5 pitfalls that make you think that online poker is rigged
Maybe you know, but in case you don’t, I am an owner of a free online poker with friends no download no registration plataform called Poker Now, if you don’t know it, you definitely need to check it out. And if you want to know more about the history behind of creating a poker app, you can check it here.
As an owner of a poker site, I receive almost daily complaints about “rigged shuffling” to promote action or make some player win more than others, but of course, it is not true, on Poker Now I do my best to offer a fair game to everyone and if you want to know more about the technicalities of it, you can read more about it on the end of this post.
Talking with people through these years and analyzing the results and the arguments, I will show to you recurrent pitfalls (speaking specifically of Texas hold ’em) that people get trapped when analyzing the results of an online poker session.
1: Analyzing statistics with human feelings
We human beings suck a lot when analyzing statistically correct randomness. We human beings have the incredible capacity to search for patterns everywhere and this has helped us a lot in many areas of our lives. Yet when it comes to randomness, this searching for patterns tricks us more than it helps us. There’s an article from The Independent showing that people often get mad when truly random selection is used for songs. When listening to songs, if the music player plays 5 songs consecutively from the same artist, which is possible, we automatically feel the algorithm isn’t random and it is biased toward this artist. However, our search for the pattern is tricking us there because we are not seeing the big picture. When it comes to poker, you will always remember those quads, but you will quickly forget that you folded 200 hands before those quads. Remember: statistics without numbers are just speculation.
2: Not considering the 7 cards pool
A lot of people “feel” the results by the showdown, but there’s a catch here. In a hold’em game, not considering the 7 card pool of the showdown is a common mistake. When you arrive in the last phase of a poker game, you try to form a combination of 5 cards out of a pool of 7, and it changes the probabilities a lot in comparison to a 5 card pool poker game. For example, the probability of a flush in a 5 card pool is 0.19%, but in a 7 card pool it increases to 3.03%, a dramatic change in the probability. https://en.wikipedia.org/wiki/Poker_probability
3: Mixing the statistics of multiple actors
When you are analyzing poker results you will make your life easier by focusing on only one actor (a player, for example) at a time. People usually tend to aggregate all the results from all players into one big results pool. For example: in 600 hands, player A gained a hand with four of a kind and some hands later player B gained a hand with four of a kind too. For a lot of players, it will look bizarre and they will say: “four of kinds are super rare hands”, but if you analyze it the probability of a four of a kind in a pool of 7 cards is 0.16%. In 600 hands, almost 1 hand will be a four of a kind on showdown and this applies to both players. So if you analyze player A, one quads in 600 hands played is fine, and the same applies to player B. https://en.wikipedia.org/wiki/Poker_probability
Let’s take another example where you are looking at 200 hands at a 7 player table. If you are going to group all the best hands made by any player, then the denominator in the analysis needs to 1,400 hands, not 200 hands (7 players * 200 hands = 1,400 hands). The only way you can use a denominator of 200 is when you analyze the hands for one player.
4: Analyzing small samples
If you flip a coin 5 times and all 5 times the coin lands with heads, can you claim that there is a 100% possibility of this coin only giving you heads? No, because you analyzed only a small sample and the random events don’t choose when they will happen. However, over the long term, with a big sample, you can see a clearer picture of the statistics of heads and tails. So when you are analyzing poker hands, you can play 5 hands and get quads, it will generate a super bizarre statistic, but if you play 620 hands later and the quads never appear again, the statistic will be ok.
In poker, by what I see, to start to have a slightly good picture of something, you will need at least 300 played hands, and bigger is your sample, less is the deviations on the statistics.
5: Only considering the hands that went to showdown
A very common pitfall is to think the number of the winner hands should only be calculated in hands that went to the showdown, ignoring the folded hands. This is not entirely wrong, but depending on the playstyle, the number of hands that go to showdown can generate a very small sample to analyze causing weird stats (small sample pitfall). So if your sample of “showdown results” is small, an alternative is to analyze by the number of dealt hands because when you are dealt, you formed a combination, you just don’t know what was the combination because you folded that hand before the last phase (ignoring the scenarios where you do rabbit hunting). When you analyze the winner hands percentage, you need to understand that your denominator will be the quantity of all hands you are dealt, even the folded ones at pre-flop, for example.
One parallel with this pitfall would be: you throw a coin 100 times, but you only saw the results of the landed coin 55 times, checking that it generated 30 heads and 25 tails. The chances of heads and tails are 50% for each, 30 heads of 100 throws are 30% and 25 tails from 100 throws is 25%, which points to a probably statically correct result of 100 throws because none of the possible results is over than 50% of 100 throws (not considering the deviation).
A little more about Poker Now
A lot of people claim that Poker Now is rigged because it generates an “unrealistic” quantity of “power hands”. At our Discord Server, many claims like this were already proven statistically wrong. At Poker Now I want a fair game for everyone, and I am always open to discuss this topic with people that have really noticed something and haven’t been trapped by these pitfalls. I wrote a script that simulates N hands with this shuffle logic (https://repl.it/@samuelsimoes22/Shuffling-Statistics) that was run literally 1 billion times by a person once and it proved to be statistically correct.
Besides that, I created a tool that analyzes the winner hands quantity based on the game logs at https://network.pokernow.club/game_log_analyzers/, so you will be able to see by yourself that your game is generating statistically correct results, even when you can’t notice this.
Thank you for your attention, feel free to say anything in the comments and don’t forget to follow Poker Now on Twitter.
