Mining loot analysis

There is no compensating for your 40% return.

Whether you have 40% or 150% return, in future runs you are expected to have 95% return in the long run.

Okey so if I understand it right, the answer is no.
Then the next question :p

Doesnt this mean that after a big hof/ath (a 150% return) you should quit mining? Feels kinda bad that the statistic then imply walk away while you can?
 
Okey so if I understand it right, the answer is no.
Then the next question :p

Doesnt this mean that after a big hof/ath (a 150% return) you should quit mining? Feels kinda bad that the statistic then imply walk away while you can?

Just as there is no compensating you for a 40% return, there is no UNcompensating you for a 150% return. So continue to fire away after your ATH.:D
 
Yeah, the conclusion is that you have exactly the same chances before your 30k HoF, as you did before it.

95% tt return on mining isn't bad right? As long as your markup returned is better than your markup used, you can cover that extra 5%.
 
Just as there is no compensating you for a 40% return, there is no UNcompensating you for a 150% return. So continue to fire away after your ATH.:D

Noodles u just want me to pump up the lootpool again. j/k :laugh:

So once a ped gained a 95% return on that ped is expected?
Still doesnt understand how you connect it to Jimmy´s post who said 95% in the long run. You 2 fight it out :)

EDIT: Dam! you both agree and I still dont understand *rofl*
But to sum it up, keep bombing?
 
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Jimmy after you added that link I think I understand better what you are trying to say.
It all comes down to memory? We dont know the answer yet? But indication from the statistic is that there aint no memory so loot aint predetermined.

I kinda tend to agree with Witte (luck loot) on the big loots but Lavawalker(decided loot) on the average.

Or it aint possible with 2 different systems working together?
 
Noodles u just want me to pump up the lootpool again. j/k :laugh:

So once a ped gained a 95% return on that ped is expected?
Still doesnt understand how you connect it to Jimmy´s post who said 95% in the long run. You 2 fight it out :)

EDIT: Dam! you both agree and I still dont understand *rofl*
But to sum it up, keep bombing?

What we are saying, is that the evidence from the data provided for this (and the hunting version) is consistent with loot being random over a given distribution.

That distribution (for this thread's data) has a 95% tt return rate. There is no evidence in data received by falkao so far, to my knowledge, that shows any evidence of memory (ie. past returns affecting future returns).

In which case, what has happened in the past will have no impact on your future returns. You were expected to get 95% in the past, and you are expected to get 95% in the future. That means, in your next 100000 PED tt spend, we would expect you to get 95000 PED tt back. And in the long run, we would expect your all-time return to converge to 95%.

So your HoF is irrelevant really. The real question is can you make a profit from a 95% tt return, from the markup of your loots? The answer I would wager is yes, provided you proceed wisely ;)

However, whilst there's a good sized data set here, it is not necessarily proof that there is no memory. This is a data set of people proceeding with a sensible method. I have seen plenty of theories based around 'if you deliberately throw away loads of PEDs you'll get a corrective big loot afterwards'. So for instance, there could be a minimum return, where if you returned less than 80% over a certain length/spend scale you'd get a corrective loot to push you back over 80%. A good way to test that would be to shoot 10000 PED worth of ammo into the air and see if you get a big HoF, or series of HoFs, amounting to say 8000 PED soon after. Personally I'm not at all convinced I would, so I'm not planning on risking it to find out. But if anyone has data from crazy tests like this, it'd be interesting to see the results.

Jimmy after you added that link I think I understand better what you are trying to say.
It all comes down to memory? We dont know the answer yet? But indication from the statistic is that there aint no memory so loot aint predetermined.

I kinda tend to agree with Witte (luck loot) on the big loots but Lavawalker(decided loot) on the average.

I would say the evidence points to either the opposite, or all Witte. The evidence from falkao's studies is fairly strongly indicative imo, that short term loots are based on a random distribution.

Whether big loots provide some corrective mechanism in a controlled way we probably cannot say for sure since it is hard to get enough data (we'd need lots of data with lots of big loots, and big loots don't happen very often).

falkao may correct me later, but I think that's a fair assessment of our understanding.
 
