Thanks OP for the time taking into this.

Could filter the list by most profit and show? This way (i think) we could get a faily good idea if the counter argument that "strong players have crused PLO100 and moved up" can have any validity.

Also what format is this sample from, rush or regular ?

Must be regular tables as Zoom was unobservable until recently. Weirdly my results are significantly better at Zoom over a decent sample, probably just variance. Although I'm not even sure what a decent sample is for PLO, suspect it is close to 1m hands, which is why I'm particularly interested in the Stars claimed winrate data and the sample sizes they were using.

I dont know how, but zoom has been dataminable for a long time. Can almost guarantee you it wont be any prettier. Maybe some of the higher regs that only played zoom 100 cuz nothing higher ran, had good wr...

Cool, ye expected regular tables.

Need to quote joeri's method here once more. A lot of people ITT don't seem to understand this method (I'm looking at you, antchev). joeri's method is pretty much necessary in order to convince any critical thinker. His suggestion is a kind of Cross Validation. I'll quote it and then explain it again in different words, and explain its advantage:

So, joeri suggests the following: you ask the pokerstars people what they would define as a "good player". You then split all the dataset into two parts: a "choosing part" and a "testing part". The hands are split randomly between these two parts. Now you apply the criterion given by the pokerstars people, and find all the "good players", only according to the hands in the "choosing part". And now you find the overall winrate of these players, but only in the "testing part".

If the pokerstars people are right that good players are winning, then the results of the latter evaluation will be that the good players are winning in the "testing part". But if people ITT are right and the game is unbeatable, then these good players, who did well in the "choosing part", will now be around breakever or worse in the "testing part". This would mean that the reason we decided they were good is just that they got lucky in the "choosing part", but that in the "testing part" they did just as badly as everyone else.

For the sake of testing by ourselves, one can just choose the "good players" to be the players that have winrate of at least 3ptbb/100 in the "choosing part". In fact, if the claim that the games are unbeatable is true, then no matter how you choose the required winrate, you won't get a good winrate on the "testing part". That's the beauty of the method.

The advantage of this method is that it tells in a scientifically-solid way, without a doubt, whether the games are beatable or not. Thus I really recommend making this analysis, even simply on OP's data.

would also be interested in seeing this, pretty shocking shocking data, have been dabbling with some SS PLO but will certainly not consider switching over after seeing this - hope the reps can stop this ridic avarice!

By the way, I believe a rep can request data from PS during the meetings because this happened before at another meeting. Only issue is they can, and did last time, pick and choose what data to show which = bias. Probably worth asking anyway for whoever goes.

I think what Stars is referring to when they say that winrates are larger in PLO is that they are larger IN RELATION to the rake ^^.

yeap

1- Numbers are disgusting.

2- Stars won't change anything (at least radically)

3- Our only hope is a site built by players to the players

4- Profit??

http://forumserver.twoplustwo.com/45...e-see-1280010/

FTW?

Maybe.

the problem is that a site built by players for players won't be able to compete with stars.
i just don't see a way how we could attract fish better than stars, and only few would consider playing on a site where only regs play (might be wrong on that tho).

it's a bitter downside to capitalism that whatever industry you take, be it poker, be it music or whatever, it's always some multi billion dollar company taking the most $$$.

edit: +1 on joeri's method. absolutely crucial to point this out @ the meeting. if the results are indeed what most here guess they will be, stars at least won't get away with that cheapo-argument anymore.

Isaac; a list of top winners is absolutely worthless. Variance...

Big advantage of constructing the analysis like i proposed is that you can lumb all hand together. Instaid of "player A played 300k hands with winrate xxx" you will get like "the good players played 3 million hands in total with an average xxx winrate" . The second one is offcourse way stronger.

Well, I was saying I was interesting in seeing those numbers. I wasn't saying we should use that as a metric for disproving that there is a potential problem with the way SSPLO is raked.

However, I still think one has to be careful when using the players with the most volume as a criterion in determining how beatable plo 100 is. I could imagine a few problems in that. I mean what percentage of players that can beat plo 100 over a large sample actually do that when they can instead move up in stakes and most likely increase their earnings?
I also think that a lot of players who decide to play high volume at plo 100 are knowingly sacrificing winrate for rakeback.
This is why in my previous post I mentioned that I wasn't surprised to see that most of the highest volume players were not making that much.

