# Category Archives: Life as a Poker Pro by the Numbers

## Life as a HU SnG Pro by the Numbers (It’s Awesome… If You’re Good and Have Rakeback)

In previous posts, I looked at how variance affects players who play large-field MTTssmaller-field MTTs, NLHE 6-max cash, and 6-man and 9-man STTs, and I found quite a bit of difference. Next up: HU SnGs. (Still to come: HU NLHE, FR NLHE, HU PLO, 6-max PLO, HU LHE, 6-max LHE, and FR LHE. I’m going to be quite busy for at least the next couple weeks, so please don’t hold your breath. Follow me on twitter if you’d like to know when these posts go up.)

HU SnGs are quite simple statistically, so I will make exactly one assumption: rake that is 1/22 of the rake-free buy-in. This is the standard rake for turbos on Stars and FTP at most stakes (e.g. $110+$5), so everything that I say will be exact for those games, but if you play games with a different rake:buy-in ratio, the numbers will be a bit different. To get most of the results, I won’t even bother to assume a normal distribution; I’ll just use the binomial distribution, which is an exact statistical representation of a HU SnG. (This won’t actually change the numbers at all after rounding, but it just requires a bit of extra algebra from me, and it’ll appease some of the statistical purists in the audience.)

## Life as a NLHE 6-max Cash Game Pro by the Numbers (It Ain’t Too Bad)

(Update 3/8: I originally completely forgot to talk about what happens with different winrates. I added this analysis to the bottom of the post.)

In two of my previous posts (1 2), I crunched some numbers to show that the variance in large-field MTTs is pretty damn crazy. Now I’m going to turn my attention to NLHE 6-max cash games. (Sorry for the delay. I could claim to have been busy, but mostly I’ve just been lazy.)

This is actually pretty easy thanks to the statistician’s best friend, the central limit theorem. For sample sizes of at least a few thousand hands, you can just take your standard deviation and winrate and use the normal approximation. (For sample sizes of less than a few thousand hands…. Who cares?) This makes the math really easy, and in a way, it’s responsible for the relatively cushy lifestyle of cash players–The normal distribution is a lot cleaner than the distribution that I found for MTT players. (Proof that the distribution is normal)

(Another consequence of this mathematical convenience is the fact that some other people have done this analysis already. While my posts on MTTs were, as far as I know, the first honest attempt at tackling that problem correctly, this post will mostly just explain what’s already known to the nerdy contingent of the poker world and anyone else with a basic understanding of statistics. I’m just bothering to share this information with our less nerdy brethren in a way that I feel is reasonably clear. To that end, I’ll mostly just leave out any explanations, but suffice it to say that all of this comes from very basic facts about the normal distribution. However, I think everybody who plays poker should be able to do some basic statistics, so I might make a tutorial explaining where these numbers come from at some point.)

To see how that works out in practice, let’s look at a basic example. Take a solid 6-max NLHE grinder with a 5 bb/100 win rate (I’m going to use bb/100, not PTBB/100 in this post. An unfortunate tradition ported from LHE leads many people–including PTR, PT3, and sometimes me–to call a BB or a PTBB twice a bb.) with a fairly typical standard deviation of 90 bb/100. What happens if 10,000 clones of this guy play 50,000 hands each? Well, this does:

## Life as an Online MTT Pro II: The Numbers Are Back, and They’re Out for Blood

(This is a follow-up to this post. You probably want to read that first if you haven’t already.)

My previous post about the variance in MTTs got some interest and plenty of criticism, so I thought I’d follow up with a (slightly) less sloppy post that considered some criticism. In future posts, I plan to look at various forms of cash games, STTs, and DoNs. (If you’d like to share your data for any of these things, please get in contact with me.)

First of all, I should clarify something: Some people are definitely better off playing large-field MTTs than other games. I’ll make an argument below that almost no small/mid-stakes players fit into this category in the current climate. For mid/high-stakes player: If you’re insanely good at MTTs and you’re down to put in lots of volume, then play them. If you really enjoy large-field MTTs and you’re willing and able to deal with their ridiculous variance, then play them. If you’ve got a really sweet backing deal, then play them. If for whatever other reason you think you’d like to play them, I won’t pretend to know what’s best for you. But, make sure you seriously consider the extreme variance that’s involved. Before this, I haven’t seen anyone present this information correctly. Thus these posts. (I have seen people use the normal approximation to look into this, but as I showed here, that method doesn’t actually work.)

Anyway, there’s lots of fun stuff that I didn’t do with Shaun’s data that I’d like to do with my shiny new data set (see the section at the bottom for more about this sexy piece of data). For example, varying buy-in sizes obviously increases variance. For Shaun’s data, I looked at buy-ins between $55 and$216, i.e. with a max buy-in about 4x his min buy-in. But what if we don’t vary the buy-ins at all? Here’s the data for that:

## Life as an Online MTT Pro by the Numbers (It’s Hard)

(After you read this post, you might want to check out my follow-up to it here.)

I tell a lot of people not to play large-field online MTTs for a living. I’ve always thought that the variance is just way too high for most professionals to trust their livelihood (and sanity) to large-field MTTs instead of cash, smaller-field MTTs, or STTs. But, admittedly, I’ve given this advice without any direct evidence to back it up. I’ve been meaning for a while to see what the numbers say, and this post will be a tentative first step.

Ideally, what I’d like to do is do a nice controlled study where I pick a few representative players based on past results and use their results over the next few months as my data. (Alternatively, I could take the results of one of the large backing groups. If anyone who backs 20+ people would be down to share some information, let me know.) But, that requires more motivation than I’ve been able to muster, so I decided to do a much rougher study: I grabbed Shaun “SFD” Deeb’s tourney results from OPR (with Shaun’s permission) and played around for a few hours. Here’s what I found: