  Correctly assessing a scientist's past research impact and potential for
future impact is key in recruitment decisions and other evaluation processes.
While a candidate's future impact is the main concern for these decisions, most
measures only quantify the impact of previous work. Recently, it has been
argued that linear regression models are capable of predicting a scientist's
future impact. By applying that future impact model to 762 careers drawn from
three disciplines: physics, biology, and mathematics, we identify a number of
subtle, but critical, flaws in current models. Specifically, cumulative
non-decreasing measures like the h-index contain intrinsic autocorrelation,
resulting in significant overestimation of their "predictive power". Moreover,
the predictive power of these models depend heavily upon scientists' career
age, producing least accurate estimates for young researchers. Our results
place in doubt the suitability of such models, and indicate further
investigation is required before they can be used in recruiting decisions.
