Printer Friendly

Factors that Shape the Demand for International Football Games Across Different Age Groups.


In recent years, the composition of revenue sources has changed substantially for professional football clubs, thereby leading to a significant dependency on broadcasting income. (1) In Europe, for example, the 20 highest-earning football clubs currently generate only about 17 percent of their revenues from game day income (Deloitte, 2017). Similarly, for international football federations, such as the Federation Internationale de Football Association (FIFA), the sale of hospitality rights and ticketing only delivers about 10 percent of the federation's total revenue (FIFA, 2017). In contrast, revenues from the sale of broadcasting rights have gradually increased to a share of about 43 percent and 53 percent, respectively. Therefore, for practitioners working with these football clubs, leagues or associations, catering to the specific needs of the so-called "couch potato audience" (Forrest, Simmons and Buraimo, 2005, p. 641) has become increasingly important.

Accordingly, economists, traditionally more interested in the economic analysis of football stadium attendances, have begun turning their attention to previously unavailable TV data to explore the potential determinants of television audience demand in professional club football. (2) In fact, previous research has already explored numerous European TV markets, including England (e.g., Alavy, Gaskell, Leach and Szymanski, 2010; Buraimo, 2008; Buraimo and Simmons, 2015; Cox, 2018; Forrest et al., 2005; Scelles, 2017), Germany (e.g., Schreyer, Schmidt and Torgler, 2018a; 2018b), Italy (e.g., Caruso, Addesa and Di Domizio, 2017; Di Domizio, 2010; 2013) and Spain (e.g., Buraimo and Simmons, 2009; Garcia and Rodriguez, 2006; Perez, Puente and Rodriguez, 2017). (3) In addition, a somewhat related research stream has slowly emerged, which models the TV demand for international football games (e.g., Artero and Bandres, 2017; Feddersen and Rott, 2011; Schreyer, Schmidt and Torgler, 2017). (4)

Despite this rapidly increasing interest of economists in exploring the determinants of football TV demand, there is, however, surprisingly little empirical evidence on the stability of such determinants across different age groups. In fact, no previous study has attempted to systematically analyze disaggregated TV data in such detail, and as a consequence, there exists no definitive answer to the question of whether heterogeneous consumer segments--such as teenagers, adults, and best agers--respond to similar product characteristics. As consumer needs, preferences, and tastes may evolve with advancing age (e.g., Du et al., 2015; Hoegele, Schmidt and Torgler, 2015; Wallendorf and Arnould, 1988), (5) it seems, however, highly likely that the opposite is true. Therefore, applying previous results obtained in, for example, rapidly aging societies such as Germany, Italy, or Spain (cf, United Nations, 2017), where so-called best agers (65+ years) represent a significant part of the audience, could ultimately favor misjudgments about how to effectively market the product to different age groups.

In this article, we address this notable research gap. More precisely, we present, to the best of our knowledge, the first comprehensive study that systematically explores the robustness of such well-established determinants as game outcome uncertainty (GOU), star appearance and the opportunity costs of watching an international football broadcast (cf., Borland and MacDonald, 2003) in explaining football TV demand across six significantly different age groups: early childhood, school age, adolescence, early adulthood, adulthood and best agers (cf, Erikson and Erikson, 1998). Analyzing the German TV demand for 174 international football games played by the German national football team between season 2004-2005 and 2016-2017, our exploratory study reveals that the determinants of international football demand are not necessarily robust about all age groups.

We proceed as follows: First, we provide a brief general overview of the previous research on international football demand; second, we introduce both our data set and our model; third, we present and discuss our results; and, fourth, we conclude this article.

International Football TV Demand

International football games are among the most popular sporting products in the world. In Germany, for example, the 2014 FIFA World Cup final reached 34.57 million viewers resulting in a market share of about 86 percent. It is, therefore, no surprise that according to the Gesellschaft fur Konsumforschung (GfK), the official German provider of television usage data, only nine out of the 100 television programs with the historically highest audience ratings in Germany were not directly related to an international football game. (6) In consequence, for football associations, such as the German Football Association (DFB), income from the marketing of the TV rights of the DFB team today already accounts for about 14 percent of the total DFB income (DFB, 2017).

Against this background, it might be somewhat surprising that economists have so far largely refrained from exploring TV audience demand for international football games. In fact, as far as we are aware, only about a handful of empirical studies have attempted to exploit such data in order to better understand the specific determinants of international football demand. (7)

To the best of our knowledge, Nuesch and Franck (2009; cf, Franck and Nuesch, 2008) were first to explore international football demand data. Primarily interested in the specific role of patriotism, the two authors analyze Swiss TV audience demand for 183 football games broadcasted during two FIFA World Cups and two Union des Associations Europeennes de Football (UEFA) European Championships. Interestingly, in line with their main hypotheses, the authors conclude that "the demand for televised national team games is largely driven by the expected game quality based on the proven playing strength of the opponents, as well as by patriotism" (p. 17).

In the aftermath of this seminal paper, a number of authors have begun exploring TV data for international football games of particular teams, rather than for particular competitions. Feddersen and Rott (2011), for example, were first to examine live broadcasts of the DFB team. Having analyzed 216 international football games between 1993 and 2008, the two authors, to some degree mirroring the results from Nuesch and Franck (2009), conclude that German TV audiences seem to "prefer a national team with established star players and high quality opponents" (p. 352). Similarly, Meier, and Leinwather (2013), predominantly interested in answering the question of whether TV audiences discriminate against perceived ethnic distance, observe that German TV viewers in general want "to be entertained by a relevant and closed competition between top teams" (p. 1,206)--a pattern, that seems to be largely robust across gender (Meier and Leinwather, 2012; cf, Meier, Strauss, and Riedl, 2017). (8) In another study, Meier, Reinhart, Konjer, and Leinwather (2016) analyze regional TV ratings to answer the question of whether the determinants of international football demand vary among TV audiences in the territory of former East and West Germany. Interestingly, the authors, while exploring relative rather than absolute TV demand approximated by regional market shares, incidentally also observe some potential cohort effects within their specific subsamples and, therefore, provide some initial empirical evidence that preferences of German TV audiences may, in fact, evolve with advancing age. (9) Further, Artero and Bandres (2017) were the first authors to examine the TV demand for another international football team than a German one. More precisely, analyzing TV data for football games of the Spanish national football team, the authors conclude that, largely in line with previous results, "variables associated with sporting relevance, the reputation of the opponent, and the uncertainty of outcome are all influential" (p. 17).

