Contrary to other answers, I'd argue that you can say something about Bolts abilities given the available data. First of all, let's narrow your question. You are asking about fastest human, but since there is a difference in the distributions of running speeds for men and woman, where the best woman runners woman seem to be slightly slower then the best man runners, we should focus on men runners. To get some data, we can look at the best year performances on 100 run from the last 45 years. There are several things to notice about this data:
- Those are the best running times, so they do not tell us about abilities of all humans, but about the minimal achieved speeds.
- We assume that this data reflects sample of the best runners in the world. While it might have happened that there were even better runners that did not participate in the championships, this assumption seems to be fairly reasonable.
First, let's discuss how not to analyze this data. You could notice that if we plot the running times against time, we would observe a strong linear relationship.
This could lead you to using linear regression to forecast how much better runners we could observe in the next years. This however would be a very bad idea, that would inevitable lead you to conclusion that in approximately two thousand years humans would be able to run 100 meters in zero seconds, and after that they would start achieving the negative running times! This is obviously absurd, as we can imagine that there is some kind of biological and physical limit of our capacities, that is unknown to us.
How could you analyze this data? First, notice that we are dealing with data about minimal values, so we should use appropriate model for such data. This leads us to considering extreme value theory models (see e.g. An Introduction to Statistical Modeling of Extreme Values book by Stuart Coles). You can assume for this data generalized extreme value distribution (GEV). If $Y = \max(X_1,X_2,\dots,X_n)$ where $X_1,X_2,\dots,X_n$ are independent and identically distributed random variables, then $Y_i$'s follow a GEV distribution. If you are interested in modeling minimas, then if $Z_1,Z_2,\dots,Z_k$ are samples of minimas, then $-Z_i$'s follow a GEV distribution for minimas. So we can fit GEV distribution to the running speeds data, what leads to pretty nice fit (see below).
If you look at at the cumulative distribution suggested by the model, you'll notice that the best running time by Usain Bolt is in the lowest $1\%$ tail of the distribution. So if we stick to this data, and this toy-example analysis, we would conclude that the much smaller running times are unlikely (but obviously, possible). The obvious problem with this analysis is that ignores the fact that we saw year-to-year improvements of the best running times. This gets us back to the problem described in the first part of the answer, i.e. that assuming a regression model in here is risky. Another thing that could be improved is that we could use Bayesian approach and assume informative prior that would account for some out-of-data knowledge about the physiologically possible running times, that might not yet been observed (but, as far as I know, this is unknown at present moment). Finally, similar extreme value theory was already used in sports research, e.g. by Einmahl and Magnus (2008) in the Records in Athletics Through Extreme-Value Theory paper.
You could protest that you didn't ask about probability of the faster running time, but about probability of observing faster runner. Unfortunately, here we can't do much since we don't know what is the probability that a runner will become a professional athlete and the recorded running times will be available for him. This doesn't happen at random and there is lots of factors contributing to the fact that some runners become professional athletes and some don't (or even that someone likes running and runs at all). For this, we would have to have a detailed population-wide data on runners, moreover since you are asking about the extremes of the distribution, the data would have to be very large. So on this, I agree with the other answers.