Personnel Selection, whether by sports teams, militaries, universities or corporations, is all about identifying statistical predictors of future performance. How good are these predictors?
Let's take college football as an example. Talent evaluation is difficult, but scouts definitely know something. A five star high school football prospect is almost four times more likely to become an NCAA All-American than a four star prospect. (Graphs from this article; NFL draft order related to HS ranking here.)
Oregon, which finished last season ranked #4 in the country (Rose Bowl and PAC-12 champs), and played in BCS bowls each of the last three seasons, landed only one five star recruit this year. Schools like Alabama (3), Texas (3), USC (3) and Michigan (2) landed significantly more.
What about other kinds of talent? Below is an example from psychometrics applied to 13 year olds.
Horsepower matters: Can psychometrics separate the top .1 percent from the top 1 percent in ability? Yes: SAT-M quartile within top 1 percent predicts future scientific success, even when the testing is done at age 13. The top quartile clearly outperforms the lower quartiles. These results strongly refute the "IQ above 120 doesn't matter" claim, at least in fields like science and engineering; everyone in this sample is above 120 and the top quartile are at the 1 in 10,000 level. The data comes from the Study of Mathematically Precocious Youth (SMPY), a planned 50-year longitudinal study of intellectual talent. ...
Another example: this graph displays upper bounds on probability of graduating with a physics GPA greater than 3.5 (about .5 SD above the average) at Oregon as a function of SAT-M. Note the blue markers are conservative (95 percent confidence level) upper bounds; the central value for the probability at SAT-M > 750 is around 50 percent. The upper bounds were computed to show that the probability for SAT-M below about 600 is close to zero. The red line is the probability of earning an A in calculus-based introductory physics.