More recently, Schreyer et al., (2017) made a first attempt to combine these two previous analytical approaches by not only analyzing the German TV demand for international football games broadcasted during 6 FIFA World Cups and 5 UEFA European Championships, but by also exploring TV data for international games played by the DFB team. Interestingly, with regard to the much-debated role of GOU in shaping football audiences TV demand (cf., Pawlowski, 2013), the authors observe that "solely the demand for friendly games is affected by increasing uncertainty regarding the expected game outcomes" (p. 31).

In sum, the results from these pioneering studies suggest significant roles of some well-established determinants of professional football demand, including factors such as the game type (e.g., Artero and Bandres, 2017; Nuesch and Franck, 2009), scheduling (e.g., Meier et al., 2016; 2017; Schreyer et al., 2017), and the weather (e.g., Feddersen and Rott, 2011; Meier and Leinwather, 2012), in explaining international football TV demand. (10) Against this specific background, it is somewhat surprising that these first empirical studies have systematically failed to also employ otherwise well-established proxies for two of the arguably most-discussed determinants in the economic literature on professional football demand: betting odds proxying GOU (e.g., Buraimo and Simmons, 2015; Benz et al., 2009; Cox, 2018; Di Domizio, 2010; Peel and Thomas, 1992; Roy, 2004; Schreyer et al., 2018a; 2018b; 2016) and market values to approximate the presence of international superstars/talent (e.g., Brandes et al., 2008; Schreyer et al., 2018a; 2018b; Serrano et al., 2015). Further, perhaps with the notable exception of Meier et al. (2016), these previous studies have generally refrained from exploring the robustness of aforementioned determinants across different age groups in necessary detail. Accordingly, in this paper, we aim to complement the otherwise rich economic literature on professional football demand by exploring the robustness of the potential determinants of international football TV demand across six different age groups with a particular emphasis on both GOU and the presence of superstars as proxied by information derived from betting odds and market values, respectively.

Data Set and Empirical Model

Our analysis is based on a unique data set containing detailed information on 174 broadcasts of international football games played by the DFB team between August 2004 and July 2017, i.e., between the seasons 2004-2005 and 2016-2017. This period of observation was chosen because it allows us to explore the robustness of such potential determinants as GOU, and the presence of superstars using well-established proxies calculated from betting odds and market values, respectively (11) In this period of observation, on average, 13.19 million viewers watched a DFB game, with a minimum of 2.61 million viewers (Friendly against China, May 29, 2009) and a maximum audience of 34.57 million viewers (World Cup final against Argentina, July 13, 2014). In general, the average time spent in front of a TV has hardly changed during this period (AGF, 2018a), with a minimum average TV viewing time of 207 minutes per day in 2008 and a maximum of 225 minutes in 2011 (M = 217.07; SD = 6.57).

In order to explore the robustness of determinants shaping the demand for international DFB games across different age groups, we largely follow an established standard procedure. More precisely, we evaluate factor robustness using ordinary least squares (OLS) with White (1980) standard errors robust to heteroscedasticity, clustering over opponents, which takes into account the heterogeneity based on which two teams are playing (cf., Schreyer et al., 2017).

International football TV demand (AUDIENCE), our dependent variable, is measured in millions of viewers. Similar to previous studies (e.g., Feddersen and Rott, 2011; Meier and Leinwather, 2012; Schreyer et al., 2017), this information is taken from the GfK, the official German provider of television data. Unlike these previous attempts, however, we are not only interested in the analysis of aggregated German TV demand data, (12) but further differentiate between six different stages of life (cfi, Erikson and Erikson, 1998): childhood (3-4 years), school age (5-12 years), adolescence (13-19 years), early adulthood (20-39 years), adulthood (40-64 years) and best agers (65+ years).

Interestingly, relative TV audience demand, that is, an international football game's market share, was, on average, highest among best agers (M= 46.12, SD = 15.94), while absolute TV audience demand was, on average, highest in the second oldest age group, that is, adulthood (M= 5.41, SD = 3.05). As such, these initial descriptive results are largely reflective of the current German age structure. That is, at the end of 2016, about 35.84 percent of all Germans were in adulthood, while best agers, those born and raised in the golden age of television and the age group with the longest average TV viewing time (AFG, 2018b), currently represent about 21.22 percent of the German population (Statistisches Bundesamt, 2018). In contrast, only about 18.44 percent of the German population were adolescent or younger.

In Table 1, we first present a brief overview of the main explanatory variables. In line with previous research mentioned earlier, our set of independent variables includes a fairly standard set of quality aspects, as well as several factors proxying the opportunity costs of watching an international football game.

In general, we expect similar effects across age groups for quality aspects, but different effects across age groups for factors that proxy the opportunity costs of watching international football games. We base this argument on the observation that team identification, an antecedent of supportive behavior (, Mael and Ashforth, 1992; Hoegele, Schmidt, and Torgler, 2014), seems to vary significantly across a life stages. More precisely, Bergmann et al. (2016) observe a robust nonlinear, u-shaped relationship between consumer age and the identification with a professional football team, here the German national football team, "with a probable turning point in the 40s" (p. 718). As such, it seems at least likely that the viewing behavior of those individuals in their midlife phase (e.g., in early adulthood, adulthood), i.e., individuals with less discretionary time (e.g., Bowen and Riley, 2005), (13) might be more responsive to, for example, variations in the scheduling and also the weather of an international football game than the viewing behavior of those individuals in their late life phase (e.g., best agers), i.e., individuals with considerable discretionary time.

In general, aspects of international football game quality are frequently employed to predict TV audience demand (cf., Schreyer et al., 2017). Accordingly, first, we include the absolute difference in the winning probability of the home and the away team (APD), an established approximation for game outcome uncertainty (e.g., Buraimo and Simmons, 2009; Cox, 2018; Di Domizio and Caruso, 2015; Schreyer and Dauper, 2018). Initially suggested by Rottenberg (1956), increasing uncertainty about the expected game outcome is often, though not unambiguously, associated with an increase in the demand for professional football. With regard to international football, Schreyer et al. (2017) observe that German TV audiences do, to some extent, care about game outcome uncertainty. Although the authors explore the absolute difference between the teams according to the FIFA/Coca-Cola World Ranking, a GOU-approximation derived from league standings, rather than APD--a proxy derived from betting information--it would, therefore, be a surprise if an increase in uncertainty with regard to the game outcome would not lead to a robust increase in international football demand. Second, on a somewhat related note, we expect a similar robust positive relationship between the summed MARKET VALUE of both opponents, an established approximation not only for team quality, but also the appearance of superstars (e.g., Serrano et al., 2015), and German TV demand for international football games featuring the DFB team across all age groups. While the authors of most previous international football demand studies have primarily proxied the presence of superstars by employing measures based on the number of international football games previously played by a number of squad members (e.g., Artero and Bandres, 2017; Feddersen and Rott, 2011; Schreyer et al., 2017), more traditional football demand studies typically tend to include the summed market values of the two opponents' starting eleven (e.g., Buraimo and Simmons, 2015; Schreyer et al., 2018a; 2018b; Serrano et al., 2015). Empirical findings by these previous studies suggest that, in general, an increase in accumulated talent, and therefore the cumulated market value, leads to an increase in football demand.

