Distinguishing Between Roles of Football Players in Play-by-play Match Event Data

September 13, 2018 ยท Declared Dead ยท ๐Ÿ› MLSA@PKDD/ECML

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Authors Bart Aalbers, Jan Van Haaren arXiv ID 1809.05173 Category stat.ML: Machine Learning (Stat) Cross-listed cs.LG, stat.AP Citations 24 Venue MLSA@PKDD/ECML Last Checked 4 months ago
Abstract
Over the last few decades, the player recruitment process in professional football has evolved into a multi-billion industry and has thus become of vital importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and advanced statistics. However, the question whether a given player would fit the team's playing style often still remains unanswered. In this paper, we aim to bridge that gap by proposing a set of 21 player roles and introducing a method for automatically identifying the most applicable roles for each player from play-by-play event data collected during matches.
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