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One of the most important and challenging problems in football is predictingfuture player performance when transferred to another club within and betweendifferent leagues. In addition to being the most valuable prediction a teammakes, it is also the most complex analytics task to perform as it needs totake into consideration: a) differences in playing style between the player'scurrent team and target team, b) differences in style and ability of otherplayers on each team, c) differences in league quality and style, and d) therole the player is desired to play. In this paper, we present a method whichaddresses these issues and enables us to make accurate predictions of futureperformance. Our Transfer Portal model utilizes a personalized neural networkaccounting for both stylistic and ability level input representations forplayers, teams, and leagues to simulate future player performance at any chosenclub. Furthermore, we use a Bayesian updating framework to dynamically modifyplayer and team representations over time which enables us to generatepredictions for rising stars with small amounts of data.

Transfer Portal: Accurately Forecasting the Impact of a Player Transfer in Soccer