In line with previous studies (e.g., Feddersen and Rott, 2011; Meier and Leinwather, 2011; Schreyer et al., 2017), we further include both a dummy variable, LOW, that captures the DFB team's head coach, which takes the value of one for Joachim Low and a value of zero for Jiirgen Klinsmann--the only other head coach in our period of observation, and a factor variable that accounts for the actual game type. More specifically, we differentiate between international friendlies, qualification games for the participation in both the UEFA European Championship and the FIFA World Cup, and subsequent tournament games in contrast to, for example, Schreyer et al. (2017), including FIFA Confederations Cup games. It is worth noting that during our period of observation, the vast majority of the 174 DFB games in our dataset were friend-lies for which German TV demand was, on average, significantly lower (M = 9.15, SD = 2.09) than for qualification games (M = 10.27, SD = 1.61), and tournament games (M = 24.72, SD = 6.43). While it seems rather obvious that the demand for Championship games is stronger than for international friendlies, this might be particularly true for children (e.g., childhood, school age) whose parents might be more willing to let them consume a FIFA World Cup game than an international friendly. As such, in this study, for the children age variable, we expect a positive effect of both UEFA European Championship and FIFA World Cup games, but not necessarily qualification games on international football demand. Further, differences might be somewhat more nuanced starting in the remaining age groups.

The remaining set of explanatory variables are all related to either scheduling aspects of the game or the weather and therefore, largely account for the potential opportunity costs of watching a football game. More precisely, we first include another dummy variable, HOME, that takes the value of one if the DFB team faces its opponent in Germany, and a value of zero otherwise (cf., Artero and Bandres, 2017; Feddersen and Rott, 2011; Schreyer et al., 2017). Second, by employing an additional dummy, PRIME TIME, we also consider whether (or not) an international football game was kicked-off at prime time (8:00 pm to 9:59 pm; e.g., Feddersen and Rott, 2011; Meier and Leinwather, 2012; Meier et al., 2016), while third, we use another factor variable, WEEKDAY, to capture the potential differences in German TV audience demand for international games scheduled on different days of the week. Fourth, we control for a potential effect of distinct broadcasting networks that might differ with regard to such aspects as journalistic quality (e.g., Feddersen and Rott, 2011). Over our observation period, two cooperating public broadcasting stations, namely ARD and ZDF, broadcast roughly 91 percent of all 174 international football games. Accordingly, we control for a broadcast preference by including a PUBLIC dummy. Finally, with regard to opportunity costs associated with the weather (e.g., Barwise and Ehrenberg, 1988; Eisinga, Franses and Vergeer, 2010; Roe and Vandebosch, 1996; Rott and Schmitt, 2000), we add three more continuous variables that account for RAIN (accumulated, in millimeter), SUNSHINE (accumulated, in hours) and the average TEMPERATURE (in [degrees]Celsius) in Germany on game day (cf., Table 1). Unlike previous attempts, the latter is included in a non-linear, squared form. In fact, while it seems intuitive that the opportunity costs of watching a game at home when it is freezing are rather low (cf., Feddersen and Rott, 2011; Meier and Leinwather, 2012), extreme heat, in contrast, could yield a similar effect. Finally, in order to account for varying TV demand over time, we also include season-fixed effects.


In Table 2, we report the results based on OLS estimation. Interestingly, and in line with our prediction, our exploratory results give reason to presume that the determinants of international football demand are not necessarily robust across all age groups. This is particularly true with regard to the comparatively large group of best agers, that is, TV audiences aged at least 65 years, who seem to significantly differ in their response to several potential determinants, including GOU, home games, and broadcasting network.

To explore the role of GOU in German TV audience demand for international football games of the DFB team, we run seven different regressions. First, in specification (1), we provide an estimation using aggregated German TV ratings (age 3+). Surprisingly, and contrary to previous research using approximations based on the FIFA/Coca-Cola World ranking (e.g., Schreyer et al., 2017), we observe no significant effect in support of Rottenberg's (1956) well-known uncertainty of outcome hypothesis (UOH). Intriguingly, however, this does not necessarily mean that all TV audiences are ultimately unresponsive to variations in competitive balance, and the resulting uncertainty with regard to the expected game outcome. We observe a negative and, more importantly, significant relationship supporting the UOH for both extremely young and rather old TV audiences. Although it is quite likely that the decision of these extremely young audiences is not a conscious decision in favor of probable close international football games, running additional seemingly unrelated regression (SUR) models followed by Wald tests reveals that best agers seem, in fact, to significantly differ from almost all the remaining groups when it comes to the role of suspense in shaping the decision to watch an international DFB game. In specification (7), all else being equal, we observe an increase of roughly 174,400 viewers moving from mean game outcome uncertainty to one standard deviation below that mean. It is also worth noting that both the direction and strength of the observed effect is largely robust to the usage of alternative game outcome uncertainty-proxies, including both the well-used THEIL (1967) measure and also Roy's (2004) relative winning probability measure.

With regard to the second explanatory variable capturing game quality, the presence of superstars/talent, we observe a positive and significant relationship between the two starting squads' accumulated market values and German TV demand. For example, in specification (6), all else being equal, we observe an increase of about 224,600 viewers moving from mean market values to one standard deviation above that mean. In fact, again with the exception of extremely young TV audiences, we observe a similar pattern for audiences in school age, adolescent, early adulthood and also for best agers, who all seem to become more interested in an international football game as the presence of superstars/talent increases.

Therefore, our results are, in general, in line with previous research incorporating international games played to proxy for a potential superstar effect (e.g., Feddersen and Rott, 2011; Schreyer et al., 2017), but somewhat contrast more recent findings by Artero and Bandres (2017) who, exploring Spanish TV data, observe that "the number of appearances by the players called up [...] are not significant" (p. 10).

