Game outcome uncertainty in the English Premier League: Do

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Game outcome uncertainty in the English Premier League: Do German

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https://doi.org/10.1177/1527002516673406

Article

Game Outcome

Uncertainty in the English

Premier League: Do

German Fans Care?

Dominik Schreyer

, Sascha L. Schmidt

1,2

and Benno Torgler

2,3

Abstract

Despite the increasing internationalization of marketing activities by professional

sporting clubs, previous research exploring the role of game outcome uncertainty

(GOU) in spectator demand has been exclusively conducted within national con-

texts. As a consequence, very little is known about the preferences of international

television (TV) spectators watching games from abroad. Hence, this study analyzes

all 571 English Premier League (EPL) games broadcast in Germany between the

seasons 2011-2012 and 2015-2016 in order to explore whether TV demand for

transnational football games is affected by GOU. In line with the prominent

uncertainty of outcome hypothesis, the results of this analysis reveal a significant and

positive relation between German EPL demand and GOU. This result, however, is

not consistent for male and female spectators.

Keywords

competitive balance, demand, football, game outcome uncertainty, television,

uncertainty of outcome hypothesis

Center for Sports and Management (CSM), WHU—Otto Beisheim School of Management, Du¨sseldorf,

Germany

CREMA–Center for Research in Economics, Management and the Arts, Zu¨rich, Switzerland

The School of Economics and Finance, Queensland University of Technology, Brisbane, Queensland,

Australia

Corresponding Author:

Dominik Schreyer, Center for Sports and Management (CSM), WHU—Otto Beisheim School of

Management, Erkrather Str. 224a, 40233 Du¨sseldorf, Germany.

Email: [email protected]

Journal of Sports Economics

1-20

ª The Author(s) 2016

Reprints and permission:

sagepub.com/journalsPermissions.nav

DOI: 10.1177/1527002516673406

jse.sagepub.com

Since Rottenberg’s (1956) pioneering work on the economics of professional team

sports, the composition of revenue sources has changed dramatically for profes-

sional sporting clubs (Buraimo & Simmons, 2015). For example, in the German

Bundesliga (the most attended football league in Europe),

football clubs (FCs)

currently generate only about 20% of their revenues from game day income

(Deutsche Fußball Liga, 2016). This pattern is also reflected in the balance sheets

of top European clubs such as Real Madrid Club de Fu

tbol (22%), Fu

tbol Club

Barcelona (24%), Manchester United FC (23%), Paris Saint-Germain FC (10%),

or Juventus (13%; cf. Union des Associations Europe´ennes de Football [UEFA],

2015). Most European FCs have experienced substantial growth in revenue gener-

ated from the sale of broadcasting rights and further commercialization not only

within the domestic market (cf. Deloitte, 2016) but with the increasing importance in

international markets.

This is particularly true for those professional FCs competing in the English

Premier League (EPL), where revenues generated from auctioning both national

and international media rights currently account for more than half of the clubs’

total revenues (UEFA, 2015).

While traditionally the majority of these revenues

were realized in the domestic market (i.e., the United Kingdom), EPL revenues

generated from international media rights have become an increasingly important

source of income. During the 1992-1997 cycle, international media rights brought in

revenues of 7.6 million British pounds sterling (£)/year, a figure that has increased

to an estimated £1.1 billion/year (bn/year) in the 2016-2019 cycle (see Table 1).

Currently, that is, during the 2013-2016 cycle, most revenues are generated in Asia

(£0.31 bn/year) and Europe (£0.20 bn/year), while broadcasting revenues from the

rest of the world account for an additional £0.29 bn/year (cf. Harris, 2016).

Within this context, it would make sense to test the so-called uncertainty of

outcome hypothesis (UOH), which is presumably Rottenberg’s most prominent

legacy.

It is therefore somewhat surprising that previous research in professional

football environments has essentially been limited to traditional stadium attendance

studies (for comprehensive reviews, see Pawlowski, 2013, and also Coates, Hum-

phreys, & Zhou, 2014).

Further, few studies exploring the role of game outcome

uncertainty (GOU) in shaping the television (TV) demand for professional football

have so far been conducted exclusively within national contexts (cf. Alavy, Gaskell,

Leach, & Szymanski, 2010; Buraimo, 2008; Buraimo & Simmons, 2009, 2015; Cox,

2015; Di Domizio, 2010, 2013; Forrest, Simmons, & Buraimo, 2005, 2006; Garcia

& Rodriguez, 2006; Johnsen & Solvoll, 2007; Kuypers, 1996).

As such, our knowl-

edge on foreign, that is, transnational, TV audiences’ GOU preferences, is limited.

Thus, in this article, we propose to reduce such a shortcoming by exploring trans-

national football TV spectator demand, analyzing all EPL games broadcasted in

Germany between the seasons 2011-2012 to 2014-2015.

More specifically, we test

Rottenberg’s prominent UOH by exploring the German TV demand for 571 EPL

games broadcasted live (and exclusive) by Sky Deutschland

—that is, Germany’s

2 Journal of Sports Economics

leading pay-TV platform with roughly 4.63 million customers at the end of Q2 2016.

As such, our sample allows EPL officials to better understand the role of GOU in

shaping TV audiences’ demand for EPL football in a foreign market, which, unlike the

markets in countries such as Australia, Hong Kong, and Thailand, is traditionally

dominated by a very strong domestic competitor, that is, the Bundesliga.

