Well done sir!

Making conclusive statements based on a sample size of tournaments from one years WSOP.. hmm... yeah, seems intelligent. 600% ROI is sustainable.

ben lamb played only 4 events? defending POY? that cant be right

yes it is. he prefers cash just like the vast majority of all pros. and he only played like 6 events last year that's why his POY was so sick

shouldnt there be a movement to restructure tourney payouts to be less fiscally variant. i.e. pay out 20%, take a bunch of 1st-3rd money to the whole final table.

OP, anyway you can post this somewhere as a spreadsheet rather than an image? I would be interested in stats like avg cash %, avg buy-in, etc.

Entry fees were 10% in the $1,500's, and was less as buy-in amount rose. I think the $10Ks were 6% (?). Anyway, it means Harrah's raked something between 2nd and 3rd place money in every bracelet event.

If the payouts were stretched to top 20% finishers, intuitively it seems like Harrahs would "win" lots more than 1st place money.

If you actually have a 30% ROI, you can certainly make a living playing tournament poker....you just need to swap/buy/sell enough pieces with other 30% ROI players to sufficiently reduce you variance. If all these players had swapped evenly 88-ways, they would have each made 35k over 6 weeks.

Not saying that the real numbers are any higher, but fwiw this isn't anywhere close to long term data.

1. This is such a small sample size of data it's essentially still almost completely meaningless. It's what, somewhere in the neighborhood of 1500 tournaments total? The standard deviation for a given player's ROI over an infinite set of 1500 tournament samples is absolutely tremendous.

2. The data is heavily biased towards players that play small, tough fields, and play mixed games with fewer fish

3. The data is heavily biased towards players that are going to play tons of events rather than players that are going to have the highest rois in the events that they do play. The draft is such that a slightly losing mixed game player who is gonna grind really hard is a much better draft pick than someone like westmenlo or gboro who are gonna play fewer tourneys and mostly large field nlhe stuff. It's a bit misleading to title this results for "Top Players," even though there is a lot of overlap with this list and a list of top players.

4. Several players on this list multi table events or blind out of events without playing a hand and during the series play to maximize bracelet equity rather than ROI.


Again, lol sample size, but kudos for the effort. Doing this year after year for a decade might tell us something meaningful, and we can also extrapolate some reasonable estimates from cash %s and things like that which the data can demonstrate much more conclusively than ROI.

Should a larger sample size significantly change these results? If so, why?

I'd like to amend my question since it doesn't ask what I want to know. (too late to edit)

Instead of one year, take 10 years of WSOP performance. I don't see any reason why the results of that much larger sample size, and the ones in this chart should be markedly different.

Sure, if you include specialists playing only their specialties, results will be "better", but that is a biased sample.

This chart seems to be a totally random, average sampling and although it is small, it could accurately reflect average results obtained by an average group of very skilled MMT players.

1500 tournaments is a small sample size? How many tournaments would you expect a player to enter over the course of their entire career?

I would be interested in seeing the confidence interval around the win rate presented here if all of these individual entries were treated as being played by a single player (particularly if the entries paid and buy-ins won were normalized to # of buy-ins....i.e. rather than saying Mizrachi won $1.45 M in the $50k, say he won 29 buy-ins).

i doubt any player plays enough live tournies over thier lifetime to see thier true ROI.

Calculating ROI by gross cashes for players that play $1k buyins and $50k buyins in the same year really skews results towards the big buyins.

You should use relative ROI, I.e. # of buyins cashed for.