Drivers’ moves in Formula One Economics: A network analysis since 2000
Resumen
This paper explored the potentiality of social networks analysis to discuss the industrial organization of Formula One since the 2000 season. We tested three major hypotheses related to the centrality of championship teams, their selectiveness when observing drivers’ moves, and the role of certain explicative attributes. There are oligopolistic elements in Formula One, with champions adopting high values of betweenness centrality, sending and receiving drivers from some other teams and opting to exchange drivers and resources from other teams with not-so-competitive scuderias. Formula One teams that win the Constructors’ Championship tend to assume central roles in the network of drivers’ moves. Despite their centrality, these winning teams are very selective regarding the origin of the drivers they want to contract. There are more chances of contractual ties between teams which are not significantly close in terms of ranks or budgets.
Descargas
Citas
Benchekroun, H., & Breton, M., & Chaudhuri, A. R. (2018). Mergers in Nonrenewable Resource Oligopolies and Environmental Policies. European Economic Review, 111, 35-52. https://doi.org/10.1016/j.euroecorev.2018.08.008
Budzinski, O., & Müller-Kock. A. (2017). Is the revenue allocation scheme of Formula One Motor Racing a case for European Competition Policy? Formula One Antitrust. Contemporary Economic Policy, 36(1), 215-233.
Budzinski, O., & Gaenssle, S., & Kunz-Kaltenhäuser, P. (2019). How Does Online Streaming Affect Antitrust Remedies to Centralized Marketing? The Case of European Football Broadcasting Rights. SSRN Electronic Journal, 25(128), 1-27.
Budzinski, O., & Gaenssle, S., & Lindstädt, N. (2021). Wettbewerb und Antitrust in Unterhaltungsmärkten (Competition and Antitrust in Entertainment Markets). SSRN Electronic Journal, 25(128), 1-27.
Budzinski, O., & Stöhr, A. (2019). Public Interest Considerations in European Merger Control Regimes. SSRN Electronic Journal, 25(130), 1-40.
Cabral, L., Finnegan, C., & Finnegan, M. (2012). Formula one. In L. Cabral Ed., The Economics of Entertainment and Sports: Concepts and Cases. Forthcoming. Mimeo. Available from http://luiscabral.net/economics/books/entertainment/.
Celik, O. (2020). Survival of Formula One Drivers. Social Science Quarterly, 101(49), 1272-1281.
Cimarosti, A. (1997). The Complete History of Grand Prix Motor Racing. London: Aurum Press.
Cohen, T. & Kleiner, B. (2004). Managing wage and hours in the hotel industry. Management Research News, 27(6), 21-30.
Fialho, J., Saragoça, J., Baltazar, M., & Santos, M. (2018). Redes Sociais – Para uma Compreensão Multidisciplinar da Sociedade. Edições Sílabo: Lisboa.
Formula One. (2018). Various databases. Available through. http://www.f1.com
Frank, O., & Strauss, D., (1986). Markov graphs. Journal of the American Statistical Association 81, 832–842.
Gilbert, R., Riis, C., & S. Erlend. (2018). Stepwise Innovation by an Oligopoly. International Journal of Industrial Organization 61, 413-438.
Giuffre, K. (2013). Communities and Networks: Using Social Network Analysis to Rethink Urban and Community Studies. Wiley: New York.
Hanneman, R. A., & Mark, R. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside.
Holland, P. W., & Leinhardt, S. (1981). An Exponential Family of Probability Distributions for Directed Graphs. Journal of the American Statistical Association, 76(373), 33-50
Jenkins, M. (2002). The Formula One Constructors. Prentice-Hall.
Judde, C., Booth, R., & Brooks, R. (2013). Second Place Is First of the Losers. Journal of Sports Economics, 14. 411-439.
Kesteren, E. J., & Bergkamp, T. (2023). Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage. Journal of Quantitative Analysis in Sports, 19(4), 273–293. https://doi.org/10.1515/jqas-2022-0021
Lapre, Michael & Cravey, Candace. (2022). When Success Is Rare and Competitive: Learning from Others' Success and My Failure at the Speed of Formula One. Management Science, 16(2), 1-16.
Mariotti, F., & S. Haider. (2017). Networks of practice’ in the Italian motorsport industry. Technology Analysis and Strategic Management, 30(3), 1-12.
Morris, M. (2013). Stochastic Network Models with (but mostly without) epidemics. Isaac Newton Institute Seminar. https://www.newton.ac.uk/files/seminar/20130820093010001-153750.pdf
Mourao, P. (2016). Soccer transfers, team efficiency and the sports cycle in the most valued European soccer leagues – have European soccer teams been efficient in trading players?. Applied Economics, 48(56), 5513-5524
Mourao, P. (2017). The Economics of Motorsports – the case of Formula One. Palgrave-Macmillan.
