A strategy to predict association football players' passing skills (2017)
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 do es so by using event-level data from the Spanis hand 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 at tempt 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 performin the Spanish league, taking into account that teammates and rivals' abilities are different in the latter.