I assume you are giving each pool a table of random block find times (randomly generate a high-precision round percentile 0% to 100%, turn that percentile into number of individual hashes required using correct math, turn that into times using hashrate), and then are simulating the switching and share percentages earned.

For more interesting real-world factors to model, analysis could be biased with a 'pool hop-in' time delay (like jumping 5 minutes after block find), and a 'wrong pool hop error rate' that reflects what people are seeing with early block-detection methods, when they get incorrect blockfinding because they aren't getting info from the pool that actually found a block, and jump to a false-positive.