The second group of variables captures the potential role of the type of international football game in shaping German TV demand. Here, we observe a positive and significant increase of both UEFA European Championship and FIFA World Cup games that is robust across all six different age groups. Since we, further, observe a similar and largely robust effect for FIFA Confederations Cup games, it is ultimately fair to conclude that those games staged during international tournaments seem to generate the strongest demand among all age groups. At the other end of the spectrum lie international friendlies that are--with the exception for very young TV audiences--significantly less demanded than qualification games for the UEFA European Championship, but not necessarily for the FIFA World Cup.

As with FIFA World Cup qualification games, determinants that relate with the actual scheduling of an international football game seem to be more or less effective across the different age groups. For example, we observe a positive effect of both HOME and PUBLIC only among best agers, while a game kicked-off at PRIME TIME unsurprisingly increases international football demand among TV audiences in adolescence, early adulthood, adulthood, and also best agers, and decreases the absolute number of extremely young TV audiences, but, also seems to have no significant effect on school kids. Interestingly, with regard to the WEEKDAY, TV audiences across all age groups seem to be largely unresponsive to variations indicating that, all else being equal, German TV demand should be similarly large regardless of whether a DFB game is played on a Monday or a Wednesday. There is, however, one exception. We observe that the demand across TV audiences in adulthood, those aged between 20 and 39 years, is significantly lower on both Friday and Saturday--a finding, that may reflect varying leisure preferences over time.

Finally, we also observe largely consistent responses to weather changes across TV audiences in all six age groups. In line with previous research by Schreyer et al. (2017), we are unable to observe a robust and significant effect of both RAIN and SUNSHINE. In contrast, most age groups (TV audiences in childhood and school age being the exception that proves the rule) respond to varying TEMPERATURE. In specification (6), for example, we observe, all else being equal, a non-linear, quadratic, effect of TEMPERATURE on AUDIENCE with a turning point of about 11 degrees Celsius, indicating that TV audiences may prefer alternatives to international football games if temperatures are either rather cold or rather warm.


For football associations such as the DFB, revenues from the sale of broadcasting rights constitute a significant portion of the annual income. Accordingly, sports economists and practitioners have recently begun exploring the potential determinants of international football TV demand. Interestingly; however, there as yet exists little research examining the robustness of factors such as the role of uncertainty with regard to the expected game outcome or the presence of superstars in shaping the demand for professional football broadcasts across different age groups. This is particularly surprising because, in rapidly aging societies like Germany, where the share of the so-called best agers (65+ years) is likely to successively increase from about 23 percent in 2020 to about 31 percent in 2040 (Statistisches Bundesamt, 2015), differences between at least two consumer groups, digital natives (i.e., today's children and young adults) and digital immigrants (today's best ager), might become more significant over time. As such, if the determinants of international football TV demand vary across age groups, football associations would be well-advised to shape the existing media product accordingly towards the preferences of the future's largest consumer group.

Analyzing the German TV audience demand for 174 international football games played by the DFB team between the thirteen seasons 2004-2005 and 2016-2017, our results reveal that, in fact, the effect of most potential determinants on international football demand is not necessarily robust across different age groups. In particular, we observe that it is the best agers (65+ years)--a comparatively large and increasingly important spectator group--whose behavioral responses seem to differ significantly from those of the five remaining groups. Accordingly, sports administrators responsible for the marketing of international football games are well-advised to consider different strategies when addressing their heterogeneous audiences (cf., Bergmann et al., 2016).

Although we are first to systematically explore the robustness of the potential determinants of professional football demand across different age groups, admittedly our exploratory study is nevertheless limited by its relatively small sample size that encompasses only one international football team. Hence, for our findings to be generalizable, future demand studies would benefit strongly from adapting a similar approach to analyze international football demand in different contexts and over an extended period of time. In addition, a panel approach could help better understanding individual changes over time.


(1) Throughout this article, the term football refers to European football, alternatively known as soccer in some areas.

(2) As Buraimo and Simmons (2015) observe, one obvious reason for the disproportionately small number of TV demand studies is that, "television audience data are typically difficult and expensive to obtain" (p. 452).

(3) It goes (almost) without saying that economists have not shied away from exploring TV data from other sports, too; for example, in American football (e.g., Paul and Weinbach, 2007; Tainsky, 2010), Cycling (e.g., Rodriguez-Gutierrez and Fernandez-Bianco, 2017; Van Reeth, 2013), motorsport (e.g., Berkowitz, Depken and Wilson, 2011; Schreyer and Torgler, 2018) or tennis (e.g., Meier and Konjer, 2015; Konjer, Meier and Wedeking, 2017).

(4) While economists have only recently begun to explore football TV data, the majority of previous football demand studies have analyzed stadium attendance demand (e.g., Allan and Roy, 2008; Benz, Brandes and Franck, 2009; Cox, 2018; Czarnitzki and Stadtmann, 2002; Di Domizio and Caruso, 2015; Garcia and Rodriguez, 2002; Pawlowski and Anders, 2012; Scelles et al., 2013; Schreyer and Dauper, 2018; Schreyer, Schmidt and Torgler, 2016; Wilson and Sim, 1995). Accordingly, Nalbantis and Pawlowski (2016) concluding that the "demand for televised football is a relatively under-researched topic" (p. 5).

(5) Bergmann, Schmidt, Schreyer, and Torgler (2016), observe a quadratic, u-shaped relationship between a consumer's age and the degree of identification with his/her favorite football team "with a probable turning point in the 40s" (p. 718). With regard to stadium attendances demand, Schreyer, Schmidt, and Torgler (2018) observe that season ticket holders' age, in contrast to gender, "seems to shape the stadium attendance demand" (cf., Schreyer et al., 2016, p. 276).

(6) Historical data since 1992. According to the GfK, at the time of submission, these nine programs include two UEFA Champions League finals, Borussia Dortmund against Bayern Munich (May 25, 2013; 21.61 million viewers, rank 58), and Bayern Munich against Chelsea London (May 19, 2012; 19.24 million viewers, rank 86), as well as three episodes of Wetten, dass..?, two episodes of Diese Drombuschs, and two episodes of Die Rudi Carrell Show.

(7) As one reviewer has rightfully pointed out, an obvious reason for this otherwise surprising neglect is the lack of data that would allow for analyzing the potential determinants of international football demand. In fact, the cost of acquiring such data is often prohibitively expensive.

(8) However this is only true for international football games played by the DFB team. In contrast, Meier and Leinwather (2012) observe gender differences in explaining TV audiences demand for international football games of the German Women's national football team.

