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**this is Xposted from Poker Theory....it was recommended I post it here.
Sorry if this is repeating old posts, but I did search the topic and found nothing. I'm in an intro statistics course and we were given an assignment to build a statistical model. (ie get data, run a regression, analyse the R^2, check for multicollinearity etc....) I thought it might be fun to try to build a model on what makes a successful low limit hold'em player. Here's what I was thinking about using for my variables: The concensus seems to be tight-aggressive play is the best way to win long-term. I thought to test this I would use my poker tracker data and examine all players I have data for over 500 hands (too few? - I think so, but it's hard to get more)... Anyway, I thought I would use BB/100 as the dependent variable, and VP$IP and Agression Factor as independent variables, and see what excel came up with. Right now I don't have enough data to make a meaningful regression (I am data-mining 1-2 as I type this). My questions are: 1.) Is it feasible to create a statistical model like this? (ie are there too many random variables - luck etc - to create a model like this?) 2.) What would you recommend using as independent and dependent variables? 3.) What do you think the minimum amount of observed hands should be for a player to be included in the model? Any advice you could provide would be greatly, GREATLY appreciated. |
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