Somehow I missed this thread as well. Thanks for clone chipping it!
I couldn’t disagree stronger with the nay-sayers here. I would agree that this information isn’t useful on its own given this high variance, but there are a number of interesting possible use-cases:
- We can cluster the data and see what variable impact the credits required in a game. This could lead to an understanding, for example, of if this variable is mainly a function of the corp, or the runner. If it isn’t clear which, we’ll see which identities might flip the equation.
- We can correlate the win/loss with credits expended, possibly after separating different corp/runner identities. It isn’t clear what will come of this.
- We can correlate this data with output from simulations (e.g. QuantANR) to validate that they are reporting the proper values. Though we aren’t doing hard science here, validations from different perspectives is a required part of strong and correct scientific work.
- We can better understand warning signs during deck construction for if an econ is insufficient/over-compensating. This will save testing time for people who aren’t deep in the meta. I live in the world of jank, and it is not trivial to understand how to tune an economy and a tempo (this is why I started QuantANR).
All of this will 1. give us an interesting framework with which to think about the game, and 2. possibly inform deck design. The act of modeling systems in science isn’t to out-do the experts that can make fine-grained decisions (which breakers are best in the current meta); it is to try and create a conceptual framework to think about the system from a higher level. One area this could be useful is to provide a set of idioms that new players can keep in mind that are “often correct” for their forays into deck building. This can increase the appeal of the game, and broaden our audience.
So @db0, @mtgred, if it is possible to get a data-dump, even if it is flawed, with credits/clicks/identities/win-condition/who won, we might be able to do something “interesting” with it. Even better would be the credit curve over the game, but that might be asking too much.
In short, if you aren’t interested in the data, that’s fine. I think that some of us can make good use of it. Experimentation and exploration of the unknown (even in the face of intuitive roadblocks) is essential for successful communities that don’t want to ossify.
That said, I’m the author of the QuantANR series, so I have an interest in getting a different perspective on this game we enjoy.