Good job and thanks for posting the results and your analysis!
I was a bit surprised by the first round of analysis because I never experienced lower hit-rates in areas that are mined heavily. Your tests indicate that the reason is probably that the “recovery rate” is just a few minutes (5-15), so unless you are closely following another miner you will not be affected too much.
As I remember correct HR for every miner was above 30% so that was ok and a timer of 15 min is pretty fast
Question is also if claims where maxed and I think that was the case cause looking at TT return, can’t say where, but this area I know for having very good or very bad returns, nothing in between and ofc I have no idea what makes those differences.
But was great fun again and I’m in for the next round !!!
I’m not really sure what you’re getting at there, but if you mean predicting the true average hit rate for a given time or area, the much simpler terminology would be a sequential probability ratio test looking at how often you get hits and misses. That’s tricky at best though, and isn’t really relevant to the other miners question, but rather how do you know you’ve had enough samples to make a decision. The true mean of the hit/miss random number generator can vary, and if you get more samples, you get a better idea of what that mean is. That’s basically how the confidence intervals in the graphs work too.
I hadn’t read that one in years and forgot about it. Your test two was kind of in the ballpark of our first test for hit rate, and you waited 8 minutes instead of 5. That was a little while ago (there’s no way 2010 is already almost 10 years ago) and single avatar, but if nothing has really changed, it’s probably safe to say you need to wait at least 10 minutes to start getting back to normal HR.
It’s been years since I’ve seen that and I forgot about it. A lot of that is actually the type of analyses I do in my line of work too, so a lot of the concepts there apply to what we’re doing and vice versa.
It makes sense to a degree. The way those confidence intervals work is that if you repeated this experiment 100 times, 95 of those times you’d expect the average hit rate to fall within that range. In that paper, the confidence intervals around their average of 27.1% would be between 26-28%. We were working with smaller sample sizes, so our confidence intervals were much wider, but 30 is normally considered a high enough sample size to run statistical tests without problems associated with say a sample size of 10.
What about a variable sample size … Say of up to when the first NRF occurs in a mining run ?
Here’s my hypothesis, when mining, you are either removing or adding claims to the pool in the form of peds. On average you can expect to get I think it was 95-98% of what was used back as usable resources..
If I am correct this value can be “skewed” by simply “quitting while you are ahead” so to speak and letting other avatars spend their probes to “recharge” the pool.
The analysis can handle variable sample sizes, but I don’t see that helping anything here. If you’re just going up to the first NRF, that’s not really allowing for randomness either, and you’re going to have tiny sample sizes you can’t infer anything from.
There’s also a caution with the “quitting while you are ahead” approach. You need to differentiate between randomness just giving you a lucky 40% HR in a few test drops versus the true mean HR being 40% for that given location/time. In some sequential samples, you can get wide swings even if the mean was 28% where you get up to 50% HR for 30 drops, then nothing for the remaining ones. If the true mean was 28% in that case, you aren’t quitting while you’re ahead (unless you quit the game entirely) because your returns will roughly approach the mean over time (called regression toward the mean)
We can’t really easily test how the loot pool works, just how HR varies or doesn’t under controlled conditions since we can generate data for that.
HR is very different than the “usual” for the moment, (MA adjusted it) with lower HR and bigger claims for people to try those boxes from the “mining” event cause they need a VII claim to get a box.
Also spawn rates of resources vary a lot, it’s normal that belk/blaus are there in a few minutes and rare ones take longer.
I understand what you are saying. But what controlled condition improves HR ? Does not repeatably mining at the same coordinate set improve HR ? I would say yes…
Is the system absolute ie does it rely on certain coordinate sets to generate claims or is it more relative ?
Does it create an array like Project Entropia did ?
I don’t understand why MA would put in a system that works like this… It encourages miners to wait, ie not to participate in the game rather than causing tools to decay. It makes no sense…
What I can tell is how many peds I am taking out of the loot pool. The issue with the game and I am noticing it more and more is how “front loaded” it is. The game gives good loot when you have been offline for a while in order to encourage you to play.
It’s like a form of entropy (I know, what a concept) with each avatar that is active shedding off some of their heat, in this case peds, to those that are less active ie colder.
We’re getting off the topic of this thread, but even if that, those claims still need data with formal testing to back it up. This thread isn’t really a theories thread, but is focused on how hit rate varies based on other miners in the area or related testable data.
