Towards Data-Driven Football Player Assessment

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Understanding the value of a football player is a challenging problem. Player valuation is not only critical for scouting, bidding and negotiation processes but also attracts a large media and fan interest. Due to the complexities which arise from the fact that player pool is distributed over hundreds of different leagues and many different playing positions, many clubs hire domain experts (often retired professional players) in order to evaluate the value of potential players. We argue that such human-based scouting has several drawbacks including high cost, inability to scale to thousands of active players and inevitable subjective biases. In this paper we present a methodology for data-driven player market value estimation which tackles these drawbacks. To examine the quality of the proposed methodology and demonstrate that our data-driven valuation outperforms widely used transfermarkt.com market value estimates in predicting the team performance.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherIEEE Computer Society
Pages167-172
Number of pages6
ISBN (Electronic)9781509054725
DOIs
Publication statusPublished - 2 Jul 2016
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume0
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1615/12/16

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Keywords

  • Data mining
  • Football
  • Performance measurement

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this

Stanojevic, R., & Gyarmati, L. (2016). Towards Data-Driven Football Player Assessment. In C. Domeniconi, F. Gullo, F. Bonchi, F. Bonchi, J. Domingo-Ferrer, R. Baeza-Yates, R. Baeza-Yates, R. Baeza-Yates, Z-H. Zhou, & X. Wu (Eds.), Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 (pp. 167-172). [7836662] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2016.0031