PlayeRank is a data-driven algorithm that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. Playerank is designed to work with soccer-logs, in which a match consists of a sequence of events encoded as a tuple: (id, type, position, timestamp), where id is the identifer of the player that originated/refers to this event, type is the event type (i.e., passes, shots, goals, tackles, etc.), position and timestamp denote the spatio-temporal coordinates of the event over the soccer field. PlayeRank assumes that soccer-logs are stored into a database, which is updated with new events after each soccer match. An exhaustive description of PlayeRank framework is available in this paper: Pappalardo, Luca, Cintia, Paolo, Ferragina, Paolo, Massucco, Emanuele, Pedreschi, Dino & Giannotti, Fosca (2019) PlayeRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach. ACM Transactions on Intelligent Systems and Technologies 10(5), DOI:https://doi.org/10.1145/3343172
PlayeRank
Cintia P;Pappalardo L
2019
Abstract
PlayeRank is a data-driven algorithm that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. Playerank is designed to work with soccer-logs, in which a match consists of a sequence of events encoded as a tuple: (id, type, position, timestamp), where id is the identifer of the player that originated/refers to this event, type is the event type (i.e., passes, shots, goals, tackles, etc.), position and timestamp denote the spatio-temporal coordinates of the event over the soccer field. PlayeRank assumes that soccer-logs are stored into a database, which is updated with new events after each soccer match. An exhaustive description of PlayeRank framework is available in this paper: Pappalardo, Luca, Cintia, Paolo, Ferragina, Paolo, Massucco, Emanuele, Pedreschi, Dino & Giannotti, Fosca (2019) PlayeRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach. ACM Transactions on Intelligent Systems and Technologies 10(5), DOI:https://doi.org/10.1145/3343172I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