In addition, the current data set will not only allow observation of whether GOU

matters for German TV audiences’ EPL demand but also whether there are potential

differences between male and female TV demand,

a perspective largely neglected

in this stream of research (Meier & Leinwather, 2012). Thus, this study aims to

contribute to the growing literature on the role of competitive balance (CB) in

football league attractiveness, while also presenting a new analytical perspective

that may ultimately allow for a more accurate segmentation of the German TV

market. More specifically, male and female demand for professional sport broad-

casts may differ not only in general (e.g., Gantz, Wang, Paul, & Potter, 2006; Gantz

& Wenner, 1991; Meier & Leinwather, 2012)

but also with regard to the specific

role of GOU (cf. Gan, Tuggle, Mitrook, Coussement, & Zillmann, 1997).

The remainder of this relatively short article is structured as follows: the second

section reviews the literature examining the role of GOU in German football

demand, the third section then introduces our model, the fourth section presents the

results, and the fifth section concludes this article.

GOU and German Football Demand

Despite numerous empirical attempts to document the role of GOU in shaping the

demand for European professional football,

our knowledge on German GOU

Table 1. Development of English Premier League Revenues per Year Generated From

Auctioning Both National and International Media Rights Between the Seasons 1992-1997

and 2016-2019 Cycle.

Cycles Years

Revenues

Domestic

Revenues

International

Revenues

Combined

International (%)

1992-1997 5 £0.0382 bn £0.0076 bn £0.0458 bn 16.59

1997-2001 4 £0.1675 bn £0.0245 bn £0.1920 bn 12.76

2001-2004 3 £0.4000 bn £0.0593 bn £0.4593 bn 12.91

2004-2007 3 £0.3413 bn £0.1083 bn £0.4496 bn 24.09

2007-2010 3 £0.5686 bn £0.2167 bn £0.7853 bn 27.59

2010-2013 3 £0.5910 bn £0.4790 bn £1.0700 bn 44.77

2013-2016 3 £1.0060 bn £0.7440 bn £1.7500 bn 42.51

2016-2019 3 £1,7120 bn £1.1000 bn

£2.8120 bn 39.12

Note. Own calculations are based on external data (cf. British Broadcasting Corporation, 2015; Harris,

2016). Bn ¼ billion.

Forecast by Harris (2016).

Schreyer et al. 3

preferences is fairly limited. More specifically, not only have earlier attempts to

explore the GOU demand relationship in the Bundesliga focused (almost) exclu-

sively on examining stadium demand,

but they have also produced contradictory

findings (cf. Meier & Leinwather, 2016).

Table 2 gives an overview of the existing literature. Being the first to address the

economics of professional football in Germany, Ga¨rtner and Pommerehne (1978)

find no evidence in support of Rottenberg’s UOH during the six Bundesliga seasons

1969-1970 to 1974-1975.

Similarly, though at a considerably later stage during the

two seasons 1996-1997 and 1997-1998, Czarnitzki and Stadtmann (2002) conclude

that ‘‘uncertainty plays only a minor role in explaining attendance figures in the first

German football league’’ (p. 111). Interestingly, Roy (2004) observes a negative

relationship between the winning probability of the home team (WPH) and the

revenues of six Bundesliga teams from standing, though not from seated, accom-

modation for the four seasons 1998-1999 to 2001-2002,

implying that spectators in

the Bundesliga may not be in favor of overly imbalanced fixtures, where Goliath

Table 2. Empirical Studies on the Role of Game Outcome Uncertainty in German Profes-

sional Football Demand.

Authors (Year) Seasons

Dependent

Variables GOU Proxies

UOH

Support

Schreyer, Schmidt, and

Torgler (2016)

2012-2013 (1) Admission ADST, APD, FAVORITE,

FSB, ROY, THEIL,

and WPH

Yes

Pawlowski and Anders

(2012)

2005-2006 (1) Attendance THEIL No

Benz, Brandes, and

Franck (2009)

1999-2000 to

2003-2004 (5)

Attendance ADST, FSB, ROY, THEIL,

and WPH

Rottmann and Seitz

(2008)

2000-2001 to

2004-2005 (5)

Attendance ADST No

Roy (2004) 1998-1999 to

2001-2002 (4)

Attendance ADST, ROY, and WPH Mixed

Czarnitzki and

Stadtmann (2002)

1996-1997 to

1997-1998 (2)

Attendance WPH

rtner and

Pommerehne (1978)

1969-1970 to

1974-1975 (6)

Attendance PD No

Note. ADST ¼ absolute difference in league standing; APD ¼ absolute difference in winning probability of

the home and the away team; FAVORITE ¼ home team is favorite to win the game; FSB ¼ difference in

points per game corrected for home advantage; PD ¼ absolute difference in points between opponents;

ROY ¼ probability that one team will win; THEIL ¼ probability inequality of the three possible game

outcomes; WPH ¼ winning probability of the home team; GOU ¼ game outcome uncertainty; UOH ¼

uncertainty of outcome hypothesis.

Including its square.

Schreyer, Schmidt, and Torgler (2016), however, only employ a subset of one

Bundesliga team.

Supporting effects are significant only at the 10% level (p < .10).

Effect only positive/

significant for an ADST dummy in the heteroscedastic model.

Only for one proxy, that is, WPH, and only

for standing accommodation.

