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The impact of English Premier League broadcasts on Danish spectator demand: a small league perspective


The effect of live transmissions of football matches on spectator demand in European football has been extensively studied over the years, although with little focus on the smaller leagues. By deploying robust panel data regression models on Danish first tier (Superligaen) data from 2010/11 to 2015/16, this paper contribute to filling this gap. We find that matches clashing with English Premier League (EPL) broadcasts do not suffer in attendance and that weather is an important driver of demand.

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Fig. 1
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Fig. 3


  1. 1.

    The Big Five leagues include England, Germany, France, Italy and Spain.

  2. 2.

    The number of studies on match day attendance is significant. While the literature review in this paper focuses on the studies that are most relevant to analyzing the problem presented, the operationalization of our model variables in Sect. 3 draws on other parts of the body of literature on spectator demand as inspiration for the variables chosen.

  3. 3.

    Blackouts refer to matches not being broadcast.

  4. 4.

    We use this period, as very few matches before 2010/11 clashed with Premier League broadcasts.

  5. 5.

    In 2015/16 Tottenham claimed its first top 3 placing since the 1989/90 season. Furthermore, the Danish footballer Christian Eriksen (who also had a leading role in the national team) had a dominant role in the team.

  6. 6.

    The lowest temperature in the dataset is − 8. To prevent negative values when squaring our variable a constant of ‘9’ has been added to its values.

  7. 7.

    57% of all matches from 2004/05 to 2015/16 were played on Sundays.

  8. 8.

    We also include Sønderjyske versus Esbjerg, which are located further apart, in Derby. This is because games between Sønderjyske and Esbjerg are characterised as derbies among fans and in the press (Tipsbladet 2017).

  9. 9.

    Noll argues that per capita income also reflects other societal differences between cities leading to different conclusions across studies.

  10. 10.

    When we have repeated observations on each unit (the home teams), we can elaborate on the regression equation by including unit-specific dummy variables Di. The fixed effects (within) estimator takes into account the measured time-varying independent variables that we have included in our model (xit) but also accounts for both the time-invariant independent variables (xi) that cannot be included in our model and the unmeasured time-invariant variables.

  11. 11.

    The 95% confidence intervals calculated in Figs. 1 and 3 show at which combinations of EPL clash and precipitation the effects are significantly or not significantly different with 95% statistical certainty in Fig. 1, and in Fig. 3 different combinations of temperature and precipitation.

  12. 12.

    Note that the graphs shows temperature plus 9°.

  13. 13.

    The sign is negative because the best position is assigned with ‘1’, while the bottom position is assigned with ‘12’.

  14. 14.

    Distance is also non-significant in the log–log models, however, with p-values close to 0, 10.


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Correspondence to Rasmus K. Storm.

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See Table 4

Table 4 Fixed effects log–log models, home team and interaction between EPL clash and precipitation


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Nielsen, C.G., Storm, R.K. & Jakobsen, T.G. The impact of English Premier League broadcasts on Danish spectator demand: a small league perspective. J Bus Econ 89, 633–653 (2019).

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  • Spectator demand
  • Professional soccer
  • Europe

JEL Classification

  • C10
  • C23
  • C57
  • D11