Okay, write it up (sounds like you've done it mostly anyway) and I'll take a look ... I'm interested how that works, if nothing else. Doesn't have to be flash, just the meat.
And your basic contention is that smaller miners are better off in bigger pools?
It's the other way around. For tiny miners it makes absolutely no difference what is the size of their pool. It's the bigger miners that are better off in a bigger pool.And your basic contention is that smaller miners are better off in bigger pools?
Let's say a miner submits a share at a random point in time. Let A be the event the the share was submitted for a given round, and X the eventual number of shares in this round. Then $P(X=x) \propto p(1-p)^{x-1}$ (exponential distribution) and $P(A|X=x) \propto x$ (the more shares in a round, the higher its chance to include a given share). By Bayes' formula, $P(X=x|A) \propto P(X=x)P(A|X=x) = p(1-p)^{x-1}x$. Summing for x>=1 gives $P(X=x|A) = p^2(1-p)^{x-1}x$.
The payout received for this share, in units of block reward, is 1/x. So the expected payout is $\sum_{x=1}^{\infty}p^2(1-p)^{x-1}x/x=p$, as expected (pun unintended). The expectation of the squared payout is $\sum_{x=1}^{\infty}p^2(1-p)^{x-1}x/x^2=p^2\log p/(p-1)$, so the variance is $p^2(1-p+\log p)/(p-1)$.
As you can see, this does not depend on any additional factors such as the size of the pool. When several shares are submitted, if the miner is tiny then they will be uncorrelated and their variance is additive. As the pool becomes smaller the variance for the miner increases.
If you're not convinced this accurately models the situation (well, it doesn't, but it doesn't deviate in ways that are relevant for this discussion), you can write a simple computer simulation of the dynamics and see that the variance is smaller in a larger pool.
(This would be a big issue if the network is driven towards everybody mining for the same pool.)
It is. There are threads for discussing this problem. 