4 Journal of Sports Economics

hosts David. It is however worth noting that this result does not necessarily support

the assumption that Bundesliga spectators in general prefer close games, since Roy

(2004), in further analysis, does not find a robust effect on spectator demand of

two additional GOU proxies, that is, the probability that one team will win the

upcoming game (ROY) and the absolute difference in league standings (ADST;

cf., e.g., Baimbridge, Cameron, & Dawson, 1996; Falter & Pe´rignon, 2000;

Reilly, 2015). Similarly, for the four seasons 2001-2002 to 2004-2005, Rottmann

and Seitz (2008) observe no statistically significant effect of ADST on stadium

attendance demand. Interestingly enough, when ADST was transformed into a

dummy, capturing whether the ADST of any fixture was at or below the value

of 4 (and therefore featuring a Bundesliga game between two league table

neighbors), a positive and significant relationship was found between GOU and

Bundesliga demand. Employing a quantile regression approach to study Bunde-

sliga demand between the five seasons 1999-2000 and 2003-2004, Benz,

Brandes, and Franck (2009) conclude that GOU (once more proxied by ADST,

among others) shapes the demand for those games with evidence of strong

demand—a finding that, as the authors reflect, is basically in support of the

UOH.

Apart from this exception, however, German stadium attendance is

most likely to be maximized when the WPH reaches a value of roughly 53%,

that is, whenever the home team is expected to be the favorite. More recently,

Pawlowski and Anders (2012) report that when a dummy (FAVORITE) is

included for whether the WPH exceeds that of an away team win (WPA), there

is no significant evidence of spectator preference for those season 2005-2006

games hosted by a FAVORITE. Finally, exploring both season ticket holders’

stadium admission and stadium admission time, Schreyer, Schmidt, and Torgler

(2016) observe that GOU seems to play an important role in season ticket holder

decision-making.

Besides the results from more traditional demand studies, there is, however, more

recent evidence that German audiences may nevertheless value GOU in transna-

tional TV sport broadcasts. More specifically, Pawlowski (2013) tests the UOH

using a stated preference approach, revealing that an overwhelming majority of

German spectators seem to care about CB, that is, in the Bundesliga. In addition,

survey data from Nalbantis, Pawlowski, and Coates (2015) reveal that Bundesliga

fans’ willingness to pay for a single game ticket increases as the perceived suspense

increases. Interestingly, such a general preference for football games featuring two

equally strong competitors may also hold true for TV broadcasts of international

football games featuring the German DFB team as observed by Meier and

Leinwather (2012; see also Meier & Leinwather, 2013).

The latter finding partic-

ularly seems to reflect prior observations implying that GOU may be more likely

to shape the demand for football TV broadcasts than for live stadium attendance

(cf. Buraimo & Simmons, 2009). Specifically, Buraimo and Simmons (2009)

hypothesize that, in contrast to stadium attendance (most likely consisting of sup-

porters of either the home or the away team),

Schreyer et al. 5

television viewers will tend to comprise less-committed fans and many who have no

particular loyalty to either participating team [ ...] [and] may [therefore] well prefer a

close game to a contest that is effectively over as one team takes an early commanding

lead. (p. 327)

Although increasing evidence corroborates the suspicion that TV audiences in the

United Kingdom may be largely uninterested in GOU (cf. Buraimo, 2008; Buraimo

& Simmons, 2015; Forrest et al., 2006; Kuypers, 1996),

transnational spectators

watching EPL games out of Germany are nonetheless best understood as neutral

audiences that may appreciate the excitement and anticipation of a balanced cham-

pionship game.

Model

The predictions of the UOH are widely known: The demand for sporting products,

ceteris paribus, increases as uncertainty regarding the expected game outcome

increases (Rottenberg, 1956). In order to test whether the UOH holds true in a

new and so far unexplored transnational setting, GOU’s potential influence on TV

demand for transnational football games is evaluated using fractional probit

regressions, in which the standard errors are robust to heteroscedasticity and clus-

tering over team pairs, which takes into account the game heterogeneity based on

which teams are playing. Transnational TV audience’s demand is proxied using a

fixtures’ market SHARE; that is, the market share of EPL game, i, at kickoff time,

While TV demand is usually proxied by the absolute number of viewers

(AUDIENCE), SHARE represents relative demand, that is, relative to the total

number of all actual TV viewers at a certain moment in time. Therefore, SHARE

controls for general influences on TV demand such as variations in opportunity

costs such as weather conditions (e.g., Roe & Vandebosch, 1996).

It is worth

noting, however, that in order to further increase the robustness of our results, in

Specification (5)–(8), we also present additional OLS estimates employing AUDI-

ENCE as a dependent variable.

Following Benz et al. (2009), GOU, the key independent variable, is proxied by a

measure based on Theil’s (1967) inequality measure. Noticeably, the widely used

THEIL measure (e.g., Pawlowski & Anders, 2012; Serrano, Garcı´a-Bernal, Ferna´n-

dez-Olmos, & Espitia-Escuer, 2015) is based on the distribution of all three possible

game outcomes rather than just incorporating the probability of a home win.