(9) Noticeably, this does not mean that there is no need for additional research on the exploration of the robustness of the determinants of international football TV demand across heterogeneous age groups. Quite the contrary, Meier et al. (2016), for whom the robustness of determinants across age groups is not central limit their analysis to three rather broad age groups: (1) 14 to 35 years, (2) 36 to 55 years, and (3) older than 55 years. As such, the results do not allow to explore life phases, such as childhood, school age, and adolescence. In addition, the authors employ a somewhat reduced set of explanatory variables, excluding, the presence of superstars or opportunity costs associated with the weather, which, in turn, somewhat diminishes the significance of the presented results.

(10) Further, Baimbridge (1997) was the first to explore stadium attendance demand in an international context.

(11) In this period, a total of 186 international football games were played by the DFB team. Unfortunately, however, betting odds, which we derived from, were only available for 174 of those games.

(12) In specification (1), however, we nevertheless provide an estimation using aggregated German TV ratings (age 3 years and older).

(13) According to AGF (2018b), in 2017 the average TV viewing time per day and person was: 73 minutes (03 to 13 years), 105 minutes (14 to 29 years), 197 minutes (30 to 49 years) and 316 minutes (50 years and older). Although more granular data is currently not available, a positive correlation between age and average TV viewing time seems apparent. That is, the older a consumer, the more television he or she consumes.


AGF Videoforschung [AGF] (2018a). Entwicklung der durchschnittlichen Sehdauer pro Tag/Person in Minuten. [Development of the average duration of vision per day/person in minutes]. Retrieved from

AGF Videoforschung [AGF] (2018b). Durchschnittliche Sehdauer pro Tag/Person 2017. [Average viewing time per day/person 2017]. Retrieved from:

Alavy, K., Gaskell, A., Leach, S., & Szymanski, S. (2010). On the Edge of Your Seat: Demand for Football on Television and the Uncertainty of [the] Outcome Hypothesis. International Journal of Sport Finance, 5, 75-95.

Allan G., & Roy G. (2008). Does television crowd out spectators?: New evidence from the Scottish Premier League. Journal of Sports Economics, 9, 592-605.

Artero, I., & Bandres, E. (2017). The broadcasting demand for the Spanish National Soccer Team. Journal of Sports Economics. doi:10.1177/1527002517690786

Baimbridge, M. (1997). Match attendance at Euro 96: Was the crowd waving or drowning? Applied Economics Letters, 4, 555-558.

Barwise, P., & Ehrenberg, A. (1988). Television and its audience. London, United Kingdom: Sage.

Benz, M.-A., Brandes, L., & Franck, E. (2009). Do soccer associations really spend on a good thing? Empirical evidence on heterogeneity in the consumer response to match uncertainty of outcome. Contemporary Economic Policy, 27, 216-235.

Bergmann, A., Schmidt, S. L., Schreyer, D., & Torgler, B. (2016). Age and organizational identification: Empirical findings from professional sports. Applied Economics Letters, 23, 718-722.

Berkowitz, J. P., Depken, C. A, & Wilson, D. P. (2011). When going in circles is going backward: Outcome uncertainty in NASCAR. Journal of Sports Economics, 12, 253-283.

Borland, J., and MacDonald, R. (2003). Demand for Sport. Oxford Review of Economic Policy, 19, 478-502.

Buraimo, B. (2008). Stadium attendance and television audience demand in English league football. Managerial and Decision Economics, 29, 513-523.

Buraimo, B., & Simmons, R. (2009). A tale of two audiences: Spectators, television viewers and outcome uncertainty in Spanish football. Journal of Economics and Business, 61, 326-338.

Buraimo, B., & Simmons, R. (2015). Uncertainty of outcome or star quality? Television audience demand for English Premier League football. International Journal of the Economics of Business, 22, 449-469.

Brandes, L., Franck, E., & Niiesch, S. (2008). Local heroes and superstars: An empirical analysis of star attraction in in German soccer. Journal of Sports Economics, 9, 266-286.

Caruso, R., Addesa, F., & Di Domizio, D. (2017). The determinants of the TV demand for Soccer: Empirical evidence on Italian Serie A for the period 2008-2015. Journal of Sports Economics. doi: 10.1177/ 1527002517717298

Cox, A. (2018). Spectator demand, uncertainty of results, and public interest: Evidence from the English Premier League. Journal of Sports Economics, 19, 3-30.

Czarnitzki, D., & Stadtmann, G. (2002). Uncertainty of outcome versus reputation: Empirical evidence for the first German Football Division. Empirical Economics, 27, 101-112.

Deloitte (2017). Deloitte Football Money League 2017. Manchester, UK: Deloitte.

DFB (2017). DFB-Finanzbericht 2016. Frankfurt, GER: DFB. Retrieved from

Di Domizio, M. (2010). Competitive balance e audience televisva: Una analisi empirica dalla Serie A Italiana. [Competetive balance and television audience: An empirical analysis from the Italian Serie A]. Rivista di Diritto ed Economia Dello Sport, 6, 27-57.

Di Domizio, M. (2013). Football on TV: An empirical analysis on the Italian couch potato attitudes. Papeles de Europa, 26, 26-45.

Di Domizio, M., & Caruso, R. (2015). Hooliganism and demand for football in Italy: Attendance and counter violence policy evaluation. German Economic Review, 16, 123-137.

Du, R. Y., Hu, Y, & Damangir, S. (2015). Leveraging trends in online searches for product features in market response modeling. Journal of Marketing, 79, 29-43.

Eisinga, R., Franses, P. H., & Vergeer, M. (2010). Weather conditions and daily television use in the Netherlands, 1996-2005. International journal of Biometeorology, 55, 555-564.

Erikson, E. H., & Erikson, J. M. (1998). The life cycle completed (extended version). New York: WW Norton & Company.

Feddersen, A., & Rott, A. (2011). Determinants of demand for televised live football: Features of the German national football team. Journal of Sports Economics, 12, 352-369.

Federation Internationale de Football Association [FIFA] (2017). Financial report 2016. Zurich, Switzerland: FIFA.

Forrest, D., Simmons, R., & Buraimo, B. (2005). Outcome uncertainty and the couch potato audience. Scottish Journal of Political Economy, 52, 641-661.

Franck, E., & Niiesch, S. (2008). Alles nur Patrioten?: Eine empirische Analyse der Fernsehnachfrage wahrend der FIFA WM 2006. [All patriots?: An empirical analysis of television demand during the 2006 FIFA World Cup]. In Dietl, H., Franck, E. & Kempf, H. (Eds.), Fufiball: die Okonomie einer Leidenschaft. Magglingen: Hofman.

Garcia, J., & Rodriguez, P. (2002). The determinants of football match attendance revisited: Empirical evidence from the Spanish football league. Journal of Sports Economics, 3, 18-38.

