A combined methodology for the concurrent evaluation of the business, financial and sports performance of football clubs: the case of France

Abstract

The recent transformation of football clubs to businesses and the challenges posed by this transformation motivate us to study the financial, business, and sports performance of French football clubs. We propose a two-stage method that can be applied to other settings, especially when there exist sample size and theoretical/model specification issues: first, Multicriteria Analysis is used to rank clubs on their financial and business performance dimensions; second, these rankings and the league standing (capturing sports performance) are used to assess the interrelationships of the different dimensions by means of a Partial Least Squares Structural Equation Modeling Approach. We find an amphidromous positive relationship between business performance and sports performance, and a one-way inverse relationship where financial performance affects sports performance. Put simply, more revenues affect sports achievements positively and these in turn impact positively on revenues in a virtuous cycle. The higher revenues do not aid financial performance given a race for success that can be possibly augmented by stakeholder myopia: the inherent to the sport pursuit of short term objectives to the detriment of long term sustainability. Consequently, the role of regulators (national authorities, UEFA Financial Fair Play) as custodians, is ever more important in protecting clubs from financial distress.

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Notes

  1. 1.

    For some recent instances, the total cost of the FIFA WORLD Cup 2014 in Brazil was estimated to be around $ 11.6 billion, while FIFA was able to generate revenues of $ 4.8 billion (FIFA.com). Football clubs around the world spent $ 4.1 billion on player transfers during 2014 (BBC News Business, 28 January) and the aggregate annual revenue of the twenty highest earning clubs in Europe has amounted to $ 7.9 billion in 2014 (€ 6.4 billion) which is double to the amount spent a decade earlier (Source: Deloitte, Soccer Money League, 2014). At the same time, according to the Forbes Magazine (Ozanian 2015) the twenty most valuable football companies in the word are all European (and have been so for many years) with a combined 2015 market value and projected revenues of more than $ 23 billion and $ 8.3 billion respectively.

  2. 2.

    In March 2015, the Serie A club Parma was declared bankrupt by an Italian court with debts of more than $ 220 million (The New York Times, March 20 2015).

  3. 3.

    Please note with regards to the sample end date, that some of the data we use become available in France 1 year after the end of the season, hence the latest period we could apply in the study that commenced in 2015 was for 2013 end data, obtained in 2014.

  4. 4.

    Based on UEFA’s rankings for club competitions, France ranks consistently at the top 6 positions during the sample period, and it has gained one place moving up to 5th out of 54 of European leagues in 2014–2015. http://www.uefa.com/memberassociations/uefarankings/country/.

  5. 5.

    TV rights are not considered in the research model as this would introduce a bias in the analysis of the relationship between sports performance and business performance. In France, TV rights are allocated to football clubs at the end of each season according to the rank in the league.

  6. 6.

    For example, these rules can protect stakeholders vis-à-vis management, given the presence of information asymmetries (e.g., protect professional footballers from not receiving their wages, as in Dimitropoulos 2010).

  7. 7.

    Managerial myopia is a well-known phenomenon in the neoclassical finance literature according to which in the presence of information asymmetries, managers need to provide short term results to boost reputation and therefore become short-sighted like myopia sufferers, focusing on short-term results at the expense of long-term outcomes and interests of the firm. This has been linked to agency theory (Jensen and Meckling 1976) the method of payment of managers (gratification) as well as to information asymmetries (Narayanan 1985).

  8. 8.

    For example, player and trainer compensation schemes contain a large number of short term bonuses, linked to game-by-game results and performance, with some also linked to attaining titles or certain positions in competitions. Similarly, club owners receive short-term ethical gratification by supporter appreciation and monetary gratification and success from direct and indirect revenues linked to club game-by-game wins, especially at the Champions League level.

References

  1. Antón, J. M., Grau, J. B., Cisneros, J. M., Tarquis, A. M., Laguna, F. V., Cantero, J. J., et al. (2016). Discrete multi-criteria methods for lands use and conservation planning on La Colacha in Arroyos Menores (Río Cuarto, Province of Córdoba, Argentina). Annals of Operations Research, 245(1–2), 315–336.

    Article  Google Scholar 

  2. Assaf, A., Barros, C. P., & Sá-Earp, F. (2009). Brazilian football league technical efficiency: A bootstrap approach. Technical University of Lisbon, WP 27/2009/DE/UECE.

  3. Bagozzi, R. P. (1994). Structural equation models in marketing research: Basic principles. In R. P. Bagozzi (Ed.), Principles of marketing research (pp. 317–385). Oxford: Blackwell.