Specifically, THEIL is calculated by

THEIL ¼

i¼1

log



;

where p

denotes the reported home win, draw, and away win probability, respec-

tively. Because small differences between these three probabilities result in a large

6 Journal of Sports Economics

THEIL, an increase in this measure is associatedwithanincreaseinoutcome

uncertainty. Furthermore, we not only increase the robustness of our results but also

allow for a better comparison with previous research efforts by including a number

of additional specifications in which we proxy GOU by (1) the absolute difference in

winning probability of home and away team (APD; e.g., Buraimo & Simmons, 2015;

Cox, 2015; Di Domizio & Caruso, 2015), (2) ROY (e.g., Benz, Brandes, & Franck,

2009; Roy, 2004), (3) the draw probability (DRAW; e.g., Cox, 2015; Di Domizio,

2010), and (4) the WPH (including its squared term; e.g., Cox, 2015; Jena & Reilly,

2016; Peel & Thomas, 1992).

In addition, several explanatory factors are considered which may shape trans-

national EPL demand (cf. Table 3 for descriptive statistics as well as information

on both the proxies’ specific composition and source). First, the summed

MARKET VALUE of the adversaries’ starting squad is included in order to control

for a potential positive superstar effect (Brandes, Franck, & Nu

esch, 2008; Serrano

et al., 2015).

Since PATRIOTISM seems to play an important role in interna-

tional football demand (Nu

esch & Franck, 2009), a dummy is included that takes

the value of 1 if a German player has made it into the starting 11. Further, we

control for whether the broadcasted game features a GEOGRAPHICAL DERBY,

often regarded as particularly exciting (e.g., Buraimo, 2008; Cairns, 1987; Hart,

Hutton, & Sharot, 1975), through the inclusion of a dummy variable that takes a

value of 1 if both contestants are located within a distance of roughly 100 km.

Further, we also include a dummy that accounts for whether at least one team was

PROMOTED, which accounts for participation by a less familiar team, which may,

in turn, induce disinterest.

The three final factors primarily account for scheduling effects. Specifically, we

control for whether a Bundesliga game is broadcasted in PARALLEL

and include

GAME DAY fixed effects (cf. Pawlowski & Anders, 2012), KICKOFF fixed effects

(a mutually exclusive combination of day and kickoff), and SEASON fixed effects.

Results

In Table 3, we first present the descriptive statistics of our key variables. On average,

TV AUDIENCES (14 years and older) for the 571 EPL games in our data set were

roughly 22,687 spectators,

ranging from a minimum of 0 to a maximum of 219,503

active EPL spectators (cf. Table 4). Overall, mean audiences show a positive trend in

EPL demand increasing from an average of roughly 11,889 (season 2011-2012) to

44,041 spectators (2015-2016). Interestingly, male EPL TV demand (M ¼ 19,263,

SD ¼ 22,164) was significantly higher than female demand, M ¼ 3,421; SD ¼ 5,188;

t(570) ¼ 19.9389, p < .001, in our period of observation.

To explore the relationship between GOU and transnational TV demand for EPL

games in Germany, a total of 16 estimations are conducted (cf. Tables 5 and 6).

Specification (01) includes only the THEIL measure, as well as those three controls

Schreyer et al. 7

Table 3. Descriptive Statistics.

Variables Source Expected Sign MSDMinimum Maximum

Dependent variables

AUDIENCE Audience rating English Premier League (in millions, age

14þ years)

GfK 0.0226 0.0259 0.0000 0.2195

SHARE Market share English Premier League (in percentage, age

14þ years)

GfK 0.1338 0.1676 0.0000 1.7271

SHAREF Female market share English Premier League (in percentage,

age 14þ years)

GfK 0.0382 0.0622 0.0000 0.4588

SHAREM Male market share English Premier League (in percentage,

age 14þ years)

GfK 0.2389 0.3032 0.0001 3.2578

GOU proxies

THEIL Probability inequality of the three outcomes FD þ 0.4245 0.0521 0.2484 0.4759

APD Absolute difference in winning probability of home and away

team

FD  0.3212 0.2023 0.0000 0.7686

ROY Probability that one team will win FD þ 0.2457 0.0519 0.1049 0.3010

DRAW Draw probability FD þ 0.2491 0.0388 0.1157 0.2994

WPH Winning probability of home team FD þ/ 0.4178 0.1958 0.1011 0.8264

Further game characteristics

MARKET VALUE Summed market value of the two adversaries’ starting 11 (in

million €)

TM þ 294.0545 102.9915 48.7500 640.0000

PATRIOTISM

German player has made it into the starting 11 (yes ¼ 1 and

otherwise ¼ 0)

TM þ 0.2311 0.4219 0.0000 1.0000

GEOGEAPHICAL

DERBY

Game is a geographical derby (yes ¼ 1 and otherwise ¼ 0) GM þ 0.2784 0.4486 0.0000 1.0000

PROMOTED

At least one team was promoted from the championship in

the previous season (yes ¼ 1 and otherwise ¼ 0)

Kicker  0.1593 0.3663 0.0000 1.0000

PARALLEL

Number of Bundesliga games broadcasted simultaneously Kicker  0.3607 0.4806 0.0000 1.0000

Note. N ¼ 571. English Premier League games broadcasted in Germany between the seasons 2011-2012 and 2015-2016. FD ¼ https://fottball-data.co.uk; GfK ¼ Gesellschaft

fu¨r Konsumforschung; Kicker ¼ Kicker Sportmagazin (i.e., the most prominent German football magazine); TM ¼ https://transfermarkt.com.

Dummy variable.

directly related to a fixtures’ scheduling, that is, GAME DAY, KICKOFF, and

SEASON fixed effects. Specification (02) then adds the remaining game character-

istics, that is, MARKET VALUE, PATRIOTISM, DERBY, PROMOTED, and

PARALLEL. While Specifications (03) and (04) report coefficients for relative

female and male EPL demand, respectively, the additional Specifications

(05)–(08) are best understood as robustness tests with an alternative dependent

variable, that is, AUDIENCE. Finally, in the Specifications (09)–(16), we present

further robustness checks with varying GOU proxies, that is, APD, ROY, DRAW,

and WPH.