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well... don't want to kill your work but...

Nice theory :kos:

Now just one thing.... go to wikipedia, search for entropy, then go to the information theories meaning of entropy..... have fun ;)

ok I give you the links (hope I won't brake EF rules)
Thermodynamics explanation here
Information theory explanation here

Now think about it... why do you think "Entropia" is in the name of this universe?
What I think is that the fundaments of it is uncertainty, too many variables we don't know to predict the behavior of it.... I gave up on the maths ;)
I do what I feel is good, mostly works :D

just my :twocents: ;)

Lez. :wtg:
 
Jimmy and Noodles already answered sufficiently your question Sircus, but may I add the following.

As Jimmy mentioned, loot seems to be random. More precisely, we can't atm contradict the hypothesis that loot is random and therefore we do assume that it behaves randomly.

If we assume that loot behaves randomly, then gambling strategies can be applied. Your question was if it makes sense to use amps after several losses.

We have two situations here. A random system doesn't have memory and therefore today's returns are not related to future ones. That's like when using a fair coin, the probability for a head is always 50% independently from previous tosses. However, there is also another point of view.

Instead of evaluating single events you can combine them, like what's the probability to toss a tail after 3 heads in a row. Although, tosses are independent from each other, this probability is quite higher than 50%. Let me explain.

The probability for 3 heads in a row is .5^3 = .125
The probability for 4 heads in a row is .5^4 = .0625

The probability to get a tail in the 4th toss is still 50% but to get at least one tail in 4 tosses has a probability of 1-.0625 = .9375 and that’s about 94%. Hence, the more heads you observe the more improbable this series will become and hence the more probable a tail will become in the whole series but not as single event.

Now let's come back to your question and assume that you had 3 bad runs. Let’s define a run to consist of 1000 drops and we’re mining enmatter. We must now identify the relevant probabilities. The return rate is only an expected mean loot relative to expenses and it’s not the probability we're looking for.

We know from the loot model that till loot class 4 expected return rate is about 80%. In 1000 drops, assuming a find rate of 27%, we should have 270 finds. 1000 drops will cost (without decay) 500 PED and a return rate of 80% would imply to get 400 PED back. Loot above loot class 4 has an expected mean of 91 PED but a large range ranging from 40 to 200 PED. So if one is able to get loot from loot class 5 or above then his return rate will be quite close or over 95%. Now the probability of loot above class 4 is only .42% and hence overall all runs you can expect only about 38.5 PEC from it. However, seen from single runs there will be quite some runs with profit. In 270 finds you can expect 1.14 finds to have loot from this class. But what’s when you get none or 2 or 3? From the simulation that we did, we know that in runs of 1k drops at least 40% will have over 100% return rate and that’s the probability we’re looking for.

So let’s define a good run as a run with return rate above 100%. It’s probability is 40%, like the probability of a head when tossing a coin. Now let’s assume that we had 3 bad runs in a row.

The probability for 3 bad runs is .6^3 = 21.6%. So it’s not uncommon to have 3 bad runs in a row. The probability to get 4 bad runs in a row is 13% and hence the probability to observe at least one good run out of 4 runs is now 87%. That’s why persons do believe that they will have more luck after some bad runs. However, don’t forget that we’ve already observed 3 bad ones. The probability to have exactly 3 bad runs and then a good one is .6^3 +.4 = 8.6% and this is lower as 4 bad runs in a row. Hence, although the probability to observe at least one good run out of 4 is high, its realization after 3 bad ones is still quite low.

Very confusing I would say. But let’s repeat, to observe BBBB (means 4 bad runs) has a probability of 13%, to observe BBBG has only 8.6%, that’s an implication of memoryless. However to get at least one G out of 4 runs, which could be GBBB, BGBB, BBGB, BBBG, GGBB, GBGB, and so forth .. has a probability of 87%. That’s the reason why some do use gambling strategies.

One consist in influencing output like when using amps. In roulette games you can use a doubling strategy. However, such strategies do only work if you have the possibility to double infinitely often, and that’s the reason why banks did introduce table stakes. With amps we have a similar situation, amps are limited.

