  One of the most important and challenging problems in football is predicting
future player performance when transferred to another club within and between
different leagues. In addition to being the most valuable prediction a team
makes, it is also the most complex analytics task to perform as it needs to
take into consideration: a) differences in playing style between the player's
current team and target team, b) differences in style and ability of other
players on each team, c) differences in league quality and style, and d) the
role the player is desired to play. In this paper, we present a method which
addresses these issues and enables us to make accurate predictions of future
performance. Our Transfer Portal model utilizes a personalized neural network
accounting for both stylistic and ability level input representations for
players, teams, and leagues to simulate future player performance at any chosen
club. Furthermore, we use a Bayesian updating framework to dynamically modify
player and team representations over time which enables us to generate
predictions for rising stars with small amounts of data.
