Even if any of these guys were true winners they are putting the blame in the wrong place. It's not the software it's simply the result of variance. Some variance simulations are very sick. People can be extremely unlucky over the short term, however short that is, but should win over the long term, however long that is if they are a true winner. People having trouble grasping this. I bet tilt makes a lot of us losers.
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lol
blame your bad poker on the company set up
games are fixed
Fukk someone who participates and then spills his guts, just for that i think this letter dude is a piece of shit
bbbbbutttttt
i don't believe it anyway, its bogusComment -
I'm glad we finally found the truthComment -
Play enough hands and you'll see this frequently. How many hands have you played in that 5 year period? According to holdem manager, I've played 24,818 hands since the first of this year. And thats only on Pokerstars. Some people see 25k hands in a lifetime, you see where I'm going with this. Weird board textures and all-ins with the same pairs etc is no proof of sites cheating players.Comment -
Holdem manager could be lying to youComment -
where is your full tilt money losers?Comment -
This is definitely one of the things I've questioned about online poker; generating "random" decks is a non-trivial problem. You can't do some variation of Rand.Next() to compile decks; those values can potentially follow a very deterministic pattern. I'm fairly confident those poker sites have an in-house random deck generator algorithm that would at least raise red flags.
Also, there is no assurance that the decks are compiled in a way to induce more action; I'd be interested to see if certain scenarios happen far more often than expected in online poker. Examples would include the 1) number of decks w/ multiple top-tier starting hands dealt out or 2) number of decks with multiple solid starting hands where the flop would give the nuts to one guy, but he would get runner-runnered.
Hopefully online poker regulation will address this in some manner.Comment -
online poker has already addressed this, whether you believe their explanation or not, they have already stated they don't use in house random generator functions, you could research before tripping yourself
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SHUFFLE
"Anyone who considers arithmetic methods of producing random digits is, of course, in a state of sin." - John von Neumann, 1951
We understand that a use of a fair and unpredictable shuffle algorithm is critical to our software. To ensure this and avoid major problems described in [2], we are using two independent sources of truly random data:
- user input, including summary of mouse movements and events timing, collected from client software
- Quantis [3], a true hardware random number generator developed by Swiss-based company ID Quantique, which uses quantum randomness as an entropy source
Each of these sources itself generates enough entropy to ensure a fair and unpredictable shuffle.
Shuffle Highlights:
- A deck of 52 cards can be shuffled in 52! ways. 52! is about 2^225 (to be precise, 80,658,175,170,943,878,571,660,636,856,4 04,000,000,000,000,000,000,000,000,000, 000,000,000 ways). We use 249 random bits from both entropy sources (user input and quantum randomness) to achieve an even and unpredictable statistical distribution.
- Furthermore, we apply conservative rules to enforce the required degree of randomness; for instance, if user input does not generate required amount of entropy, we do not start the next hand until we obtain the required amount of entropy from the Quantis RNG.
- We use the SHA-1 cryptographic hash algorithm to mix the entropy gathered from both sources to provide an extra level of security
- We also maintain a SHA-1-based pseudo-random generator to provide even more security and protection from user data attacks
- To convert random bit stream to random numbers within a required range without bias, we use a simple and reliable algorithm. For example, if we need a random number in the range 0-25:
- we take 5 random bits and convert them to a random number 0-31
- if this number is greater than 25 we just discard all 5 bits and repeat the process
- This method is not affected by biases related to modulus operation for generation of random numbers that are not 2n, n = 1,2,..
- To perform an actual shuffle, we use another simple and reliable algorithm:
- first we draw a random card from the original deck (1 of 52) and place it in a new deck - now original deck contains 51 cards and the new deck contains 1 card
- then we draw another random card from the original deck (1 of 51) and place it on top of the new deck - now original deck contains 50 cards and the new deck contains 2 cards
- we repeat the process until all cards have moved from the original deck to the new deck
- This algorithm does not suffer from "Bad Distribution Of Shuffles" described in [2].
PokerStars shuffle verified by Cigital
PokerStars submitted extensive information about the PokerStars random number generator (RNG) to Cigital. We asked this trusted resource to perform an in-depth analysis of the randomness of the output of the RNG, and its implementation in the shuffling of the cards on PokerStars.
They were given full access to the source code and confirmed the randomness and security of our shuffle. Visit the Online Poker Random Number Generator for more details.
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