    Google Scholar 

  4. Barajas, Á., & Rodríguez, P. (2010). Spanish football clubs’ finances: Crisis and players’ salaries. International Journal of Sport Finance, 5(1), 52–66.

    Google Scholar 

  5. Barlev, B., & Haddad, J. R. (2003). Fair value accounting and the management of the firm. Critical Perspectives on Accounting, 14(4), 383–415.

    Article  Google Scholar 

  6. Baroncelli, A., & Lago, U. (2006). Italian football. Journal of Sports Economics, 7(1), 13–28.

    Article  Google Scholar 

  7. Barros, C. P., & Leach, S. (2006). Performance evaluation of the English premier football league with data envelopment analysis. Applied Economics, 38(12), 1149–1458.

    Article  Google Scholar 

  8. Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200(1), 198–215.

    Article  Google Scholar 

  9. Birkinshaw, J., Morrison, A., & Hulland, J. (1995). Structural and competitive determinants of a global integration strategy. Strategic Management Journal, 16(8), 637–655.

    Article  Google Scholar 

  10. Boscá, J. E., Liern, V., Martínez, A., & Sala, R. (2009). Increasing offensive or defensive efficiency? An analysis of Italian and Spanish football. Omega, 37(1), 63–78.

    Article  Google Scholar 

  11. Brans, J. P., & Mareschal, B. (2005). PROMETHEE methods. In J. R. Figueira, S. Greco & M. Ehrgott (Eds.), Multiple criteria decision analysis: State of the art surveys (pp. 163–186). New York: Springer.

  12. Brans, J. P., & Vincke, P. (1985). Note—A preference ranking organisation method: (The PROMETHEE method for multiple criteria decision-making). Management Science, 31(6), 647–656.

    Article  Google Scholar 

  13. Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to rank and how to select projects: The PROMETHEE method. European Journal of Operational Research, 24(2), 228–238.

    Article  Google Scholar 

  14. Caballero, R., Romero, C., & Ruiz, F. (2016). Multiple criteria decision making and economics: An introduction. Annals of Operations Research, 245(1–2), 1–5.

    Article  Google Scholar 

  15. Campbell, J. Y., Hilscher, C., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899–2939.

    Article  Google Scholar 

  16. Carlos, M. A., & Preve, L. A. (2009). Trade receivables policy of distressed firms and its effect on the costs of financial distress. Financial Management, 38(3), 663–686.

    Article  Google Scholar 

  17. Cassel, C. M., Hackl, P., & Westlund, A. H. (1999). Robustness of partial least-squares method for estimating latent variable quality structures. Journal of Applied Statistics, 26, 435–446.

    Article  Google Scholar 

  18. Cohen, S., Doumpos, M., Neofytou, E., & Zopounidis, C. (2012). Assessing financial distress where bankruptcy is not an option: An alternative approach for local municipalities. European Journal of Operational Research, 218(1), 270–279.

    Article  Google Scholar 

  19. Corrente, S., Figueira, J. R., & Greco, S. (2014a). The SMAA-PROMETHEE method. European Journal of Operational Research, 239(2), 514–522.

    Article  Google Scholar 

  20. Corrente, S., Figueira, J. R., & Greco, S. (2014b). Dealing with interaction between bipolar multiple criteria preferences in PROMETHEE methods. Annals of Operations Research, 217(1), 137–164.

    Article  Google Scholar 

  21. Dawson, P., & Dobson, S. (2002). Managerial efficiency and human capital: An application to English association football. Managerial and Decision Economics, 23(8), 471–486.

    Article  Google Scholar 

  22. de Dios Tena, J., & Forrest, D. (2007). Within-season dismissal of football coaches: Statistical analysis of causes and consequences. European Journal of Operational Research, 181(1), 362–373.

    Article  Google Scholar 

  23. de Heij, R., Vermeulen, P. A. M., & Teunter, L. (2006). Strategic actions in European soccer: Do they matter? The Service Industries Journal, 26(6), 615–632.

    Article  Google Scholar 

  24. Dijkstra, T. (1983). Some comments on maximum likelihood and partial least squares methods. Journal of Econometrics, 22, 67–90.

    Article  Google Scholar 

  25. Dimitropoulos, P. (2010). The financial performance of the Greek football clubs. Sport Management International Journal, 6(1), 5–27.

    Google Scholar 

  26. Dobson, S., & Gerrard, B. (1999). The determination of player transfer fees in English professional soccer. Journal of Sport Management, 13(4), 259–279.

    Article  Google Scholar 

  27. Dobson, S. M., & Goddard, J. A. (1998). Performance and revenue in professional league football: Evidence from Granger causality tests. Applied Economics, 30(12), 1641–1651.