Now what if the top 100 winners are all winning at 8bb/100 and have played between 100-170k hands? Is that "absolutely meaningless"?

thanks for your work guys!!! Here are some intelligent people around

Is it possible to increase your db directly by stars? Would be awesome.
I mean a lot of you guys would disagree on that:
Stars could get more rake on the higher stakes(let's say 600+) to rake less on the lower stakes. Would this even matter or is it just a drop in the ocean?
The rakeback on higher stakes isn't that significant imo, but I'm no SNE grinder...

^^^ jup, meaningless.

I agree that there are big sample errors in the op data though.

Well, I agree with you actually. That was a poorly chosen example. The winrates don't mean much over that sample size. But I think we agree on the point I was trying to make. Basically I'm saying we currently have a very incomplete picture of the distribution of winnings and losses and that PLO 100 is a unique stake to look at due to the nature of players moving in and out of the stake frequently and there being a lower percentage of professionals making a living from PLO 100 relative to higher stakes (atleast I believe this to be true).

sounds v nice. how much do u think sample would be skewed by getting in a chunk of players that mainly just bumhunt? (or arent we concerned with this, im a donkey here i just moved started playing PLO100 last week haha thought would transission to PLO, kinda rethinking that atm... )

About winrates aswell, i thought by getting a list of the most profitable players we could draw conclutions from it if winrates are in general very poor. As if the 9th biggest winner with some type of sample hold .5BB/100 we could assume games are seriously unhealthy cause with variance if winrates was anything near healthy we would see alot better winrates (assuming the top winrates would be running hot)?

Wow, eye-opening thread. Concerning and ridiculous

The math shows that this would be more than meaningless, assuming there are at least 5,000 regs. It would be meaningful if, for example, there were 1,000 regs or less. Here is the math:

Let's take a player whose "true winrate" is 0bb/100, that is breakeven. What are the chances for him being a 4 ptbb/100 winner over 160k hands?

Assume a usual standard deviation, for PLO, of 80ptbb/100. This translates into a standard deviation of 3200ptbb over 160k hands. (To get this, we compute 80*sqrt(160000/100).)

A winrate of 4ptbb/100 translates into 6400ptbb over 160k hands. So two standard deviations. The chance of a random variable to be at least two standard deviations above its mean is around 2.2%. (This assumes the random variable is normal, but you'll get similar results for other well-behaved random variables, and besides, the winrate over a large number of hands should be approximately random due to the law of large numbers.)

Therefore, if we know that the number of regs is at least 5,000, then it wouldn't at all be surprising if the luckiest 100 of them are winning at least 4ptbb/100 over 160k hands. If the number of regs is, say, 500, then the existence of a hundred players winning at 4ptbb/100 over 160k hands would indeed be surprising.

If you give me relevant numbers to check I can do similar analysis for them.

Edit: by the way, it appears to me like there are few people involved in the discussions here with solid professional background in statistics (I mean Masters-level or higher). No offense meant: obviously this is something one has to learn. I think such background is really important for the purpose of discussions with pokerstars, as well as to understand for ourselves what the data actually means. I think the topic needs a dedicated statistician. But if no one steps up, I have some relevant background (Ph.D. in math) and would love to lend a hand. I'll be available in this thread and in PMs, and when the player reps get selected for the pokerstars meeting, I'll contact them directly to ask if they want some one-on-one discussions about statistics of poker vis-a-vis rake. My offer is standing to just about anyone interested.

Sorry i respect your posts but it sounds like you don't have much experience grinding SSPLO to make this comment. The vast majority of hands are played by the group <Supernova which is when you will see rewards at 30-35% rakeback. I would imagine the true average cash paid back via fpps is closer to 15-20%.

You also have to consider you have a much different mindset when you are actually grinding out a profit at the tables vs an fpp pro. The first looks forward to playing and actually enjoys it whereas the latter likely thinks about quitting all the time.

This data has great potential, although you need to get it into excel ASAP to be able to perform more meaningful analysis as joeri proposed.

If you need help, find someone with good excel skills to help you out. There are a number of techniques such as grouping you could use to both improve your analysis, as well as make the data way more user friendly for the meeting.

Good luck but I'm fairly sure if you just went to the meeting with the raw data the outcome will not be as positive as you hoped. Raw data allows for lots of "but what about this" discussion to argue your point. Make it user friendly by grouping and displaying certain scenarios with the click of a button.