In Tables 5 and 6, we present the results from the OLS regression. Interestingly,

and contrary to more recent research on national TV demand (e.g., Buraimo &

Simmons, 2015; Caruso, Addesa, & Di Domizio, 2016), we observe a significant

and robust though comparatively weak relationship between GOU and transnational

TV demand in support of the UOH. This is the case regardless of which GOU proxy

is employed (cf. Table 6). More specifically, in Specification (02), we observe that a

1% increase of THEIL increases SHARE by 0.0009. Similarly, in Specification (06),

that is, expressed in absolute rather than relative terms, the results suggest that a 1

standard deviation increase in THEIL increases German EPL TV demand by

roughly 2,550 viewers. It is however noteworthy that, as can be observed from both

Specifications (03) and (04) as well as (07) and (08), such an uncertainty effect is not

robust across gender. Quite to the contrary, while we observe a significant and robust

relationship between GOU and both SHARE and AUDIENCE for male audiences,

female EPL demand in Germany seems to be rather unaffected by variations in

uncertainty regarding the expected game outcome.

Besides that, our results suggest that female and male TV demand is driven by

similar factors. More specifically, we observe a robust and significant positive

relationship between EPL demand and both MARKET VALUE and PATRIOTISM,

whereas derbies and fixtures including at least one promoted team seem to leave

Table 4. English Premier League Fixtures With the Highest German TV Demand Between

Seasons 2011-2012 and 2015-2016 (Sky; Age 14þ Years).

Rank Date Fixture Demand Share (in Percentage)

1 December 26, 2015 Liverpool–Leicester 219,503 1.3851

2 October 17, 2015 Tottenham–Liverpool 197,008 1.7271

3 January 17, 2016 Liverpool–Manchester United 187,892 0.9870

4 May 1, 2016 Manchester United–Leicester 172,844 1.2169

5 February 14, 2016 Arsenal–Leicester 126,131 0.7838

6 February 6, 2016 Manchester City–Leicester 118,380 0.9843

7 January 13, 2016 Liverpool–Arsenal 117,657 0.4082

8 October 31, 2015 Chelsea–Liverpool 117,375 1.1276

9 January 19, 2014 Chelsea–Manchester United 112,759 0.5102

10 January 2, 2016 West Ham–Liverpool 105,618 0.7127

Schreyer et al. 9

Table 5. Factors That Shape the German TV Demand for English Premier League Football.

Dependent Variables

Share Audience

14þ Years 14þ Years

All

Female Male

All

Female Male

(01) (02) (03) (04) (05) (06) (07) (08)

Game outcome uncertainty

THEIL 0.7947**

(0.2722)

0.5369*

(0.2260)

0.2318

(0.2725)

0.6920**

(0.2549)

0.0556*

(0.0229)

0.0489*

(0.0215)

0.0057

(0.0035)

0.0432*

(0.0191)

Further game characteristics

MARKET VALUE 0.0007***

(0.0001)

0.0006***

(0.0001)

0.0007***

(0.0001)

0.0000***

(0.0000)

0.0000**

(0.0000)

0.0000***

(0.0000)

PATRIOTISM

0.1253***

(0.0224)

0.0968**

(0.0311)

0.1349***

(0.0248)

0.0118***

(0.0024)

0.0018**

(0.0005)

0.0100***

(0.0021)

DERBY

0.0079

(0.0302)

0.0066

(0.0415)

0.0127

(0.0321)

0.0002

(0.0025)

0.0000

(0.0005)

0.0003

(0.0021)

PROMOTED

0.0108

(0.0272)

0.0776y

(0.0459)

0.0003

(0.0299)

0.0011

(0.0020)

0.0000

(0.0000)

0.0011

(0.0016)

Scheduling

PARALLEL

0.1970***

(0.0464)

0.1878*

(0.0726)

0.2051***

(0.0475)

0.0158**

(0.0052)

0.0025*

(0.0011)

0.0132**

(0.0042)

Kickoff FEs Yes Yes Yes Yes Yes Yes Yes Yes

Game day FEs Yes Yes Yes Yes Yes Yes Yes Yes

Season FEs Yes Yes Yes Yes Yes Yes Yes Yes

Constant 4.1571*** 3.9919*** 3.9702***  4.0252*** 0.0471*** 0.0427*** 0.0047 0.0379***

Cluster 176 176 176 176 170 170 170 170

N 548 548 548 548 548 548 548 548

Pseudo R

.0379 .0437 .0492 .0469

.3976 .4881 .3496 .4824

Note. Robust standard errors are given in parentheses. THEIL ¼ probability inequality of the three possible game outcomes; FE ¼ Fixed effects.

Dummy variable.

y, *, **, and *** represent statistically significance at the 10% (p < .10), 5% (p < .05), 1% (p < .01), and .01% (p < .001) levels, respectively.

Table 6. Robustness Checks: Game Outcome Uncertainty as a Factor That Shapes the German TV Demand for English Premier League Football.

