In this paper, the relationship between the performance level of football teams and certain factors of success has been investigated. Mainly, this has been examined through regression analysis, but also other methods such as correlation analysis has been used in some parts of the analysis. The data which is mainly used in these analyses are annual accounts for five selected football clubs over five years, 2006-2010, together with various football statistics from the internet. Much of the data is compiled in Excel and is available on the enclosed CD-ROM. For other parts of this paper, also an interview with a representative from one of the football clubs, Rosenborg BK, is used as a source of information. In addition, the financial situation of European football clubs has been discussed and examined through analysis of the annual accounts. Also here regression analysis has been an important method of investigation. Briefly, the results show that several of the suggested factors of success seems to have an impact of football teams performance level, here represented by the five best teams in the Norwegian elite division, Tippeligaen. Most important of all seems to be player wages and financial resources through operating income. Further, it‟s concluded that that there are several reasons for the financially difficult situation of many European football clubs. Among them are a win-maximizing approach rather than a profit-maximizing approach among European football clubs, and a very high cost level which, despite the high levels of income in many clubs, creates a financially instability. For football clubs it‟s therefore important to find a balance between income and costs, as well as restrict their expenditure, but still manage to maintain competitive advantages by buying players and spend money on wages and investment. In retrospect, it has been known to the author through the work that it had been more appropriate to base parts of this analysis on more observations than what has been done, particularly with respect to adding more football teams to the datasets. The alternative could have been to use more time trying to find a more appropriate analysis method than regression, which is the most widely used method here.
|Educations||MSc in Applied Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||93|