So all in all, I wouldn’t recommend any betting strategy using amps after bad runs, but if you’re lucky it will for sure make your day ;)
 
One consist in influencing output like when using amps. In roulette games you can use a doubling strategy. However, such strategies do only work if you have the possibility to double infinitely often, and that’s the reason why banks did introduce table stakes. With amps we have a similar situation, amps are limited.

So all in all, I wouldn’t recommend any betting strategy using amps after bad runs, but if you’re lucky it will for sure make your day ;)

Ah, I see where you're going now. Yes, a Martingale strategy is rarely advisable. Its a good way to ensure you will rarely lose, but when you do lose, you lose everything.
 
the big question remaining to me is "is loot absolute or relative". my data was mainly with constant setup, so absolute return equals relative. not sure if other's data was with switching setup during observation.

so far we interprete return memory purely as "peds back for peds spent", absolute relation.

but imagine it's relative (as in "every loot action gives ~95% of its own costs") then one could optimize absolute tt return by chosing right amps at the right time.

example: i notice i'm in a bad spree so i use no amps and get back less than 95% due to bad period. since i use no amps i keep absolute losses minimal. now return seems to pick up so i use 109 amps and get back more than 95% due to good period. absolute profit is maximal with maximal amp (considering markup=0%).

same could be applied to switching between snables and ara. or crafting filters versus hunnir.

if that was falsified already, please forgive me :)
 
removed.....
 
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the big question remaining to me is "is loot absolute or relative". my data was mainly with constant setup, so absolute return equals relative. not sure if other's data was with switching setup during observation.

comparing your data to Noodles ones, and he uses a different setup, doesn't show much differences.

example: i notice i'm in a bad spree so i use no amps and get back less than 95% due to bad period. since i use no amps i keep absolute losses minimal. now return seems to pick up so i use 109 amps and get back more than 95% due to good period. absolute profit is maximal with maximal amp (considering markup=0%).


Unfortunately the contrary is also true, you can have the feeling of being in a good run, you use amps but finally you had only loss.

As Jimmy mentioned, Martingale strategies can be applied but are rather risky.

However, we can't be 100% sure that loot is purely random and maybe some corrections are applied.

As per definition, gambling is a game where the outcome is related purely to randomness. In EU it seems that loot follows that definition but you still have possibilities to influence costs (skills, eco etc.) and therefore this contradicts gambling in some way. Furthermore, I still have the hope that we might find sooner or later, also loot corrections according to spend Peds.
 
No gambling involved in this, Mindark has access to all players data at all times, it would be simple to code something that looks at all those values, charts it and injects it back into the game - 5%
 
No gambling involved in this, Mindark has access to all players data at all times, it would be simple to code something that looks at all those values, charts it and injects it back into the game - 5%

sure we know that they can do everything but we still don't know if they do it.
 
the big question remaining to me is "is loot absolute or relative". my data was mainly with constant setup, so absolute return equals relative. not sure if other's data was with switching setup during observation.

so far we interprete return memory purely as "peds back for peds spent", absolute relation.

but imagine it's relative (as in "every loot action gives ~95% of its own costs") then one could optimize absolute tt return by chosing right amps at the right time.

example: i notice i'm in a bad spree so i use no amps and get back less than 95% due to bad period. since i use no amps i keep absolute losses minimal. now return seems to pick up so i use 109 amps and get back more than 95% due to good period. absolute profit is maximal with maximal amp (considering markup=0%).

same could be applied to switching between snables and ara. or crafting filters versus hunnir.

if that was falsified already, please forgive me :)

Have any of you factored in your increased skills ?

What I mean is saying that you are having a bad run, do you get more skills on a bad run than what you do on a good run, you are looking for profit, perhaps you should look for balance between skills and financial gain
 
Catching up on this thread. I kept track of my hitrate for a while.
[br]Click to enlarge[/br]

Nearly perfect agreement with Steffel's 27.1% number. Some of the drops were ore (mostly unamped) and some were enmatter (mostly 101 amped).

I have some comments on your updates to the first post (great work!). Will try to get to them later.

