When we talk about standardizing a variable, we mean that we want to convert the variable to a z-score. If we know how to do a conversion to a z-score, then we can use the z-score to determine information like confidence intervals and probabilities from the tables at the back of the big thick statistical books. So, what is a z-score?

z-score:
A z-score is a unit of measurement that is obtained by subtracting the mean and dividing by the standard deviation. Mathematically, it looks like:

z = (x-mu)/sd,

where z is the z-score we are looking for,
x is a random variable,
mu is the mean of the distribution, and
sd is the standard deviation.

Example:
We have run a 110,537,444 round sim and the results indicate that the flat betting Initial Bet Advantage (IBA), or mean is +.213% with a St. Err. of 0.0150915%. To find the z-score at a true count of +3, when the advantage at a TC of +3 is 1.373% and the Standard Deviation per Round (per unit bet) at +3 is 1.1377, we:

Step 1) Convert Standard Error to Standard Deviation

We know that SE = SD/sqrt(n). Therefore SD = SE * sqrt(n) = 0.000150915 * sqrt(110,537,444) = 1.586671851105

Step 2) Plug the converted numbers into the z-score equation as follows:

z = (1.373 - (+0.213))/1.586671851105 ~ 0.73109

In other words, the z-score of 0.73109 in this example indicates that the +3 True Count percentage of 1.373% is 0.73109 Standard Deviations from the mean of +0.213%. The Standard Deviation of 1.1377 has been include in the problem to illustrate that one should not use the Standard Deviation per Round (per unit bet) to determine the z-score in this type of example.