Dependent Variables

Share Audience

14þ Years 14þ Years

APD ROY DRAW WPH APD ROY DRAW WPH

(09) (10) (11) (12) (13) (14) (15) (16)

Game outcome uncertainty

PROXY 0.1290*

(0.0570)

0.5471*

(0.2239)

0.8535**

(0.3134)

1.1124**

(0.3253)

0.0133***

(0.0052)

0.0511*

(0.0211)

0.0723*

(0.0297)

0.0833**

(0.0286)

PROXY  PROXY 1.2090**

(0.3604)

0.0942**

(0.0333)

Further game characteristics

MARKET VALUE 0.0007***

(0.0001)

0.0007***

(0.0001)

0.0007***

(0.0001)

0.0006***

(0.0001)

0.0000***

(0.0000)

0.0000***

(0.0000)

0.0000***

(0.0000)

0.0000***

(0.0000)

PATRIOTISM

0.1237***

0.0225

0.1248***

0.0224

0.1252***

0.0223

0.1166***

0.0225

0.0117***

0.0024

.0118***

.0024

0.0118***

0.0024

0.0111***

0.0025

DERBY

0.0095

(0.0305)

0.0087

(0.0302)

0.0081

(0.0298)

0.0118

(0.0291)

0.0004

(0.0025)

0.0003

(0.0025)

0.0002

(0.0025)

0.0005

(0.0024)

PROMOTED

0.0132

(0.0266)

0.0111

(0.0270)

0.0081

(0.0267)

0.0056

(0.0274)

0.0010

(0.0019)

0.0011

(0.0019)

0.0011

(0.0019)

0.0019

(0.0020)

Scheduling

PARALLEL

0.1958***

(0.0464)

0.1966***

(0.0463)

0.1989***

(0.0461)

0.1854***

(0.0465)

0.0156***

(0.0052)

0.0157**

(0.0052)

0.0159**

(0.0052)

0.0151**

(0.0052)

Kickoff FEs Yes Yes Yes Yes Yes Yes Yes Yes

Game day FEs Yes Yes Yes Yes Yes Yes Yes Yes

Season FEs Yes Yes Yes Yes Yes Yes Yes Yes

Constant 3.7146*** 3.8935*** 3.9734*** 3.9745*** 0.0165* 0.0340*** 0.0399*** 0.0362***

Cluster 176 176 176 176 170 170 170 170

N 548 548 548 548 548 548 548 548

Pseudo R

.0437 .0437 .0438 .0441

.4889 .4887 .4894 .4922

Note. Robust standard errors are given in parentheses. APD ¼ absolute difference in winning probability of the home and the away team; ROY ¼ probability that one team

will win; DRAW ¼ the draw probability; WPH ¼ winning probability of the home team; FE ¼ Fixed effects.

Dummy variable.

y, *, **, and *** represent statistical significance at the 10% (p < .10), 5% (p < .05), 1% (p < .01), and .01% (p < .001) levels, respectively.

German TV audiences unimpressed. In contrast, we observed it is the actual fixture,

that is, GAME DAY, KICKOFF, and SEASON fixed effects and also PARALLEL

Bundesliga broadcasts that seem to affect transnational TV demand. In Specifica-

tions (02) and (06), for example, we observe a significant and negative relationship

between PARALLEL and both SHARE and AUDIENCE, indicating that, at least for

German TV audiences, EPL football seems to be most attractive as a supplement

rather than an actual substitute for domestic Bundesliga football broadcasts. Or, in

other words, it seems to be a product whose attractiveness depends, at least to some

extent, upon the unavailability of the domestic competitor. Accordingly, we further

observe a significantly higher EPL demand for some fixtures typically broadcasted

during the German winter break, most notably GAMEDAY 18 that, more often than

not, includes Boxing Day.

However, there seem to be some gender differences in

response to the fixture. For example, we are unable to identify a robust peak in EPL

interest at GAMEDAY 18 for females. Moreover, while the kickoff time seems to

matter for males, it does not drive female audiences’ demand.

The latter observation underlines the possibility that German female EPL

demand, based on our explanatory models, seems to be significantly more difficult

to predict (cf. R

in the Specifications [07] and [08]). In fact, an additional Wald test,

conducted to test whether all the coefficients in the two Specifications (07) and (08)

are equal, indicates statistical differences between the overall male and female

estimations (Wald w

¼ 420.29, p < .001). This higher uncertainty with respect to

female preferences and viewing patterns might be an important consideration in the

context of market segmentation. As such, this short article emphasizes the impor-

tance of a somewhat more nuanced examination of sport spectator decision-making,

that is, one that differentiates between individual spectator groups such as not only

male and female audiences but also national and international audiences, neutral

audiences and fans, and so on.

Conclusion

As a novelty, this research explores the relationship between GOU and spectator

demand in a transnational scenario. Analyzing 571 EPL games broadcasted in Ger-

many between the seasons 2011-2012 and 2015-2016, we observe a significant and

robust though comparatively weak relationship between GOU and transnational TV

demand in support of the UOH.

Although this finding seems to be quite robust

regardless of which GOU proxy is employed, we do find significant differences

across gender. More precisely, we observe that, contrary to male audiences, female

TV audiences in Germany seem to be unimpressed by variations in GOU.

