On gambler's fallacy and the nature of probability:
Most events are independent events, thus say there is 50% probability that a Russian roulette will land on a red space (ignoring the green space for now for simplicity)
Gambler's fallacy says that if you keep on landing on a sequence of blacks, then the probability of landing on red will increase in order to balance out
The reason why this is a fallacy has to do with the independence of the events:
Because each event is independent, the probability of landing at e.g. 50 regardless of what happens before does not change. This also means that you can land on 50 again
If Gambler's fallacy is to be true, then it means the events are dependent in the following way:
Sorry typo, I mean Roulette, not Russian roulette
Let $x_i$ be a random variable on what number will be landed on the Roulette after the ith game. Then Gambler's fallacy is true if for all $i,j$:
$$\text{Pr} (x_i|x_j) < \text{Pr} (x_i)$$
This means, the probability of landing in the same place again will diminish over time, and hence by the conservation of probability $\int \text{Pr}(x) dx = 1$ the complementary outcomes has to increase
The most extreme of these cases coincides with the typical ball without replacement problem, the case where $\text{Pr}(x_i|x_j) = 0$ thus an outcome will not be landed twice
Now as for the nature of probability. What probability told us is given any n trials, how likely is for some event x to occur (Or for the Bayesian interpretation, first make a guess on how much you put your belief that x happens, and then refine your guess iteratively as more evidence became available).
By itself, $\text{Pr}(x)$ does not give you any information on whether the event will happen on the next trial, or the next next trial and so on. This is how it captures one aspect of unpredictability
If you want to say predict how likely that $x$ happens after say, n trials, then what you need is really all the conditional probabilities:
$$\text{Pr}(x|y_1,...,y_n)$$
If that list that follows after the "given" is ordered, then it tell us something about the system. A computation of all of these thus gives you some idea on the ordering that a sequence of events happens
Therefore, if a process is deterministic, it basically means something like this:
$$\text{Pr}(x_0) = \delta_{0i}, \text{Pr}(x_1|x_i) = \delta_{0i}, \text{Pr} (x_2|x_i,x_j) = \delta_{0i}\delta_{1j}, ...$$
Put it in another way, let $\mathbf{x}$ be an ordered sequence of events. Let $x_0 \in \mathbf{x}$. Then for every $x_0$, known as the initial condition, there is a unique $\mathbf{x}$ such that $\text{Pr}(\mathbf{x})=1$ and all the rest are zero