Garcia, J., & Rodriguez, p. (2006). The determinants of television audience for Spanish football: A first approach. In: P. Rodriguez, S. Kesenne, and J. Garcia (Eds.), Sports economics after fifty years: Essays in honor of Simon Rottenberg. Oviedo, Spain: Ediciones de la Universidad de Oviedo, 147-167.

Hoegele, D., Schmidt, S. L., & Torgler, B. (2014). Superstars as drivers of organizational identification: Empirical findings from professional soccer. Psychology & Marketing, 31, 736-757.

Hoegele, D., Schmidt, S. L., & Torgler, B. (2015). The importance of key celebrity characteristics for customer segmentation by age and gender: Does beauty matter in professional football? Review of Managerial Science, 10, 601-627.

Konjer, M., Meier, H. E., & Wedeking, K. (2017). Consumer demand for telecasts of tennis matches in Germany. Journal of Sports Economics, 18, 351-375.

Nalbantis, G., & Pawlowski, T. (2016). The demand for international football telecasts in the United States. Cham, Switzerland: Palgrave Macmillan.

Nuesch, S., & Franck, E. (2009). The role of patriotism in explaining the TV audience of national team games: Evidence from four international tournaments. Journal of Media Economics, 22, 6-19.

Mael, F., & Ashforth, B. E. (1992). Alumni and their alma mater: A partial test of the reformulated model of organizational identification. Journal of Organizational Behavior, 13, 103-123.

Meier, H. E., & Konjer, M. (2015). Is there a premium for beauty in sport consumption? Evidence from German TV ratings for tennis matches. European Journal for Sport and Society, 12, 309-340.

Meier, E. H., & Leinwather, M. (2012). Woman as "armchair audience"? Evidence from German national team football. Sociology of Sport Journal, 29, 365-384.

Meier, E. H., & Leinwather, M. (2013). Finally a "taste for diversity"? National identity, consumer discrimination, and the multi-ethnic German national football team. European Sociological Review, 29, 1201-1213.

Meier, E. H., Reinhart, K., Konjer, M., & Leinwather, M.. (2016). Deutschland, einig FuBballland? Ost-West-Unterschiede in der Nachfrage nach Nationalmannschaftsspielen. [Football: the economy of a passion Germany, united football country? East-West differences in demand for national team games]. Leviathan, 44, 247-279.

Meier, E. H., Strauss, B. & Riedl, D. (2017). Feminization of sport audiences and fans? Evidence from the German men's national soccer team. International Review for the Sociology of Sport, 52, 717-733.

Paul, R. J., & Weinbach, A. P. (2007). The uncertainty of outcome and scoring effects on Nielsen ratings for Monday Night Football. Journal of Economics and Business, 59, 199-211.

Pawlowski, T, & Anders, C. (2012). Stadium attendance in German professional football: The (un)importance of uncertainty of outcome reconsidered. Applied Economics Letters, 19, 1553-1556.

Pawlowski, T. (2013). Testing the uncertainty of outcome hypothesis in European professional football. A stated preference approach. Journal of Sports Economics, 14, 341-367.

Peel, D. A., & Thomas, D. A. (1992). The demand for football: Some evidence on outcome uncertainty. Empirical Economics, 17, 323-331.

Perez, L., Puente, V., & Rodriguez, P. (2017). Factors determining TV soccer viewing: Does uncertainty of outcome really matter? International Journal of Sport Finance, 12, 124-139.

Rodriguez-Gutierrez, C., & Fernandez-Bianco, V (2017). Continuous TV demand in road cycling: The 2015 Vuelta a Espana. European Sport Management Quarterly, 17, 349-369.

Roe, K., & Vandebosch, H. (1996). Weather to view or not: That is the question. European Journal of Communication, 11, 201-216.

Rott, A., & Schmitt, S. (2000). Wochenend und Sonnenschein.... Medien & Kommunikationswissenschaft, 48, 537-553.

Rottenberg, S. (1956). The baseball players' labor market. Journal of Political Economy, 64, 242-258.

Roy, P. (2004). Die Zuschauernachfrage im professionellen Teamsport: Eine okonomische Untersuchung am Beispiel der deutschen Fussball-Bundesliga [The demand for professional team sports: An empirical examination of the German Bundesliga]. Aachen, Germany: Shaker Verlag.

Scelles, N. (2017). Star quality or competitive balance? Television audience demand for English Premier League football reconsidered. Applied Economics Letters, 24, 1399-1402.

Scelles, N, Durand, C., Bonnal, L., Goyeau, D., & Andreff, W. (2013). Competitive balance versus competitive intensity before a match: Is one of these two concepts more relevant in explaining attendance? The case of the French football Ligue 1 over the period 2008-2011. Applied Economics, 45, 4184-4192.

Schreyer, D., Schmidt, S. L., & Torgler, B. (2018a). Game outcome uncertainty and television audience demand: New evidence from German football. German Economic Review, 19, 140-161.

Schreyer, D., Schmidt, S. L., & Torgler, B. (2018b). Game outcome uncertainty in the English Premier League: Do German fans care? Journal of Sports Economics, 19, 625-644.

Schreyer, D., Schmidt, S. L., & Torgler, B. (2016). Against all odds? Exploring the role of game outcome uncertainty in season ticket holders' stadium attendance demand. Journal of Economic Psychology, 56, 192-217

Schreyer, D., Schmidt, S. L., & Torgler, B. (2017). Game outcome uncertainty and the demand for international football games: Evidence from the German TV market. Journal of Media Economics, 30, 31-45.

Schreyer, D., Schmidt, S. L., & Torgler, B. (2018). Predicting season ticket holder loyalty using geographical information. Applied Economics Letters, 25, 272-277.

Schreyer, D. & Dauper, D. (2018). Determinants of spectator no-show behaviour: First empirical evidence from the German Bundesliga. Applied Economics Letters, 25. 1475-1480.

Schreyer, D., & Torgler, B. (2018). On the Role of Race Outcome Uncertainty in the Television Demand for Formula 1 Grands Prix. Journal of Sports Economics, 19, 211-229.

Serrano, R., Garcia-Bernal, J., Fernandez-Olmos, M., & Espitia-Escuer, M. A. (2015). Expected quality in European football attendance: Market value and uncertainty reconsidered. Applied Economics Letters, 22, 1051-1054.

Statistisches Bundesamt (2015). Bevolkerung Deutschlands bis 2060. [German population until 2060]. Wiesbaden, Germany: Statistisches Bundesamt.

Statistisches Bundesamt (2018). Altersstruktur der Bevolkerung in Deutschland zum 31. Dezember 2016. [Age structure of the population in Germany as of 31 December 2016]. Retrieved from: (12411-0005).