In addition, factors such as the appearance of stars (as proxied by market values),

German players, and the fixture itself seem to shape such demand. Therefore, EPL

executives are, in general, well advised to not only further encourage its clubs to

attract established (German) talent to the league but also to internationally market its

12 Journal of Sports Economics

core product, the Premier League, accordingly. With regard to the German market

(being a market with a comparably strong domestic league, i.e., the German Bun-

desliga), it seems questionable whether such a strategy would in fact further increase

the existing EPL demand. More precisely, our results give reason to presume that

EPL football is, at least for German audiences, best understood as a product that

supplements domestic Bundesliga football broadcasts. This might, however, not

necessarily be the case in all international markets—quite to the contrary—the

successful international marketing of the EPL suggests that the opposite might be

true in countries such as Australia, Hong Kong, and Thailand. Accordingly, future

research would benefit from considering such alternative TV markets in order to

explore both the determinants of transnational audiences’ football demand in general

and the specific role of GOU therein in particular.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, author-

ship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of

this article.

Notes

1. Please note that throughout this article, the term football refers to European football,

which in some parts of the world is also known as soccer.

2. More specifically, Union des Associations Europe´ennes de Football’s (UEFA) Club

Licensing Benchmarking Report: Financial Year 2014 estimates English Premier League

(EPL) media revenues of roughly 2.07 billion (bn) €/year (53% of total revenues)—an

absolute value on par with that of Major League Baseball (about 2.17 bn/year) and

considerably higher than that of National Basketball Association (about 1.43 bn/year)

and National Hockey League (about 0.32 bn/year). It is, however, worth noting that

National Football League (NFL; about 4.59 bn/year) media revenues significantly exceed

that of all competitors—that is, in both absolute and relative terms (cf. UEFA, 2015).

3. As it is widely known, Rottenberg (1956) was the first author to argue that ‘‘uncertainty

of outcome is necessary if the consumer is to be willing to pay admission to the game’’

(p. 246). The resulting uncertainty of outcome hypothesis (UOH) therefore implies that

(unlike other product markets) in professional sporting contests, a monopolistic situa-

tion does not necessarily benefit the providers (e.g., a league association). Accordingly,

results from UOH research have been frequently employed as a basis for discussing

and/or justifying the introduction of regulatory intervention(s) in professional sporting

leagues (e.g., Forrest, Simmons, & Buraimo, 2005).

4. It is almost needless to say that, aside from professional football, Rottenberg’s UOH has

been tested by exploring the role of (game) outcome uncertainty in shaping the television

Schreyer et al. 13

(TV) demand for a myriad of sporting products ranging from Formula 1 Grands Prix

(Schreyer & Torgler, 2016) and National Association for Stock Car Auto Racing races

(Berkowitz, Depken, & Wilson, 2011) to NFL games (e.g., Paul & Weinbach, 2007;

Tainsky, 2010; Tainsky & McEvoy, 2012) or Tour de France stages (Van Reeth, 2013).

For more detailed reviews on earlier UOH research, see, for example, Borland and

Macdonald (2003) and Szymanski (2003).

5. It is worth mentioning that, on a somewhat related note, Tainsky and McEvoy (2012)

provide some first empirical support for UOH’s validity by exploring the TV demand for

NFL broadcasts in markets without local teams.

6. Unfortunately, to the best of our knowledge, there is no information available on EPL

revenues generated in the German market.

7. It should be noted that in our final analysis, we drop 23 fixtures because of unusual time

slots (e.g., postponed EPL games).

8. As such, all 571 broadcasts were only available for subscribers. It is further worth noting

that before 2011, the demand for Sky broadcasts was not captured by the Gesellschaft fu

Konsumforschung (GfK), that is, the official provider of German TV usage data.

9. Although outcome uncertainty has been measured at different temporal scales (see, e.g.,

Cairns, Jennett, & Sloane, 1986; Szymanski, 2003), we are exclusively concerned with

short-term, individual game outcome uncertainty (GOU) throughout this study.

10. Moreover, analyzing detailed information on 13,892 ticket holders of a German profes-

sional football club (FC), Schreyer, Schmidt, and Torgler (2016), for example, observe

that the relative proportion of female season ticket holders is roughly 20%.

11. To the best of our knowledge, there are currently 40þ studies exploring the GOU-demand

relationship in professional football leagues such as the EPL (e.g., Buraimo & Simmons,

2015; Forrest et al., 2005; Hart, Hutton, & Sharot, 1975), the French League 1 (e.g., Falter

&Pe´rignon, 2000; Falter, Pe´rignon, & Vercruysse, 2008; Scelles, Durand, Bonnal,

Goyeau, & Andreff, 2013), the Italian Serie A (e.g., Di Domizio, 2010, 2013; Di Domizio

& Caruso, 2015), or the Spanish Primera Divisio´n (e.g., Buraimo & Simmons, 2009;

Garcı´a & Rodrı´guez, 2002, 2006). In addition, a more recent stream of literature has

begun considering the role of GOU as a determinant of international football demand

(cf. Baimbridge, 1997; Franck & Nu

esch, 2008; Nu

esch & Franck, 2009; Meier &

Leinwather, 2012, 2013).

12. As Buraimo and Simmons (2015) argue, one reason might be that ‘‘television audience

data are typically difficult and expensive to obtain’’ (p. 4).

13. Further, it should be noted that (few) additional studies have explored the role of

both medium- and long-term uncertainty in the German Bundesliga (e.g., Bu

ch, 1979;

Rottmann & Seitz, 2008).

14. It is, however, noteworthy that Ga¨rtner and Pommerehne (1978) only employ a very small

subset of the theoretically 18-team strong sample, that is, the authors explore attendance

figures of the Hamburger Sportverein, exclusively.

