CasinoCityTimes.com

Gurus
News
Newsletter
Author Home Author Archives Author Books Send to a Friend Search Articles Subscribe
Stay informed with the
NEW Casino City Times newsletter!
Newsletter Signup
Stay informed with the
NEW Casino City Times newsletter!
Articles in this Series
Best of Donald Catlin

Gaming Guru

author's picture
 

Ruin Again: Part 1 - Feedback

7 August 2011

In my June 2011 article I addressed a ruin question about Blackjack from one of my readers.  In it I proposed a simple scheme that used a one-dimensional random walk model that had the same expected value as Blackjack.  Well, did I get feedback!  

 

Several readers, including my good friend and colleague Stewart Ethier from the University of Utah, wrote and told me that there was a better way to approximate Blackjack using a one-dimensional random walk.  Indeed there is.  One should take into account not only the expected value but the variance as well.  So in this and the two following articles I am going to explain how this is done.

If X represents any random variable, E(X) represents the expected value (or mean) of X where E is the expectation operator.  Let us set

M0 = E(X)                 (1)

The variance of X is given by

V0 = E((X – M0)2)      (2)

The reason for the 0 subscripts will become clear as we proceed.  Using (2) we can write

V0 = E(X2 – 2M0X + M02)

and using the linearity of E this can be rewritten as

V0 = E(X2) – 2MoE(X) + E(M02)

Recalling (1) we finally have

V0 = E(X2) – M02           (3)

Next I am going to define a one dimensional random walk that has a step size b (for what will be interpreted later as bet size) given by

b = sqrt(V0 + M02) = sqrt(E(X2)         (4)

where sqrt is the square root function and the second equality is given by (3).  The winning and losing probabilities, respectively p and q, are given by

p = ½ + M0/2b                (5)

and

q = ½ - M0/2b              (6)

Let’s calculate the mean M of this random walk:

M = pb + q(-b) = (p – q)b = (2M0/2b)b = M0

The random walk has the same mean as our original random variable X so we can now ignore the subscript 0 on M.  To calculate the variance V of our random walk, we use formula (3) as follows:

V = pb2  + q(-b)2 – M2

so since p + q = 1

V = b2 – M2 = V0

The second equality follows from (4).  Hence the variance of our random walk is exactly the same as the variance of our original random variable X ( no more subscripts).

Next month I’ll derive the ruin formula for a one-dimensional random walk and put it in a form that will be useful for our Blackjack question and the following month I’ll put it all together for you.  Thanks to all the folks who wrote to me.  See you next month.


Don Catlin can be reached at 711cat@comcast.net

Donald Catlin

Don Catlin is a retired professor of mathematics and statistics from the University of Massachusetts. His original research area was in Stochastic Estimation applied to submarine navigation problems but has spent the last several years doing gaming analysis for gaming developers and writing about gaming. He is the author of The Lottery Book, The Truth Behind the Numbers published by Bonus books.

Books by Donald Catlin:

Lottery Book: The Truth Behind the Numbers
Donald Catlin
Don Catlin is a retired professor of mathematics and statistics from the University of Massachusetts. His original research area was in Stochastic Estimation applied to submarine navigation problems but has spent the last several years doing gaming analysis for gaming developers and writing about gaming. He is the author of The Lottery Book, The Truth Behind the Numbers published by Bonus books.

Books by Donald Catlin:

Lottery Book: The Truth Behind the Numbers