Thanks for the article - I found it very inciteful!!
Well, calendar resources were also associated with poorer performance, just not to such a strong degree as trapping. I actually think the "truth" might be that all non-mining resources are worse, but we don't see the statistical signal for coastal resources, because that is offset by the added bonus of being on the coast.
I think there is a point to be made that trapping might have poor terrain. Furs spawn of flat tundra (bad) and truffles can be in forest or jungle (slow start). Ivory is often near flat desert (bad). I think this might be why trapping did so much worse than calendar. Another reason might be that at least calendar is on the way to philosophy, whereas trapping is a bit "out of the way" for a quick tech path to National College
Okay, so now I would like to ask you to revisit your previous article where you reasonably conclude that civ choice did not statistically matter.The data is from a set of games that were always Pangaea, always 6 player. The tier list was also designed by FilthyRobot for specifically these conditions, so I think it fits the bill quite accurately.
Okay, so now I would like to ask you to revisit your previous article where you reasonably conclude that civ choice did not statistically matter.
If you only pick games with mining luxes, assuming there are enough of them, can you statistically validate FithyRobots tiers? Or at least see some statistical difference between the top and bottom teirs? With six tiers, I would be amazed to find statistical difference between any two neighbors. But surely between the best and the worst? (Controlling as best you can for other variables at least.)
I agree, but there's always a danger to this kind of conjecture. It might be a case where "these starts result in you dying to chariot rushes" or something too...just as an example. Maybe there's correlation to strategic resource availability or in a given scenario a given player mistakenly attempts a path for a wonder that hurts more than helps.
It's still interesting data, but the conclusions drawn must be done so with care. I have a lot easier time envisioning why mining resources/coast are particularly advantageous (fast yields, powerful internal trade routes in an environment where external are less viable respectively). Is the variance in calendar starts really explained by just delaying the early outputs from worked tiles until calendar...is that really enough to make you so much less likely to win? If not, it might be pointing to strategy mistakes that he makes when presented with that scenario, which would also be insightful unto itself.
One hypothesis (that I think is a good one) that Sunbeam put forward is that it simply relates to how many hammers are in the terrain. Trapping and Calendar luxuries occur on flat land, and only provide a single hammer if they are on plains.
The model I fit that gave the numbers in the article fitted Coast (Y/N), Mountain (Y/N), River (Y/N), Natural wonder (Y/N) and luxury tech (5 categories).
However, I ran a large number of permutations eliminating different variables to make sure that the results I got were robust to changes in variable selection (I also tested for possible interactions, but found none, so didn't pursue that much further).
That is actually a very good example since one reasonably presumes the map generator algorithm would correlate the presence of mountains with hills. So mountain ends up being an indicator for higher hammers -- even though one cannot work mountains and the primary benefit is unlocking wonders and maybe observatory.Of course some concerns missing data, but no interactions should in principle (given all assumptions) mean that Mountain is not favourable due to increased likelihood of mining lux e.g.
Good to know However, I don't like 9 independent variables with n=180. Haven't studied logit-models in a while, so need to think about it a bit more. A simple simulation should be sufficient as a test though.
Just have a look at this thread and see that most questions are surrounding alternative specifications and the validity of those - very common when publishing statistical analysis. Of course some concerns missing data, but no interactions should in principle (given all assumptions) mean that Mountain is not favourable due to increased likelihood of mining lux e.g.
Of course, when studying a game all this becomes much easier since we can read the code. One cannot do the same in the "real world", since the "code" is unaccessible for us.
This is really interesting! I think it might show that we're rating civs wrong, and that start bias may be far more important than a civ's UA or UUs. I have tended to notice that when I play Poland, I get salt a lot, which is an extremely dominant start.
If you look at civs by start bias instead of by tiering, do you get a significant result? That might be a question to ask for a future installment.
So to shed some light on to this debate, I think having the data consistent for one player is actually really critical for this sort of analysis. There is an enourmous amount of variance in player skill and style, and so it's important to keep that variable constant.