Mourao, P. (2018a). Smoking Gentlemen—How Formula One Has Controlled CO2 Emissions. Sustainability, 10(6), 1841.
Mourao, P. (2018b). Surviving in the shadows- an economic and empirical discussion about the survival of the non-winning F1 drivers. Economic Analysis and Policy. Elsevier, 59(C), 54-68.
Mourao, P. R. (2021). Footsteps in the sand: studying refugee paths since 2005 through a network analysis of 205 territories. Qual Quant 55, 563–600.https://doi.org/10.1007/s11135-020-01014-5
Oberstone, J. (2009). Differentiating the top English premier league football clubs from the rest of the pack: Identifying the keys to success. Journal of Quantitative Analysis in Sports, 5(3), 1–29.
Olsen, W. (2004). Triangulation in social research: qualitative and quantitative methods can really be mixed. Developments in Sociology, 20, 103-118.
Pflugfelder, E. (2009). “Something less than a driver: Toward an understanding of gendered bodies in motorsport”. Journal of Sport and Social Issues November, 33(4), 411–426.
Potkanowicz, E., & Mendel, R. (2013). The case for driver science in motorsport: A review and recommendations. Sports Medicine, 43(7), 1–6.
Robins, G., Pattison, P., & Wasserman, S. (1999). Logit models and logistic regressions for social networks: III. Valued relations. Psychometrika, 64, 371–394.
Schreyer, D., & Torgler, B. (2016). On the Role of Race Outcome Uncertainty in the TV Demand for Formula 1 Grands Prix. Journal of Sports Economics, 19, 1-27.
Shumate, M., & Palazzolo E. T. (2010). Exponential Random Graph (p*) Models as a Method for Social Network Analysis in Communication Research. Communication Methods and Measures, 4(4), 341-371.
Snijders, T. A. B., Pattison, P. E., Robins, G. L., & Handcock, M. S. (2006). New specifications for exponential random graph models. Sociological Methodology, 36, 99–153.
Storm, R., & Nielsen, C., & Jakobsen, T. (2019). The Impact of Formula One on Regional Economies in Europe. Regional Studies, 54(6), 827-837.
Strzalkowski, T.., Harrison, T., & E. Khoja. (2019). GitHub as a Social Network. Springer-Verlag.
Szymanski, S. (2013). Wages, transfers and the variation of team performance in the English Premier League. Edward Elgar.
Tavani, D., & R. Vasudevan. (2014). Capitalists, Workers, and Managers: Wage Inequality and Effective Demand. Structural Change and Economic Dynamics, 30(1), 120-131.
Uddin, S., & Hossain, L. (2013). Dyad and Triad Census Analysis of Crisis Communication. Social Networking, 2, 32-41.
Valente, T. W., Coronges, K., Lakon, C., & Costenbader, E. (2008). How Correlated Are Network Centrality Measures? Connections, 28(1), 16-26.
Varotti, F., & Jorge, M., & Souza, D. (2020). Impacts of the Brazilian Formula 1 Grand Prix in the city of São Paulo. PODIUM Sport, Leisure and Tourism Review, 9, 71-92.
Zimmermann, S. (2010). Recruitment practice of German companies in the area top management. Zeitschrift für Personalforschung, 24(4), 416-419.
Las obras que se publican en esta revista están sujetas a los siguientes términos:
1. El Servicio de Publicaciones de la Universidad de Murcia (la editorial) conserva los derechos patrimoniales (copyright) de las obras publicadas, y favorece y permite la reutilización de las mismas bajo la licencia de uso indicada en el punto 2.
© Servicio de Publicaciones, Universidad de Murcia, 2013
2. Las obras se publican en la edición electrónica de la revista bajo una licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 3.0 España (texto legal). Se pueden copiar, usar, difundir, transmitir y exponer públicamente, siempre que: i) se cite la autoría y la fuente original de su publicación (revista, editorial y URL de la obra); ii) no se usen para fines comerciales; iii) se mencione la existencia y especificaciones de esta licencia de uso.
3. Condiciones de auto-archivo. Se permite y se anima a los autores a difundir electrónicamente las versiones pre-print (versión antes de ser evaluada) y/o post-print (versión evaluada y aceptada para su publicación) de sus obras antes de su publicación, ya que favorece su circulación y difusión más temprana y con ello un posible aumento en su citación y alcance entre la comunidad académica.