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Some Issues in Distance Construction for Football Players Performance Data

Akhanli, Serhat Emre; Hennig, Christian

For mapping football (soccer) player information by using multidimensional scaling, and for clustering football players, we construct a distance measure based on players’ performance data. The variables are of mixed type, but the main focus of this paper is how count variables are treated when defining a proper distance measure between players (e.g., top and lower level variables). The distance construction involves four steps: 1) representation , 2) transformation, 3) standardisation, 4) variable weighting. Several distance measures are discussed in terms of how well they match the interpretation of distance and similarity in the application of interest, with a focus on comparing Aitchison and Manhattan distance for variables giving percentage compositions. Preliminary outcomes of multidimensional scaling and clustering are shown.

Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator DOI: 10.5445/KSP/1000058749/09
ISSN: 2363-9881
URN: urn:nbn:de:swb:90-669246
KITopen ID: 1000066924
Erschienen in Archives of Data Science, Series A (Online First)
Band 2
Heft 1
Seiten 17 S. online
Lizenz CC BY-SA 4.0: Creative Commons Namensnennung – Weitergabe unter gleichen Bedingungen 4.0 International
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