01999nas a2200181   4500008004100000245011100041210006900152300001100221490000700232520140700239100002101646700001801667700002101685700002301706700002001729700002101749856004701770       2019                        eng d00aPlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach0 aPlayeRank datadriven performance evaluation and player ranking i  a1–270 v103 aThe problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players’ evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank—i.e. searching players and player versatility—showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.1 aPappalardo, Luca1 aCintia, Paolo1 aFerragina, Paolo1 aMassucco, Emanuele1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://dl.acm.org/doi/abs/10.1145/3343172