No need to invoke Chebychev, you can use the

central limit theorem.

Let

be the results from

tournaments, which we'll assume are independent and identically distributed. The operator

is the expected value and

is the variance. Then obviously:

and

so

, which we'll denote

. Your (empirical) PPT is given by

and your (empirical) ROI is PPT / 6.5. It follows that

and

From the central limit theorem, for suitably large

we have that both PPT and ROI are approximately normal, with the correct mean and variance as given above. So, you now have the asymptotic distribution of both of these statistics.

With this you can answer a few somewhat interesting questions, like "what is the probability that I will be a losing player over my next 1000 games" for various values of x, y, and z.