This article tries to uncover the drivers of soccer players’ market value in the five major European soccer leagues taking into account model uncertainty (variable selection) in a framework with 35 billion potential models. For this purpose, we use a hedonic regression framework and implement Bayesian model averaging (BMA) through Markov chain Monte Carlo model composition (MC3). To deal with endogeneity issues, instrumental variable Bayesian model averaging (IVBMA) is implemented as well. We find very strong, and robust evidence, that the most important value drivers are player’s performance, participation in the national team (senior and under-21), age, and age squared.
AndersonT. W. (1951). Estimating linear restrictions on regression coefficients for multivariate normal distributions. Annals of Mathematical Statistics, 22, 327–351.
BaroncelliA.LagoU.SzymanskiS. (2004). Il business del calcio. Milano, Italy: Egea.
4.
BarrosC. P. (2001). Economic return on schooling for soccer players. Journal of Sports Economics, 2, 369–378.
5.
BrownA.RambaccussingD.ReadeJ. J.RossiG. (2016). Using social media to identify market inefficiencies: Evidence from twitter and Betfair, 9.
6.
BrysonA.FrickB.SimmonsR. (2013). The returns to scarce talent: Footedness and player remuneration in European soccer. Journal of Sports Economics, 14, 606–628.
7.
CappelliP. (1998). New deal at work. Boston, MA: Harvard Business School Press.
8.
CarmichaelF.ThomasD. (1993). Bargaining in the transfer market: Theory and evidence. Applied Economics, 25, 1467–1476. doi:10.1080/00036849300000150
9.
ChenH.DeP.HuY. J.HwangB.-H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of Financial Studies, 27, 1367–1403.
10.
CraggJ.DonaldS. (1993). Testing identifiability and specification in instrumental variables models. Econometric Theory, 9, 222–240.
11.
DavidsonR.MacKinnonJ. G. (1981). Several tests for model specification in the presence of alternative hypotheses. Econometrica, 49, 781–793.
12.
DawesR. M.FaustD.MeehlP. E. (1989). Clinical versus actuarial judgment. Science, 243, 1668–1674.
13.
DeCenzoD. A.RobbinsS. P. (2001). Human resource management (7th ed.). Hoboken, NJ: Wiley.
14.
DeutscherC.BüschemannA. (2014). Does performance consistency pay off financially for players? Evidence from the Bundesliga. Journal of Sports Economics, 17, 27–43.
15.
DickeyJ. M.GunelE. (1978). Bayes factors from mixed probabilities. Journal of the Royal Statistical Society. Series B, 40, 43–46.
16.
DobsonS.GoddardJ. A.DobsonS. (2001). The economics of football. Cambridge, England: Cambridge University Press.
17.
DrutB.DuhautoisR. (2015). Assortative matching using soccer data: Evidence of mobility bias. Journal of Sports Economics, 18, 1–17.
18.
EicherT. S.HennC.PapageorgiouC. (2012). Trade creation and diversion revisited: Accounting for model uncertainty and natural trading partner effects. Journal of Applied Econometrics, 27, 296–321.
19.
EicherT. S.LenkoskiA.RafteryA. E. (2009). Bayesian model averaging and endogeneity under model uncertainty: An application to development determinants. Center for Statistics and the Social Sciences, University of Washington, Working Paper No. 94. Retrieved fromhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.159.6166&rep=rep1&type=pdf
20.
EvansJ. S. B. (2006). The heuristic-analytic theory of reasoning: Extension and evaluation. Psychonomic Bulletin & Review, 13, 378–395.
21.
FernandezC.LeyE.SteelM. F. (2001a). Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100, 381–427.
22.
FernandezC.LeyE.SteelM. F. (2001b). Model uncertainty in cross-country growth regressions. Journal of Applied Econometrics, 16, 563–576.
FrickB. (2007). The football players’ labor market: Empirical evidence from the major European leagues. Scottish Journal of Political Economy, 54, 422–446.
25.
GerrardB. (2001). A new approach to measuring player and team quality in professional team sports. European Sport Management Quarterly, 1, 219–234.
26.
GiulianiM. (2013). Not all sunshine and roses: Discovering intellectual liabilities “in action.”Journal of Intellectual Capital, 14, 127–144.
27.
HansenB. E. (2005). Challenges for econometric model selection. Econometric Theory, 21, 60–68.
28.
HeM.CachuchoR.KnobbeA. (2015). Football player’s performance and market value. Proceedings of the 2nd Workshop of Sports Analytics, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Retrieved from http://ceur-ws.org/Vol-1970/paper-11.pdf
29.
HermS.Callsen-BrackerH-M.KreisH. (2014). When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community. Sport Management Review, 17, 484–492.
KarlA.LenkoskiA. (2012). Instrumental variable Bayesian model averaging via conditional Bayes factors. arXiv preprint arXiv:1202.5846. Germany: Heidelberg University.
