Well, no. At all.
If you assume that I'm based only on previous performance of athletes,
you can find a lot of literature that proves that there is no way to improve the model with "external" inputs.
For what you ask to work you should develop a logical model, not a statistical one.
You cannot beat the average number as predictor if you assume normal distribution and that's a mathematical theorem, not a matter of discussion.
Now, there are things, like points per athlete, that can use better predictors, as I tried to insinuate.
You can read the (very interesting) conclusions about Bayesian predictors and Stein paradox here:
http://www-stat.stanford.edu/~ckirby/br ... le1977.pdf
Stein Paradox: a must reading for anyone interested in predicting who will win a season, when based on initial performance of athletes
Summing up, the only thing you can assume to better my prediction (using statistics) is that, in any season, the initial performances of outstanding athletes are out-of-the-ordinary events.
In English, Vettel is not as good as it seems in the beginning, nor Di Resta is as bad as it appears at the start of the season.
You can get better results if you extrapolate under the lines I drawn for the best drivers and a little over those lines for the worst ones.
This conclusion is solidly based on analysis of baseball statistics, that have, literally, centuries of data.
So, in case anyone is interested (which I doubt seriously), what Stein has shown is that you should "contract" a bit the predictors of high scoring and low scoring athletes at the start of a season. You contract them toward the average of all people. How much? Well, there the thing gets a tad complicated.
Anyway, be my guest, the procedure is in the paper and it's not that hard.
Taking a ballpark guess, I'd say that if Stein is right, Vettel should win a little later than what the straight extrapolation I made shows.
So, for the moment, I say that all chances will be gone by... let's say India.
If anyone has a better prediction, I would like to hear it.