Article open access publication

Beyond crowd judgments: Data-driven estimation of market value in association football

European Journal of Operational Research, Elsevier, ISSN 0377-2217

Volume 263, 2, 2017

DOI:10.1016/j.ejor.2017.05.005, Dimensions: pub.1085393589,

Authors

* Corresponding author

Affiliations

Organisations

  1. (1) University of Copenhagen, grid.5254.6, KU
  2. (2) University of Liechtenstein, grid.445905.9

Countries

Liechtenstein

Denmark

Continents

Europe

Description

Association football is a popular sport, but it is also a big business. From a managerial perspective, the most important decisions that team managers make concern player transfers, so issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees—that is, prices that could be paid for a player on the football market—so they play an important role in transfer negotiations. These values have traditionally been estimated by football experts, but crowdsourcing has emerged as an increasingly popular approach to estimating market value. While researchers have found high correlations between crowdsourced market values and actual transfer fees, the process behind crowd judgments is not transparent, crowd estimates are not replicable, and they are updated infrequently because they require the participation of many users. Data analytics may thus provide a sound alternative or a complementary approach to crowd-based estimations of market value. Based on a unique data set that is comprised of 4217 players from the top five European leagues and a period of six playing seasons, we estimate players’ market values using multilevel regression analysis. The regression results suggest that data-driven estimates of market value can overcome several of the crowd's practical limitations while producing comparably accurate numbers. Our results have important implications for football managers and scouts, as data analytics facilitates precise, objective, and reliable estimates of market value that can be updated at any time.

Research Categories

Main Subject Area

Fields of Research

Links & Metrics

NORA University Profiles

University of Copenhagen

IT University of Copenhagen

Danish Open Access Indicator

2017: Realized

Research area: Science & Technology

Danish Bibliometrics Indicator

2017: Level 1

Research area: Science & Technology

Dimensions Citation Indicators

Times Cited: 20

Field Citation Ratio (FCR): 16.04

Open Access Info

Hybrid