Topic: Analysis of impact of pool hopping through numbers (Read 1001 times)
Slush.
Howdy folks... I notice some anomalous data having to do with low returns on short blocks, so I did some analysis and this is what I found:
This looks like some interesting data that seems to point at pretty substantial losses due to pool hopping in early parts of rounds.
Basically I ran a bunch of numbers and I'm looking at some anomalies that would be highly unlikely to be caused by chance.
Since I was seeing what seemed to be much lower numbers than I should have seen on short rounds, I had to do some data analysis with small sample set that had some progressive variance caused by systems going down from time to time.
So, my analysis is off a somewhat normalized figure of 5 chronologically continuous blocks average payout. I compared the 5 block normalized average to individualized payouts as a percentage compared to the average of all payouts in the sample set.
It shows that in blocks that move faster (are shorter), payouts deviate from running averages to the tune of 85.34% in this small sample set.
Larger blocks have a mean average of 104.47%.
You can see the analysis here:
https://docs.google.com/spreadsheet/pub?key=0AhhejNMzWwx8dGpYZE9FYXk3d05MQjVDNUxLcHNXZkE&output=html
In this small sample set, short rounds with the problem account for about 27% of the blocks.
This looks like some interesting data that seems to point at pretty substantial losses due to pool hopping in early parts of rounds.
Basically I ran a bunch of numbers and I'm looking at some anomalies that would be highly unlikely to be caused by chance.
Since I was seeing what seemed to be much lower numbers than I should have seen on short rounds, I had to do some data analysis with small sample set that had some progressive variance caused by systems going down from time to time.
So, my analysis is off a somewhat normalized figure of 5 chronologically continuous blocks average payout. I compared the 5 block normalized average to individualized payouts as a percentage compared to the average of all payouts in the sample set.
It shows that in blocks that move faster (are shorter), payouts deviate from running averages to the tune of 85.34% in this small sample set.
Larger blocks have a mean average of 104.47%.
You can see the analysis here:
https://docs.google.com/spreadsheet/pub?key=0AhhejNMzWwx8dGpYZE9FYXk3d05MQjVDNUxLcHNXZkE&output=html
In this small sample set, short rounds with the problem account for about 27% of the blocks.
Which pool was this done on?
Howdy folks... I notice some anomalous data having to do with low returns on short blocks, so I did some analysis and this is what I found:
This looks like some interesting data that seems to point at pretty substantial losses due to pool hopping in early parts of rounds.
Basically I ran a bunch of numbers and I'm looking at some anomalies that would be highly unlikely to be caused by chance.
Since I was seeing what seemed to be much lower numbers than I should have seen on short rounds, I had to do some data analysis with small sample set that had some progressive variance caused by systems going down from time to time.
So, my analysis is off a somewhat normalized figure of 5 chronologically continuous blocks average payout. I compared the 5 block normalized average to individualized payouts as a percentage compared to the average of all payouts in the sample set.
It shows that in blocks that move faster (are shorter), payouts deviate from running averages to the tune of 85.34% in this small sample set.
Larger blocks have a mean average of 104.47%.
You can see the analysis here:
https://docs.google.com/spreadsheet/pub?key=0AhhejNMzWwx8dGpYZE9FYXk3d05MQjVDNUxLcHNXZkE&output=html
In this small sample set, short rounds with the problem account for about 27% of the blocks.
This looks like some interesting data that seems to point at pretty substantial losses due to pool hopping in early parts of rounds.
Basically I ran a bunch of numbers and I'm looking at some anomalies that would be highly unlikely to be caused by chance.
Since I was seeing what seemed to be much lower numbers than I should have seen on short rounds, I had to do some data analysis with small sample set that had some progressive variance caused by systems going down from time to time.
So, my analysis is off a somewhat normalized figure of 5 chronologically continuous blocks average payout. I compared the 5 block normalized average to individualized payouts as a percentage compared to the average of all payouts in the sample set.
It shows that in blocks that move faster (are shorter), payouts deviate from running averages to the tune of 85.34% in this small sample set.
Larger blocks have a mean average of 104.47%.
You can see the analysis here:
https://docs.google.com/spreadsheet/pub?key=0AhhejNMzWwx8dGpYZE9FYXk3d05MQjVDNUxLcHNXZkE&output=html
In this small sample set, short rounds with the problem account for about 27% of the blocks.
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