32.
KassR. E.RafteryA. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795.
33.
KeidellR. W. (1987). Team sports models as a generic organizational framework. Human Relations, 40, 591–612.
34.
KoopG. (2003). Bayesian econometrics. Chichester, England: John Wiley.
35.
KoopG.León-GonzalezR.StrachanR. (2012). Bayesian model averaging in the instrumental variable regression model. Journal of Econometrics, 171, 237–250.
36.
KuetheT. H.MotamedM. (2010). Returns to stardom: Evidence from us major league soccer. Journal of Sports Economics, 11, 567–579.
37.
LeamerE. (1978). Specification searches. New York, NY: Wiley.
38.
León-GonzálezR.MontolioD. (2015). Endogeneity and panel data in growth regressions: A Bayesian model averaging approach. Journal of Macroeconomics, 46, 23–39.
39.
LenkoskiA.EicherT. S.RafteryA. (2014). Two-stage Bayesian model averaging in endogenous variable models. Econometric Reviews, 33, 122–151.
40.
LeyE.SteelM. F. (2009). On the effect of prior assumptions in Bayesian model averaging with applications to growth regression. Journal of Applied Econometrics, 24, 651–674.
41.
LuciforaC.SimmonsR. (2003). Superstar effects in sport: Evidence from Italian soccer. Journal of Sports Economics, 4, 35–55.
42.
MadiganD.YorkJ. (1995). Bayesian graphical models for discrete data. International Statistical Review, 63, 215–232.
43.
MadiganD.YorkJ.AllardD. (1995). Bayesian graphical models for discrete data. International Statistical Review, 215–232.
44.
MahoneyT. A. (1989). Multiple pay contingencies: Strategic design of compensation. Human Resource Management, 28, 337–347.
45.
MollickE.NandaR. (2015). Wisdom or madness? Comparing crowds with expert evaluation in funding the arts. Management Science, 62, 1533–1553.
46.
MontanariF.SilvestriG.BofF. (2008). Performance and individual characteristics as predictors of pay levels: The case of the Italian “Serie A.”European Sport Management Quarterly, 8, 27–44.
47.
Moral-BenitoE. (2013a). Determinants of economic growth: A Bayesian panel data approach. The Review of Economics and Statistics, 94, 566–579.
48.
Moral-BenitoE. (2013b). Model averaging in economics: An overview. Journal of Economic Surveys, 29, 46–75.
49.
MüllerO.SimonsA.WeinmannM. (2017). Beyond crowd judgments: Data-driven estimation of market value in association football. European Journal of Operational Research, 263, 611–624.
50.
PeetersT. (2018). Testing the wisdom of crowds in the field: Transfermarkt valuations and international soccer results. International Journal of Forecasting, 34, 17–29.
51.
PenningsJ. M.LeeK.Van WitteloostuijnA. (1998). Human capital, social capital, and firm dissolution. Academy of Management Journal, 41, 425–440.
52.
PfefferJ. (1994). Competitive advantage through people. California Management Review, 36, 9–28.
53.
R Core Team. (2018). R: A language and environment for statistical computing [computer software manual]. Vienna, Austria. Retrieved fromhttp://www.R-project.org/
54.
RafteryA. E.MadiganD.HoetingJ. A. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association, 92, 179–191.
55.
RafteryA. E.ZhengY. (2003). Discussion: Performance of Bayesian model averaging. Journal of the American Statistical Association, 98, 931–938.
56.
Ramírez HassanA.Graciano LondoñoM. (2017). Profiting from the English premier league: Predictive elicitation, the Kelly criterion, and Black Swans. International Journal of Sports Finance, 12, 386–395.
57.
Sala-i MartinX.DoppelhoferG.MillerR. I. (2004). Determinants of long-term growth: A Bayesian averaging of classical estimates (BACE) approach. American Economic Review, 94, 813–835.
58.
SarganJ. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26, 393–415.
59.
ScellesN.HelleuB.DurandC.BonnalL. (2016). Professional sports firm values: Bringing new determinants to the foreground? A study of European soccer, 2005-2013. Journal of Sports Economics, 17, 688–715.
60.
SurowieckiJ. (2005). The wisdom of crowds. Anchor, United States of America.
61.
SzymanskiS. (2000). A market test for discrimination in the English Professional Soccer Leagues. Journal of Political Economy, 108, 590–603.
62.
SzymanskiS.SmithR. (1997). The English football industry: Profit, performance and industrial structure. International Review of Applied Economics, 11, 135–153.
63.
TverskyA.KahnemanD. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.
64.
WeimarD.WickerP. (2017). Moneyball revisited: Effort and team performance in professional soccer. Journal of Sports Economics, 18, 140–161.
65.
WrightP. M.SmartD. L.McMahanG. C. (1995). Matches between human resources and strategy among NCAA basketball teams. Academy of Management Journal, 38, 1052–1074.