I am about to rock the boat as it were, we have a lets just call it 25% hitrate, so its a 1 in 4 success rate, I am assuming that you are carpet bombing, so just bomb every 4th instance of where you would normally bomb....

Better still , lets do this with CND, its a small area and makes a nice chart, a quarter of cnd's size is 250 meters ie 25% of its max size for all of those players that are viewing this forum and do not understand what the heck is going on in it.

What would be the odds now, of striking a deposit ?
 
according to this 6026 Zinc stone = 3013 std PED class 9 is reachable without an amp.

Sorry to butt in on this thread....

there are many ways to interpret class 9

3+3+3 could be a class 9 ie an area with 3 class iii in it... You may be limiting yourselves if you categorize it as a single deposit.

PS Is there any ratio between ped value and class size ?

Cheers
 
You are right--these experiments are not as controlled as we would like them to be. However, having some data to analyze is much better than having none . . . and I think the big picture is coming together quite nicely. We just need to be aware of the limitations of the conclusions that can be made.

In that spirit, Falkao is doing a great job of applying statistical tests to the return rate and hitrate data that we do have, to give an idea of the confidence we can have in the conclusions. Since the confidence intervals continue to shrink, we seem to be moving in the right direction.

In the meanwhile, good luck with your 22% hitrate; I hope (and expect!) to see it improve soon!

It would be impossible to analyze this completely.

1) Its never the same time as the 1st run that you performed
2) Its never the same method, your movements would always differ, take a detour here and there
3) Its never the same avatar, skillgain, wear and tear on equipment, who knows.
4) X - for anything other differences that may be there that we just dont know about...

Ok Enough of this thread for 1 day ....
 
Ah, I see where you're going now. Yes, a Martingale strategy is rarely advisable. Its a good way to ensure you will rarely lose, but when you do lose, you lose everything.

I personally don't like those strategies as there are some flaws:

Making it more simple, the idea behind is as follows with B = bad run, G = good run

the probability of an n*B is rather low and the contrary 1 - n*B becomes more probable. Using our numbers this would mean p(B) = .6, p(G) = 1 - p(B) = .4

p(BBB) = .6 ^3 = .216, therefore the contrary is .784.

Now let's assume that we have observed BBB, whats the probability of observing BBBG?

P(BBBG|BBB) = .6^3 * .4 = .0864

This is very low, and lower than

P(BBBB|BBB) = .6^3 * .6 = .1296.

So why do people believe in Martingales? Instead of looking at P(BBBB|BBB) they use

P(BBBB) = .6^4 = .1296, which is the same as P(BBBB|BBB) .

The contrary of P(BBBB|BBB) is P(BBBG|BBB) as mentioned above.

The contrary of P(BBBB) is 1-P(BBBB) which is a different thing. It is

P(BBBG) + P(BBGB) + P(BGBB) and so forth, so all combinations of BG except BBBB.

As we have already observed BBB the only possibilities that do remain open are BBBB and BBBG as given above. So all this concept does not hold.

Nevertheless, it is true that at least a single observation of a G becomes more probable the more runs you do. So without observing anything a Martingale is valid but that contradicts the martingale itself.
 
Now let's assume that we have observed BBB, whats the probability of observing BBBG?

P(BBBG|BBB) = .6^3 * .4 = .0864

This is very low, and lower than

P(BBBB|BBB) = .6^3 * .6 = .1296.

I'm not sure I understand - surely P(BBBG|BBB)+P(BBBB|BBB)=1?

p(BBBG|BBB)=p(G) no?

P(BBBG)=0.6^3*0.4 as opposed to P(one G, three B)=P(GBBB)+P(BGBB)+P(BBGB)+P(BBBG)=4*0.6^3*0.4
 
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Buy big mathemagic skill implant :scratch2:
 
I'm not sure I understand - surely P(BBBG|BBB)+P(BBBB|BBB)=1?

the following is valid

P(B|BBB)+P(G|BBB) = 1

due to independence of events and therefore we're back to
P(B) + P(G) = 1

p(BBBG|BBB)=p(G) no?

this would be
p(G|BBB)=p(G)

P(BBBG)=0.6^3*0.4 as opposed to P(one G, three B)=P(GBBB)+P(BGBB)+P(BBGB)+P(BBBG)=4*0.6^3*0.4

yes if we count only one G. I normally do refer to at least one G, for instance P(GGBB) or P(GGGG) would be also valid.

