(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: