When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community

Highlights

•	This research describes how an online community with interest in professional soccer evaluates players’ market values.
•	The community's market-value estimates are excellent predictors of actual transfer fees.
•	The community evaluations can largely be predicted using an econometric model that contains two blocks of determinants.
•	Blocks: measures that are directly related to players’ talent and variables that result from judgments by external sources.
•	Organizing variables into these two blocks provides a more differentiated look at the popularity of players.

Abstract

Evaluating the value of athletes to determine whether they should be transferred from one club to another has become a major challenge for managers of sports teams. In the context of German soccer, aficionados have built a large online community that evaluates professional soccer players’ market values. The community has become the main source for reporting market values in the media and has a strong impact on sports economy: it is used in real market transactions and wage negotiations, indicating the power of crowd wisdom in the sports management context.

This research describes the evaluation process of the community, investigates the accuracy of its estimated market values, and shows which attributes are most important for market-value evaluations. After demonstrating that the community's market-value estimates can predict actual transfer fees, we show that community evaluations can largely be explained by an econometric model that contains two blocks of determinants: variables that are directly related to players’ talent and variables that result from judgments by external sources (e.g., journalists). Reorganizing variables that were used in previous studies into two blocks (talent vs. external determinants) provides a more differentiated look at the popularity of players than recent literature on the “superstar phenomenon.”

Keywords:

• Crowd valuation
• Market value
• Human capital
• Professional athletes
• Soccer

Notes

1 Tel.: [phone].

2 Tel.: [phone].

1 Large companies such as Google, General Electric, Siemens, and Chrysler – to name just a few – have used prediction markets. These companies aimed to harvest information provided by “the crowd” (their employees), e.g., estimates of the number of Gmail users in the future (Google). However, Cowgill, Wolfers, and Zitzewitz (2009) have found that predictions of market results are prone to numerous biases. For example, new employees are overly optimistic or employees working closely together exhibit highly correlated views and estimates.

2 To integrate the possible role of the judges on transfermarkt.de, we did set up a two-level regression model including judge experience (number of posts of the respective judge) as a judge-level explanatory variable. However, we did not get any additional insights, as the judges had neither a significant impact on explaining market value nor could we find any significant cross-level interactions.

Steffen Herm is a professor of marketing at the Hochschule für Technik und Wirtschaft Berlin. He holds a Ph.D. and a postdoctoral degree in Marketing from the Technische Universtät (TU) Berlin. His research, related to innovation, product design, entrepreneurship, branding, and consumer behavior is published in journals as the Journal of Retailing and the Journal of Product Innovation Management.

Hans-Markus Callsen-Bracker is an project manager Projektträger Jülich and was an assistant professor of finance at the TU Berlin. His research is related to pricing and risk management.

Henning Kreis is a assistant professor for market communications at Freie Universität Berlin. He holds a Ph.D. in Marketing from Humboldt-Universität zu Berlin. His research, related to word-of-mouth communication, customer loyalty and quantitative methods for data analysis is published in journals as the Journal of Business Market Management and Marketing ZFP.