    Article  Google Scholar 

  28. Doumpos, M., & Zopounidis, C. (2007). Model combination for bankruptcy prediction and credit risk assessment: A stacked generalization approach. Annals of Operations Research, 151(1), 289–306.

    Article  Google Scholar 

  29. Duvivier, D., Roux, O., Dhaevers, V., Meskens, N., & Artiba, A. (2007). Multicriteria optimisation and simulation: An industrial application. Annals of Operations Research, 156(1), 45–60.

    Article  Google Scholar 

  30. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  31. Frick, B. (2007). The football players’ labor market: Empirical evidence from the major European leagues. Scottish Journal of Political Economy, 54(3), 422–446.

    Article  Google Scholar 

  32. Frick, B., Barros, C. P., & Prinz, J. (2010). Analysing head coach dismissals in the German Bundesliga with a mixed logit approach. European Journal of Operational Research, 200(1), 151–159.

    Article  Google Scholar 

  33. Galariotis, E., Guyot, A., Doumpos, M., & Zopounidis, C. (2016). A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France. European Journal of Operational Research, 248(1), 301–317.

    Article  Google Scholar 

  34. Garthwaite, P. H. (1994). An interpretation of partial least squares. Journal of the American Statistical Association, 89(425), 122–127.

    Article  Google Scholar 

  35. Gerrard, B. (2005). A resource-utilization model of organizational efficiency in professional sports team. Journal of Sport Management, 19(2), 143–169.

    Article  Google Scholar 

  36. Giulianotti, R. (2012). Football. London: The Wiley-Blackwell Encyclopedia of Globalization.

    Google Scholar 

  37. Giulianotti, R., & Robertson, R. (2004). The globalization of football: A study in the glocalization of the serious life. British Journal of Sociology, 55(4), 545–568.

    Article  Google Scholar 

  38. Giulianotti, R., & Robertson, R. (2007). Sport and globalization: Transnational dimensions. Global Networks, 7(2), 107–112.

    Article  Google Scholar 

  39. Goletsis, Y., Psarras, J., & Samouilidis, J. E. (2003). Project ranking in the Armenian energy sector using a multicriteria method for groups. Annals of Operations Research, 120(1–4), 135–157.

    Article  Google Scholar 

  40. Goossens, D. R., Beliën, J., & Spieksma, F. C. (2012). Comparing league formats with respect to match importance in Belgian football. Annals of Operations Research, 194(1), 223–240.

    Article  Google Scholar 

  41. Guzmán, I., & Morrow, S. (2007). Measuring efficiency and productivity in professional football teams: Evidence from the English Premier League. Central European Journal of Operations Research, 15(4), 309–328.

    Article  Google Scholar 

  42. Haas, D. J. (2003). Productive efficiency of English football teams—A data envelopment analysis approach. Managerial and Decision Economics, 24(5), 403–410.

    Article  Google Scholar 

  43. Haas, D. J., Kocher, M. G., & Sutter, M. (2004). Measuring efficiency of German football teams by data envelopment analysis. Central European Journal of Operations Research, 12(3), 251–268.

    Google Scholar 

  44. Haenlein, M., & Kaplan, A. M. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics, 3(4), 283–297.

    Article  Google Scholar 

  45. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Beverly Hills: Sage.

    Google Scholar 

  46. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.

    Article  Google Scholar 

  47. Hwang, H., Malhotra, N. K., Kim, Y., Tomiuk, M. A., & Hong, S. (2010). A comparative study on parameter recovery of three approaches to structural equation modeling. Journal of Marketing Research, 47, 699–712.

    Article  Google Scholar 

  48. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.

    Article  Google Scholar 

  49. Jones, S., & Hensher, D. A. (2004). Predicting firm financial distress: A mixed logit model. The Accounting Review, 79(4), 1011–1038.

    Article  Google Scholar 

  50. Jöreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrica, 36(2), 109–133.

    Article  Google Scholar 

  51. Késenne, S. (1996). League management in professional team sports with win maximizing clubs. European Journal for Sport Management, 2(2), 14–22.

    Google Scholar 

  52. Késenne, S. (2006). The win maximization model reconsidered: Flexible talent supply and efficiency wages. Journal of Sports Economics, 7(4), 416–427.

    Article  Google Scholar 

  53. Kounetas, K. (2014). Greek football clubs’ efficiency before and after Euro 2004 Victory: A bootstrap approach. Central European Journal of Operations Research, 22(4), 623–645.

    Article  Google Scholar 

  54. Lago, U., Baroncelli, A., & Szymanski, S. (2004). Il business del Calcio. Milano: Egea.

    Google Scholar 

  55. Lago, U., Simmons, R., & Szymanski, S. (2006). The financial crisis in European football: An introduction. Journal of Sports Economics, 7(1), 3–12.

