{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:29:47Z","timestamp":1792787387764,"version":"4.5.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T00:00:00Z","timestamp":1568246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"name":"EU project SoBigData RI","award":["#654024"],"award-info":[{"award-number":["#654024"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2019,9,30]]},"abstract":"<jats:p>The 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\u2019 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\u2014i.e. searching players and player versatility\u2014showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.<\/jats:p>","DOI":"10.1145\/3343172","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T08:28:56Z","timestamp":1568363336000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":151,"title":["PlayeRank"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1547-6007","authenticated-orcid":false,"given":"Luca","family":"Pappalardo","sequence":"first","affiliation":[{"name":"ISTI-CNR, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Cintia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Ferragina","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuele","family":"Massucco","sequence":"additional","affiliation":[{"name":"Wyscout, Chiavari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dino","family":"Pedreschi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fosca","family":"Giannotti","sequence":"additional","affiliation":[{"name":"ISTI-CNR, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-017-0513-2"},{"key":"e_1_2_1_2_1","volume-title":"The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball","author":"Baumer Benjamin"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.133"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1740-9713.2018.01146.x"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the MIT Sloan Sports Analytics Conference.","author":"Bornn Luke","year":"2018"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939695"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3192424.3192630"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2015.7344823"},{"key":"e_1_2_1_9_1","unstructured":"Paolo Cintia Salvatore Rinzivillo and Luca Pappalardo. 2015. 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