Bayesian statistics, like FBM uses, needs way less data than before you can draw valid conclusions. That makes Bayesian statistics ideal for scanning youth players for talents. Sergiño Dest is a great showcase in this regard. According to Ronald Koeman, the current manager of the Dutch national team, one and a half a year ago it was not known what a great talent Dest was. Of course, this is not the case. Quite a number of professionals knew what a talent Dest was, even in the early years.
FBM for instance created this FBM contribution chart for Dest in April 2017, two and a half years ago:
An FBM contribution chart that is quite the same as the chart of his most recent full game:
With the exception of the much higher green line representing the probability that Dest is able to contribute to the transitioning & build up of Ajax. Yet, Dest’s increase in performance in transitioning and build up is very recent. A month earlier his FBM contribution FBM chart still looked like this:
It is very hard for players to do well on transitioning & build up (the green line) in our FBM system. When they do, they often become the talk of the town. So what happened with Dest is not that his talent wasn’t recognized, but that he only very recently started to play at an exceptional level. That is the reason why you use Bayesian statistics, like FBM, to keep track of talented youth players and at the same time combine that information with scouts with a proven track record for being good at predicting the progression youth players make. Then you are not surprised or disappointed when a star player chooses to play for a different national team than for the country where he grew up.