    Article  Google Scholar 

  56. McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31, 239–270.

    Article  Google Scholar 

  57. Moreno, P., & Lozano, S. (2014). A network DEA assessment of team efficiency in the NBA. Annals of Operations Research, 217(1), 99–124.

    Article  Google Scholar 

  58. Narayanan, M. P. (1985). Managerial incentives for short term results. The Journal of Finance, 40(5), 1469–1484.

    Article  Google Scholar 

  59. Neale, W. (1964). The peculiar economics of professional sports. Quarterly Journal of Economics, 78(1), 1–14.

    Article  Google Scholar 

  60. Nikolaidis, Y. (2015). Building a basketball game strategy through statistical analysis of data. Annals of Operations Research, 227(1), 137–159.

    Article  Google Scholar 

  61. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.

    Google Scholar 

  62. Ozanian. (2015). https://www.forbes.com/sites/mikeozanian/2015/10/22/the-forbes-fab-40-the-most-valuable-brands-in-sports-2015/#781bf3ff1752.

  63. Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30, 467–480.

    Article  Google Scholar 

  64. Penman, S. H. (2001). Financial statement analysis and security valuation. New York: The McGraw-Hill Companies.

    Google Scholar 

  65. Pindado, J., Rodrigues, L., & de la Torre, C. (2008). Estimating financial distress likelihood. Journal of Business Research, 61(9), 995–1003.

    Article  Google Scholar 

  66. Pinnuck, M., & Potter, B. (2006). Impact of on-field football success on the off-field financial performance of AFL football clubs. Accounting and Finance, 46(3), 499–517.

    Article  Google Scholar 

  67. Quirk, J., & El Hodiri, M. (1974). The economic theory of a professional league. In R. Noll (Ed.), Government and the sport business (pp. 33–80). Washington, DC: Brookings Institution.

    Google Scholar 

  68. Quirk, J., & Fort, R. (1992). Pay dirt, the business of professional team sports. Princeton: Princeton University Press.

    Google Scholar 

  69. Ribeiro, A. S., & Lima, F. (2012). Portuguese football league efficiency and players wages. Applied Economics Letters, 19(6), 599–602.

    Article  Google Scholar 

  70. Sloane, P. J. (1971). The economics of professional football: The football club as a utility maximizer. Scottish Journal of Economy, 18(2), 121–146.

    Article  Google Scholar 

  71. Szymanski, S. (1998). Why is Manchester United so successful? Business Strategy Review, 9(4), 47–54.

    Article  Google Scholar 

  72. Teresa, J. A. (1993). Accounting measures of corporate liquidity, leverage, and costs of financial distress. Financial Management, 22(3), 91–100.

    Article  Google Scholar 

  73. Van Leeuwen, R., & Kalshoven, C. (2006). Soccernomics (soccer and the economy) ABN AMRO Economics Department, March Edition.

  74. Vrooman, J. (1997). A unified theory of capital and labor markets in major league baseball. Southern Economic Journal, 63(3), 594–619.

    Article  Google Scholar 

  75. Vrooman, J. (2000). The economics of American sport leagues. Scottish Journal of Political Economy, 47(4), 364–398.

    Article  Google Scholar 

  76. Walvin, J. (2001). The only game: Football in our times. London: Pearson.

    Google Scholar 

  77. Willaby, H. W., Costa, D. S. J., Burns, B. D., MacCann, C., & Roberts, R. D. (2015). Testing complex models with small sample sizes: A historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personality and Individual Differences, 84, 73–78.

    Article  Google Scholar 

  78. Yang, C.-H., Lin, H.-Y., & Chen, C.-P. (2014). Measuring the efficiency of NBA teams: Additive efficiency decomposition in two-stage DEA. Annals of Operations Research, 217(1), 565–589.

    Article  Google Scholar 

  79. Zopounidis, C., & Doumpos, M. (2001). Multi-group discrimination using multicriteria analysis: Illustrations from the field of finance. European Journal of Operational Research, 139(2), 370–388.

    Google Scholar 

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Galariotis, E., Germain, C. & Zopounidis, C. A combined methodology for the concurrent evaluation of the business, financial and sports performance of football clubs: the case of France. Ann Oper Res 266, 589–612 (2018). https://doi.org/10.1007/s10479-017-2631-z

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Keywords

  • Multiple criteria analysis
  • PROMETHEE II
  • Structural equation modeling
  • Football club performance
  • Financial performance
  • Partial least squares
  • Small samples