No, it isn't. This isn't him saying, "It won't happen to me" just because, it's that it doesn't apply because he's taking the necessary precautions. He's doing everything he can- and other people won't affect HIS rate of failure.
Yes, they will, and that's what makes him failure-prone.
When outside influences do come along that affect his expected outcome, he will be disrupted instead of scientifically understanding the situation, and his role as an individual who is not the center of the universe.
When you know there is sampling error (luck) it makes sense to blend a little bit of the distribution of the group in with each individual.
For instance, if you look at baseball hitters for a season ordered by batting average, probably the guy at the top of the list got lucky (sampling error) and the guy at the bottom of the list had bad luck. The real batting average is a hidden variable that we can only see through sampling.
A good estimator of an individual's batting average can use the group distribution as a prior against the individual distribution as an observation -- this regresses the individuals back towards the mean, which is a realistic way to perceive uncertainty.
As a baseball stat head, I can extend your analogy.
In late 1990s, when Quinton McCracken first published DIPS theory, it was postulated that pitchers had zero control over their "luck based" factors (BABIP, in this case; expanded to include LD%, LOB%, HR/FB%, etc.)
Now there exists enough data to identify outliers, to whom the theory of DIPS (Defense Independent Pitching Stats) doesn't apply. Matt Cain, for example, is acknowledged to outperform his DIPS.
No matter what the statistics suggest, people will believe they're the Matt Cain. And some will be. By definition, they're the outliers that our statistical models do not account for.
