Distinguishing Between Roles of Football Players in Play-by-play Match Event Data
September 13, 2018 ยท Declared Dead ยท ๐ MLSA@PKDD/ECML
"No code URL or promise found in abstract"
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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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