15. It is also worth noting that, unlike stadiums in, for example, the EPL, German stadiums

often still have large terraced areas.

16. These supporting effects are, however, significant only at the 10% level (p < .10).

14 Journal of Sports Economics

17. Further, exploring the demand for a different sporting product, that is, Formula 1 Cham-

pionship Grands Prix, Schreyer and Torgler (2016) present some evidence that German

TV demand is shaped by race outcome uncertainty.

18. In addition, Alavy, Gaskell, Leach, and Szymanski (2010) and Cox (2015) present mixed

evidence in dis/support of Rottenberg’s UOH.

19. It should be noted that when the dependent variable is a fraction (as here), using an OLS

estimator is not necessarily the best choice.

20. Information on market share data is taken from the GfK, the official German provider of

TV usage data. Since 2012, the GfK panel has represented the German population with

roughly 5,000 households (approximately 10,500 residents; Arbeitsgemeinschaft Fernseh-

forschung, 2016). It is worth noting that the resulting data generated from multiple devices

in the respective household represent individual spectator rather than aggregated household

decisions, as each resident has to ‘‘sign-in/sign-out’’ by pressing a particular button.

Accordingly, female and male demand are technically independent from each other.

21. The corresponding data, that is, betting odds, were originally derived from https://foot

ball-data.co.uk and afterward transformed into the probabilities of a home win, draw, and

away win, respectively. We employ adjusted probabilities by excluding the bookmakers’

margin (cf. Benz, Brandes, & Franck, 2009).

22. The MARKET VALUE of, for example, Manchester City FC and Chelsea FC on

September 21, 2014, was 593 million € (mio €); Manchester City FC (296 mio €): Joe

Hart (16), Vincent Kompany (33), Eliaquim Mangala (28), Aleksandar Kolarov (12),

Pablo Zabaleta (19), Fernandinho (32), Yaya Toure´ (30), James Milner (14), David Silva

(40), Edin Dzeko (27), and Sergio Agu

ero (45); and Chelsea FC (297): Thibaut Courtois

(25), Gary Cahill (20), John Terry (6), Ce´sar Azpilicueta (20), Branislav Ivanovic (17),

Nemanja Matic (26), Cesc Fa´bregas (40), Ramires (28), Willian (30), Eden Hazard (48),

and Diego Costa (37).

23. A game is considered to be broadcasted in parallel if both kickoffs occur within 45 min.

24. It is worth noting that some authors interested in exploring TV audiences’ football

demand have included an analysis of the so-called broadcaster’s choice in their article

(e.g., Buraimo & Simmons 2015; Forrest et al. 2005; Forrest, Simmons, & Buraimo,

2006; Garcia & Rodriguez, 2006), while others have not (Alavy, Gaskell, Leach, &

Szymanski 2010; Buraimo 2008; Buraimo & Simmons 2009; Cox, 2015; Johnsen &

Solvoll, 2007; Kuypers, 1996). In order to address potential concerns as to whether this

broadcaster’s choice is in fact affected by GOU, we contacted Sky and were informed

that, in a first step, the EPL itself determines a subset of EPL games that are then offered

abroad. In a second step, the broadcaster can then choose a number of games from this

subsample (H. Gabbe, personal communication, August 11, 2016). Unfortunately, data

on this subsample are generally not available. Accordingly, we refrain here from further

discussing the broadcaster’s choice.

25. The development of both the male and female audiences’ EPL demand follows a similar

trend, peaking in season 2015-2016.

26. As an additional robustness test, we run a seemingly unrelated regression model followed

by a Wald test to directly evaluate the statistical difference between the male THEIL

Schreyer et al. 15

coefficient estimate and the female THEIL coefficient estimate. In line with our previous

analysis, we find a statistical difference (Wald w

¼ 4.91, p < .05).

27. This finding is also reflected in Table 4, which reveals that half of the 10 EPL fixtures

with the highest German TV demand during the five seasons 2011-2012 and 2015-2016

were scheduled during this break. Extended specifications are available from the authors

upon request.

28. It is worth noting that, as one reviewer has argued, the broadcaster’s choice of games to

televise may lead to a selection effect as Sky chooses from a restricted subset of EPL

games. Possible differences in the characteristics of televised and nontelevised games

may affect the estimates of the models, although it is unclear whether the main conclu-

sions are affected by it. Unfortunately, we are not able to model the broadcaster’s choice

applying, for example, a Heckman selection model (see Heckman, 1979) which may offer

more reliable estimates. Unfortunately, the restricted subset of games are not available to

the authors.

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Author Biographies

Dominik Schreyer is an assistant professor at WHU–Otto Beisheim School of Management

and associated with the Center for Sports and Management (CSM). He explores the role of

sociopsychological factors in individual (economic) behavior and decision-making through

the lenses of professional sports. In addition, he takes a keen research interest in the analysis

of sports demand.

Sascha L. Schmidt is a full professor and director of the Center for Sports and Management

(CSM) at WHU–Otto Beisheim School of Management and research fellow of the Center for

Research in Economics, Management and the Arts (CREMA). His primary research interest

lies in the area of future of sports management and sports economics.

Benno Torgler is a professor of economics in the School of Economics and Finance, Queens-

land University of Technology, Australia, and research fellow of the Center for Research in

Economics, Management and the Arts (CREMA). His primary research interest lies in the

area of economics, but he has also published in journals with a political science, social

psychology, sociology, and biology focus.

20 Journal of Sports Economics