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From independence of events the following is also true

P(BBBG|BBB) = P(BBBG)
 
-> If an avatar would do for a few months in a row only fapping and buying/selling from auction, how would his actions be classified by the system?

Is the decay/auction fee he spent during that time considered as bad runs, good runs or it's not taken into consideration when referring to the 95% all-time return for that particular avatar?

-> How is it possible for the system to achieve a 95% return for an avatar if the system has no memory of the avatar's past actions.

Sitram
 
-> If an avatar would do for a few months in a row only fapping and buying/selling from auction, how would his actions be classified by the system?

Is the decay/auction fee he spent during that time considered as bad runs, good runs or it's not taken into consideration when referring to the 95% all-time return for that particular avatar?

-> How is it possible for the system to achieve a 95% return for an avatar if the system has no memory of the avatar's past actions.

Sitram

This thread only contains mining data, and any conclusions should only be applied to the mining profession.

These data should not be used to assume that the system targets a 95% mining return for all avatars, only that an average avatar will have 95% mining return. No memory is required in this case, it is simply the nature of randomness. There will be "lucky" and "unlucky" avatars, even over long time periods/peds cycled.

Finally, the 95% number is an estimate from these data, there is uncertainty in the number. The current confidence interval is between 90% and 110%, according to falkao's explanation in the first post.
 
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From independence of events the following is also true

P(BBBG|BBB) = P(BBBG)

OK, I was reading P(BBBG|BBB) as meaning the probability of getting to BBBG given that we've had BBB already (in other words the probability of getting G after getting BBB). But evidently that's not what you mean by it.
 
OK, I was reading P(BBBG|BBB) as meaning the probability of getting to BBBG given that we've had BBB already (in other words the probability of getting G after getting BBB). But evidently that's not what you mean by it.


indeed, with P(BBBG|BBB) I had in mind to simply observe P(BBBG), but I have to admit that this notation is misleading as one normally would assume with P(BBBG|BBB) to get BBBG after BBB, so a total of 7 tosses and not 4.

Sure to get BBBG you need to observe BBB first, but that's not P(G|BBB). Both probabilities are connected as follows:

P(G|BBB) = P(G and BBB)/P(BBB)

since G and BBB are independent we do get

= P(G) * P(BBB) / P(BBB) = P(G).

P(G and BBB) is what I have written as P(BBBG) and therefore we can also write

P(G|BBB) = P(BBBG)/P(BBB) and therefore

P(BBBG) = P(G|BBB) * P(BBB) = P(BBB) * P(G)

My intention was to show that P(at least one G out of n tosses) has nothing to do with what you have observed till some point in time. Using the above notation we can verify that.

P(at least one G out of n tosses) = 1 - (no G out of n) = 1 - P(B)^n.

So P(at least one G out of n tosses) will converged to 1 as n becomes infinitely large.

However, P(G|B n times) is only one possibility to observe one G and it's still P(G). Having observed B n times, the alternative is still P(B|B n times) which is still P(B). Since P(B) > P(G), also P(B|B n times) > P(G|B n times), and hence still the same odds.


So why do Martingales exists?

Since P(at least one G out of n tosses) converges to 1, you'll get a G sooner or later. Please note that this is before any observation was done and has nothing to do with what was observed so far. Since to win at the first toss has less probability when P(G) < P(B), you're able to recover from losses when you increase bets. With amps you have 9 possibilities to do so. So you should get a find in the first 10-12 drops. Unfortunately its also possible to wait much longer, not very often, but when it happens your loss will be quite considerable.
 
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I'm sorry, I know this is kinda pointless post, but I have to :laugh: at all the math jargon... GGGG+BBB*n/B-GG+1 etc :eek:
 
This is probability, B=bad G=good result
 
:scratch2:
Bump for interest value alone
 
whew...i really hate math. needs ta find me some aspirin... some good info here though, even for a dummie like me.
 
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