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
PublisherIEEE Computer Society
Pages167-172
Number of pages6
ISBN (Electronic)9781509054725
DOIs
Publication statusPublished - 30 Jan 2017
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016

Other

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

Fingerprint

Fans
Costs

Keywords

  • Data mining
  • Football
  • Performance measurement

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this

Stanojevic, R., & Gyarmati, L. (2017). Towards Data-Driven Football Player Assessment. In Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 (pp. 167-172). [7836662] IEEE Computer Society. https://doi.org/10.1109/ICDMW.2016.0031

Towards Data-Driven Football Player Assessment. / Stanojevic, Rade; Gyarmati, Laszlo.

Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. IEEE Computer Society, 2017. p. 167-172 7836662.

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

Stanojevic, R & Gyarmati, L 2017, Towards Data-Driven Football Player Assessment. in Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016., 7836662, IEEE Computer Society, pp. 167-172, 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016, Barcelona, Spain, 12/12/16. https://doi.org/10.1109/ICDMW.2016.0031
Stanojevic R, Gyarmati L. Towards Data-Driven Football Player Assessment. In Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. IEEE Computer Society. 2017. p. 167-172. 7836662 https://doi.org/10.1109/ICDMW.2016.0031
Stanojevic, Rade ; Gyarmati, Laszlo. / Towards Data-Driven Football Player Assessment. Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. IEEE Computer Society, 2017. pp. 167-172
@inproceedings{ab6f1ef151a74287a67b097244e16904,
title = "Towards Data-Driven Football Player Assessment",
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.",
keywords = "Data mining, Football, Performance measurement",
author = "Rade Stanojevic and Laszlo Gyarmati",
year = "2017",
month = "1",
day = "30",
doi = "10.1109/ICDMW.2016.0031",
language = "English",
pages = "167--172",
booktitle = "Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Towards Data-Driven Football Player Assessment

AU - Stanojevic, Rade

AU - Gyarmati, Laszlo

PY - 2017/1/30

Y1 - 2017/1/30

N2 - 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.

AB - 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.

KW - Data mining

KW - Football

KW - Performance measurement

UR - http://www.scopus.com/inward/record.url?scp=85015173732&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015173732&partnerID=8YFLogxK

U2 - 10.1109/ICDMW.2016.0031

DO - 10.1109/ICDMW.2016.0031

M3 - Conference contribution

AN - SCOPUS:85015173732

SP - 167

EP - 172

BT - Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016

PB - IEEE Computer Society

ER -