Tainsky, S. (2010). Television broadcast demand for National Football League contests. Journal of Sports Economics, 11, 629-640.

Theil, H. (1967). Economics and information theory. Amsterdam, The Netherlands: North-Holland.

Van Reeth, D. (2013). TV demand for the Tour de France: The importance of stage characteristics versus outcome uncertainty, patriotism, and doping. International Journal of Sport Finance, 8, 39-60.

Wallendorf, M., & Arnould, E. J. (1988). "My favorite things": A cross-cultural inquiry into object attachment, possessiveness, and social linkage. Journal of Consumer Research, 14, 531-547.

Wilson, P., & Sim, B. (1995). The demands of semi-pro league football in Malaysia 1989-91: A panel data approach. Applied Economics, 27, 131-138.

Andreas Bergmann, (1) Dominik Schreyer (2)

(1) EBS Universitat fur Wirtschaft und Recht

(2) WHU--Otto Beisheim School of Management, Center for Sports and Management (CSM)

Andreas Bergmann is a research assistant at EBS Universitat fur Wirtschaft und Recht. His current research interests include the analysis of demographic trends and socioeconomic variations in sport consumer behavior.

Dominik Schreyer is an assistant professor at WHU--Otto Beisheim School of Management and associated with the Center for Sports and Management (CSM). He takes a keen research interest in the analysis of sports demand.
Table 1. Descriptive statistics of explanatory variables


Dependent variables
  AUDIENCE           Audience rating (in millions, age 03+)
  AUDIENCE0304       Audience rating (in millions, age 03-04)
  AUDIENCE0512       Audience rating (in millions, age 05-12)
  AUDIENCE1319       Audience rating (in millions, age 13-19)
  AUDIENCE2039       Audience rating (in millions, age 20-39)
  AUDIENCE4064       Audience rating (in millions, age 40-64)
  AUDIENCE65         Audience rating (in millions, age 65+)
Quality aspects
  GOU                Absolute difference in winning probability of
                     home/away team
  MARKETVALUE        Summed market value of the adversaries' starting
                     11 (in Mio. [euro])
Head coach
  LOW (12)           Head coach is Joachim Low (yes= 1; 0)
  HOME (1)           Game is being played in Germany (yes = 1; 0)
  PRIME TIME (1)     Prime time (yes = 1; 0)
  PUBLIC (1)         Game is being broadcast by ARD/ZDF (yes = 1; 0)
  RAIN (3)           Rainfall on game day (accumulated, in millimeter)
  SUNSHINE (3)       Sunshine on game day (accumulated, in hours)
  TEMPERATURE (3)    Temperature on game day (average, in

                     Expected  Robustness    Sources   M         SD
                     sign      (across age)

Dependent variables
  AUDIENCE                                   GFK       13  .191    7
  AUDIENCE0304                               GFK        0  .039    0
  AUDIENCE0512                               GFK        0  .469    0
  AUDIENCE1319                               GFK        0  .495    0
  AUDIENCE2039                               GFK        2  .432    1
  AUDIENCE4064                               GFK        5  .405    3
  AUDIENCE65                                 GFK        4  .349    1
Quality aspects
  GOU                -         Yes           ODDS4      0  .475    0
  MARKETVALUE        +         Yes           TM       282  .310  133
Head coach
  LOW (12)           +         Yes           Kicker     0  .867    0
  HOME (1)           +         Yes           Kicker     0  .448    0
  PRIME TIME (1)     +         No            Kicker     0  .764    0
  PUBLIC (1)         +         Yes           GFK        0  .908    0
  RAIN (3)           +         No            DWD        2  .071    6
  SUNSHINE (3)       -         No            DWD        5  .666    4
  TEMPERATURE (3)    +/-       No            DWD       13  .939    6

                           Min       Max

Dependent variables
  AUDIENCE           .327   2   .61   34  .57
  AUDIENCE0304       .056   0   .00    0  .39
  AUDIENCE0512       .441   0   .04    1  .99
  AUDIENCE1319       .417   0   .07    1  .69
  AUDIENCE2039       .734   0   .35    7  .16
  AUDIENCE4064       .053   0   .89   14  .90
  AUDIENCE65         .742   1   .20    9  .10
Quality aspects
  GOU                .272   0   .00    0  .95
  MARKETVALUE        .940  55   .00  654  .00
Head coach
  LOW (12)           .339   0   .00    1  .00
  HOME (1)           .498   0   .00    1  .00
  PRIME TIME (1)     .425   0   .00    1  .00
  PUBLIC (1)         .289   0   .00    1  .00
  RAIN (3)           .143   0   .00   50  .20
  SUNSHINE (3)       .611   0   .00   15  .40
  TEMPERATURE (3)    .427  -4   .10   28  .70

Abbreviations and Notes: N = 174 International DFB games; All figures
are rounded; Deutscher Wetter Dienst (DWD); Gesellschaft fur
Konsumforschung (GFK); (Kicker); (ODDS); (TM) (1) Dummy variable; (2) The only other head
coach in our period of observation was Jurgen Klinsmann;(3) As measured
in Frankfurt am Main; (4) In line with previous approaches (cf., Benz
et al., 2009), available betting odds were transformed into adjusted
probabilities excluding bookmakers margin.

Table 2. Factors that shape the demand for international football games
across different age groups

                                   03+          03-04
                                   (01)         (02)

Game quality
  Game outcome uncertainty        -0.993         -0.024 ([dagger])
                                   0.725          0.012
  Market value                     0.008 (***)    0.000
                                   0.002          0.000
Game type (Reference = Friendly)
  EC-Qualifier                     2.112 (***)    0.003
                                   0.384          0.006
  WC-Qualifier                     1.740 (**)     0.008
                                   0.488          0.008
  UEFA EC                         16.944 (***)    0.104 (***)
                                   0.859          0.024
  FIFA CONFED                      3.134 (**)     0.006
                                   0.915          0.016
  FIFA WC                         17.556 (***)    0.089 (***)
                                   0.741          0.015
Head coach
  LOW (1)                         -2.296 (**)     0.016
                                   0.839          0.013
  HOME (1)                         0.218          0.003
                                   0.289          0.004
  PRIME TIME (1)                   1.776 (***)   -0.042 (***)
                                   0.321          0.010
     Tuesday                       0.723          0.023
                                   0.845          0.039
     Wednesday                     0.417          0.021
                                   0.836          0.035
     Thursday                     -0.315          0.013
                                   0.937          0.035
     Friday                       -0.995          0.027
                                   0.922          0.035
     Saturday                     -1.102          0.031
                                   0.740          0.033
     Sunday                        0.461          0.016
                                   0.923          0.031
  PUBLIC                           0.411          0.002
                                   0.522          0.011
  RAIN                            -0.006         -0.000
                                   0.024          0.000
  SUNSHINE                        -0.025         -0.000
                                   0.037          0.001
  TEMPERATURE                      0.230 (**)     0.000
                                   0.067          0.001
  TEMPERATURE (*) TEMPERATURE     -0.009 (***)    0.000
                                   0.002          0.000
Season fixed effects              Yes           Yes
n                                 174           174
R (2)                              0.9618         0.6949
Mean demand (Mio.)                13.191          0.039

