A Strategy to Predict Association Football Players’ Passing Skills

Transfers are big business in association football. This paper develops a generalized additive mixed model that aids managers in predicting how a football player is expected to perform in a new team. It does so by using event-level data from the Spanish and the Colombian football leagues. Using passes as a performance proxy, the model exploits the richness of the data to account for the difficulty of each pass attempt performed by each player over an entire season. The model estimates are then used to determine how a player transferred from the Colombian league should perform in the Spanish league, taking into account that teammates and rivals’ abilities are different in the latter.