bot (which is about where we ended up at least).