                                    05-12        13-19
                                    (03)         (04)

Game quality
  Game outcome uncertainty          0.020          0.021
                                    0.071          0.037
  Market value                      0.000 (*)      0.000 (**)
                                    0.000          0.000
Game type (Reference = Friendly)
  EC-Qualifier                      0.065 (*)      0.072 (**)
                                    0.030          0.023
  WC-Qualifier                      0.057          0.036
                                    0.038          0.027
  UEFA EC                           0.988 (***)    0.929 (***)
                                    0.114          0.048
  FIFA CONFED                       0.187 (*)      0.142 (**)
                                    0.090          0.044
  FIFA WC                           0.962 (***)    0.991 (***)
                                    0.070          0.044
Head coach
  LOW (1)                          -0.138 (*)     -0.262 (***)
                                    0.067          0.052
  HOME (1)                         -0.021         -0.004
                                    0.025          0.014
  PRIME TIME (1)                   -0.029          0.074 (***)
                                    0.045          0.019
     Tuesday                        0.212          0.024
                                    0.206          0.061
     Wednesday                      0.198          0.000
                                    0.194          0.062
     Thursday                       0.185         -0.049
                                    0.181          0.069
     Friday                         0.299         -0.039
                                    0.198          0.063
     Saturday                       0.261         -0.087
                                    0.197          0.057
     Sunday                         0.183          0.057
                                    0.188          0.059
  PUBLIC                            0.006          0.002
                                    0.063          0.032
  RAIN                              0.000          0.001
                                    0.002          0.001
  SUNSHINE                          0.000          0.000
                                    0.004          0.002
  TEMPERATURE                       0.009          0.012 (**)
                                    0.005          0.003
  TEMPERATURE (*) TEMPERATURE      -0.000         -0.000 (**)
                                    0.000          0.000

Season fixed effects              Yes            Yes
n                                 174            174
R (2)                               0.8808         0.9607
Mean demand (Mio.)                  0.469          0.495

                                    20-39               40-64
                                    (05)                (06)

Game quality
  Game outcome uncertainty          0.016              -0.376
                                    0.184               0.322
  Market value                      0.002 (***)         0.003 (***)
                                    0.000               0.000
Game type (Reference = Friendly)
  EC-Qualifier                      0.432 (***)         0.998 (***)
                                    0.111               0.161
  WC-Qualifier                      0.248 (*)           0.863 (***)
                                    0.118               0.199
  UEFA EC                           3.965 (***)         7.087 (***)
                                    0.179               0.375
  FIFA CONFED                       0.471 ([dagger])    1.665 (***)
                                    0.240               0.409
  FIFA WC                           3.982 (***)         7.288 (***)
                                    0.208               0.343
Head coach
  LOW (1)                          -0.945 (***)        -1.259 (**)
                                    0.207               0.366
  HOME (1)                         -0.004               0.087
                                    0.068               0.132
  PRIME TIME (1)                    0.528 (***)         0.781 (***)
                                    0.079               0.145
     Tuesday                       -0.134               0.289
                                    0.282               0.428
     Wednesday                     -0.253               0.181
                                    0.266               0.398
     Thursday                      -0.315              -0.102
                                    0.284               0.446
     Friday                        -0.501 ([dagger])   -0.568
                                    0.294               0.454
     Saturday                      -0.601 (*)          -0.460
                                    0.251               0.338
     Sunday                        -0.106               0.356
                                    0.272               0.445
  PUBLIC                            0.041              -0.053
                                    0.140               0.224
  RAIN                              0.005              -0.006
                                    0.006               0.010
  SUNSHINE                         -0.004              -0.015
                                    0.009               0.017
  TEMPERATURE                       0.060 (***)         0.097 (**)
                                    0.015               0.029
  TEMPERATURE (*) TEMPERATURE      -0.002 (***)        -0.004 (***)
                                    0.000               0.001
Season fixed effects              Yes                 Yes
n                                 174                 174
R (2)                               0.9550              0.9557
Mean demand (Mio.)                  2.432               5.405


Game quality
  Game outcome uncertainty         -0.640 (**)
  Market value                      0.001 (*)
Game type (Reference = Friendly)
  EC-Qualifier                      0.536 (***)
  WC-Qualifier                      0.521 (**)
  UEFA EC                           3.868 (***)
  FIFA CONFED                       0.664 (*)
  FIFA WC                           4.242 (***)
Head coach
  LOW (1)                           0.287
  HOME (1)                          0.157 ([dagger])
  PRIME TIME (1)                    0.466 (***)
     Tuesday                        0.307
     Wednesday                      0.266
     Thursday                      -0.051
     Friday                        -0.213
     Saturday                      -0.246
     Sunday                        -0.048
  PUBLIC                            0.407 (**)
  RAIN                             -0.006
  SUNSHINE                         -0.005
  TEMPERATURE                       0.049 (*)
  TEMPERATURE (*) TEMPERATURE      -0.002 (*)
Season fixed effects              Yes
n                                 174
R (2)                               0.9435
Mean demand (Mio.)                  4.349

Abbreviations and Notes: OPP (opponent); UEFA EC (Union des
Associations Europeennes de Football European Championship); FIFA
CONFED (Federation Internationale de Football Association
Confederations Cup); FIFA WC (Federation Internationale de Football
Association World Cup); (1) Dummy-variable; Robust standard errors are
given in bold; ([dagger]), (*), (**) and (***) represent statistical
significance at the 10% (p < .10), 5% (p < .05), 1% (p < .01) and 0.1%
(p < .001) levels, respectively.
COPYRIGHT 2019 Fitness Information Technology Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Bergmann, Andreas; Schreyer, Dominik
Publication:International Journal of Sport Finance
Article Type:Report
Date:Feb 1, 2019
Previous Article:NFL Betting Biases, Profitable Strategies, and the Wisdom of the Crowd.
Next Article:A Market Test for Ethnic Discrimination in Major League Soccer.

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters