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Stadium Attendance Demand Research: A Scoping Review

March 2021
Journal of Sports Economics

DOI:10.1177/15270025211000404

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Authors:

Dominik Schreyer

WHU – Otto Beisheim School of Management

Payam Ansari

Bournemouth University

Because maximizing stadium attendance demand is of utmost importance, for both sports economists and sport management researchers, understanding the potential determinants of such demand better has become a priority in the last decades. Here, conducting a systematic scoping review, we map this previous research in terms of its characteristics, its nature, and its volume, thus offering a concise perspective on what has been previously explored, and, more importantly, what remains to be analyzed in the future. Intriguingly, we observe a lack of studies exploring data generated in both niche and women’s sports, as well as in most emerging markets. Further, the field has not yet established the use of disaggregated stadium attendance data, despite notable potential methodological pitfalls.

Flow diagram of the systematic literature search process.

…

Development of study frequency over time.

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Development of study frequency by sport over time.

…

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Available via license: CC BY 4.0

Content may be subject to copyright.

Original Research Article

Stadium Attendance

Demand Research:

A Scoping Review

Dominik Schreyer

and Payam Ansari

Abstract

Because maximizing stadium attendance demand is of utmost importance, for both

sports economists and sport management researchers, understanding the potential

determinants of such demand better has become a priority in the last decades. Here,

conducting a systematic scoping review, we map this previous research in terms of

its characteristics, its nature, and its volume, thus offering a concise perspective on

what has been previously explored, and, more importantly, what remains to be

analyzed in the future. Intriguingly, we observe a lack of studies exploring data

generated in both niche and women’s sports, as well as in most emerging markets.

Further, the field has not yet established the use of disaggregated stadium attendance

data, despite notable potential methodological pitfalls.

Keywords

attendance, demand, football/soccer, major league baseball (MLB), major league

soccer (MLS), national basketball association (NBA), national football league (NFL),

national hockey league (NHL), outcome uncertainty, spectator sports

For executives operating in the field of professional sports, maximizing stadium

attendance demand is of utmost importance. On the one hand, generating revenue

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

Germany

Independent Researcher, Esfahan, Iran

Corresponding Author:

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

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

Email: dominik.schreyer@whu.edu.

Journal of Sports Economics

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DOI: 10.1177/15270025211000404

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from ticket sales and subsequent catering and parking fees contributes to the finan-

cial resources necessary for investing in talent and, thus, keeping up with the com-

petition. For major European professional football clubs, for instance, matchday

income still corresponds to roughly 15%of the clubs’ total annual turnover

(Deloitte, 2021), despite an increasingly important role of media income.

On the

other hand, as stadium spectators, applauding, booing, and chanting, are an integral

part of the product offered to other customers (cf., Morrow, 1999), a high(er) number

of stadium attendances caters to the direct needs of a sporting club’s many external

stakeholders, including broadcasters, corporate sponsors, and also those customers

in the hospitality section, all of whom benefit from an enhanced stadium atmosphere

(e.g., McDonald, 2010). Similarly, an underutilized stadium is likely to reduce future

visiting intentions among potential spectators, watching a match from their home

(e.g., Oh et al., 2017). On the field, maximizing the number of attending home fans,

in turn, might help the host to generate a significant home advantage (e.g., Reade

et al., 2020a). Further, off the field, an underutilized stadium might not only lead to

inefficient staffing but also to lower merchandising sales. As football clubs are

increasingly interested in diversifying their income sources (cf., Schmidt & Holz-

mayer, 2018), some clubs might also benefit from auxiliary revenues generated

through, for example, hotel stays, museum visits, and stadium tour bookings if

attendance demand increases. Therefore, it is not surprising that information on

stadium attendance demand is considered an approximation for a sporting club’s

reputation by investors, thus influencing the club’s stock market price (e.g., Gimet &

Montchaud, 2016).

For both sports economists and sport management researchers, understanding the

potential determinants of stadium attendance demand better has, therefore, become a

priority in the last decade(s). Rottenberg (1956), in his pioneering article on the

baseball players’ labor market, was first to offer a detailed demand specification (cf.,

Fort, 2005), including already factors as diverse as the ticket price, potential sub-

stitutes, and, perhaps most controversially, competitive balance and the resulting

match outcome uncertainty. Since then, sports economists, in particular, have

extended and tested this original demand specification in various markets, and, as

a consequence, today, there already exists a massive, continuously growing body of

empirical literature on those factors potentially shaping consumer interest in profes-

sional sports. Somewhat surprisingly, though, there has been made no recent attempt

to survey this previous work.

Here, conducting a systematic scoping review, we map this previous empirical

research on stadium attendance demand in terms of its characteristics, nature, and

volume (cf., Arksey & O’Malley, 2005), thus offering a concise perspective on what

has been previously explored, and, more importantly, what remains to be analyzed in

the future. While there already exist important literature reviews on the determinants

of stadium attendance demand (e.g., Borland & MacDonald, 2003; Cairns et al.,

1986; Downward et al., 2009), to the best of our knowledge, the last comprehensive

2Journal of Sports Economics XX(X)

review was published over a decade ago (e.g., Villar & Guerrero, 2009). Further,

these early reviews, despite their evident merits, have all been limited in scope, as

they typically tend to focus on summarizing the robustness of some previously

observed effects rather than to present a more holistic overview across the two

disciplines. Intriguingly, conducting such a scoping review is still relatively rare

in sports economics and/or management research (e.g., Dowling et al., 2018),

despite its growth in popularity in loosely related fields such as public health and

health care services research.

In sum, we observe that most authors, concentrating primarily on established

sports such as baseball and football, have so far refrained from exploring numerous

sporting environments, perhaps most notable niche and women’s sports, both of

which are likely to benefit significantly from understanding those factors shaping

stadium attendance demand better. Further, we find that the field has not yet estab-

lished the use of disaggregated stadium attendance data, despite potential methodo-

logical pitfalls (cf., Forrest et al., 2005). We believe that addressing these important

issues offers an exciting path for future research exploring the robustness of previous

findings across alternative settings.

Methodological Approach

In emerging research fields such as the economics of sports, conducting a scoping

review is helpful in identifying, locating, and, then, synthesizing the existing knowl-

edge (e.g., McKinstry et al., 2014; Tricco et al., 2016, 2018). In line with most such

previous reviews, here, our objective is not only to explore the extent, range, and

characteristics of the existing literature, thus presenting a concise overview of cur-

rent key themes, but, more importantly though, to also identify the remaining knowl-

edge gaps within. In contrast, and somewhat different to systematic literature

reviews, we refrain from assessing research quality per se (cf., Dowling et al., 2018).

The broader empirical stadium attendance literature is quite heterogeneous. That

is, not only in terms of the many objects under investigation, that is, the many sports

in the various markets, and the thematic themes in focus during these investigations,

but also the different methodological approaches.

To map the resulting stadium attendance demand research landscape, here, we

conduct a scoping review by loosely adopting to the well-established Preferred

Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping

Reviews (PRISMA-ScR) approach developed by Tricco et al. (2018). More specif-

ically, in the following, we, first, clarify our search approach; second, present our

eligibility criteria; third, explain our study selection; and, forth, give an overview on

both our data charting process, as well as the resulting data items.

Schreyer and Ansari 3

Information Sources and Search

To identify an eligible set of articles on stadium attendance demand, on January 20,

2020, we conducted a search in three multidisciplinary databases, the Web of Sci-

ence (WoS), Scopus, and SPORTDiscus. We derived our search string primarily

based on our previous reading of the existing stadium attendance demand research.

Then, we performed a search on manuscript title, abstract, and, where existing,

author keywords. Initially, this resulted in a total of 1,015 documents: 222 in the

WoS, 416 in Scopus, and another 377 in SPORTDiscus. We extracted these results

and compiled a Microsoft Excel spreadsheet, where we, using an existing function,

removed 262 duplicates. Because we had chosen a relatively broad search string, we

then conducted a systematic screening of both the titles and the abstracts of all the

remaining 753 manuscripts, thereby eliminating literature that was obviously not

focusing on stadium attendance demand research. After this step, our data set con-

tained 277 manuscripts, that is, we had excluded 476 documents.

Eligibility Criteria and Study Selection

In an initial screening round of these 277 manuscripts, we excluded 19 items. More

specifically, we excluded nine manuscripts because they were missing/not available

to us, seven articles because they were conference proceedings, and three articles

because they were not written in the English language.

We, then, screened the remaining 258 manuscripts according to three exclusion

criteria intended to help us identify those empirical studies modeling the stadium

attendance demand on matchday. First, as the use of survey data is often criticized

for lacking explanatory power of actual behavior (e.g., Katz et al., 2019), we not

only excluded 16 studies without an empirical contribution but also those 109 studies

based on either qualitative interviews or employing survey data modeling various

dependent variables including season ticket holder retention intention (e.g., Lee

et al., 2020), spectator preferences (e.g., Wang et al., 2018) or sport event attendance

motives (e.g., Funk et al., 2009). Second, to ensure that those studies in our data set

were highly comparable, we only considered contributions from authors who prox-

ied stadium attendance demand using the number of tickets distributed by a profes-

sional sporting club,

thus excluding more recent attempts to move towards

behavioral stadium attendance demand by exploring, for example, data on stadium

admission (e.g., Sacheti et al., 2016; Schreyer et al., 2016, 2018) or spectator no-

show behavior (e.g., Frevel & Schreyer, 2020; Schreyer, 2019; Schreyer et al.,

2019).

Similarly, we also excluded studies exploring the descendants of stadium

attendances such as the potential economic impact (e.g., Roberts et al., 2016), the

home advantage (e.g., Harris & Roebber, 2019), the willingness-to-pay (e.g., San-

ford & Scott, 2016), and studies examining alternative proxies for demand such as

TV audiences (e.g., Caruso et al., 2019; Humphreys & Perez, 2019; Schreyer et al.,

2017). In sum, in this second step, we excluded another 33 manuscripts. Third, to

further increase study design conformity, we excluded all 24 studies that were not

4Journal of Sports Economics XX(X)

modeling matchday demand.

As such, after the exclusion of a total of 182 manu-

scripts, our temporary data set contained 76 studies modeling stadium attendance

demand on the primary market as proxied by behavioral intentions to attend a

particular match; that is, by analyzing stated rather than revealed preferences

(e.g., Singleton et al., 2021).

Then, to not ignore any relevant literature, we performed both backward and

forward citation search, thus screening both citations and reference lists of those

76 articles and identified 88 additional peer-reviewed articles that would fit our

eligibility criteria. While the number of additional references seems rather high and,

thus, might cast doubt on our initial identification strategy, there’s an easy explana-

tion for that. That is, in fact, we would have been able to identify a fair share of these

88 manuscripts while using “attendance” as a keyword in our initial search but

refrained from doing so, as this would also have given back an almost unmanageable

amount of unrelated publications referring to, for example, church attendance (e.g.,

Azzi & Ehrenberg, 1975), student attendance (e.g., Epstein & Sheldon, 2002), and,

perhaps most prominently, work/employee attendance (e.g., Steers & Rhodes,

1978).

Finally, we cross-checked our already comprehensive data set against the promi-

nent earlier literature reviews from Borland and MacDonald (2003), Cairns et al.

(1986), Downward et al. (2009), and Villar and Guerrero (2009), and also against a

short, more recent book chapter on the economics of attendance published in the

SAGE Handbook of Sports Economics (cf., Rodr´ıguez, 2019). In this last step, we

found six additional manuscripts that met our selection criteria and, then, also

scanned available Google scholar profiles of the most active authors in our tempo-

rary sample (cf., Table 1),

identifying another 10 articles. Further, we added five

either rather recent or surprisingly little cited studies, Cairns (1984), Feng et al.

(2018), Gitter (2017), Goller and Krumer (2020), and Krumer (2020), that we were

aware of from previous research projects, but that, for some reasons, did not emerge

from our exhaustive identification approach. Because a quick keyword screening in

the Journal of Sports Economics (JSE), the International Journal of Sports Finance

(IJSF), Applied Economics (AE), Applied Economics Letters (AEL), European

Sport Management Quarterly (ESMQ), Managerial and Decision Economics

(MDE), and Sport, Business and Management (SBM), the seven international,

peer-reviewed journals with the highest share of manuscripts in our temporary

sample, did only result in the addition of ten more manuscripts, four of which were

published online first after our initial search (i.e., Gasparetto & Barajas, 2020; Jung

et al., 2020; Kelley, 2020; Mueller, 2020), we decided to end the identification

process in early August 2020.

In sum, our data set contains 195 published manuscripts on 2,918 pages.

How-

ever, as some authors (e.g., Paul et al., 2016; Pawlowski & Nalbantis, 2015; Tyler

et al., 2017), modeled the attendances of more than one league,

we decided to

include these studies separately if authors had made separate regressions available

for these different leagues. Therefore, to be more precise, our final data set contains

Schreyer and Ansari 5

Table 1. Most Productive Authors in Our Sample.

# Author Contributions (Single) Preferred sport(s), ...market(s), ...and theme(s)

1 Paul, R. 18 (1) Baseball (10), Hockey (7) USA (15) Fighting, promotions, winning

2 Buraimo, B. 11 (3) Football/Soccer (11) England (8) None, i.e., attendance modeling

Simmons, R. 11 (0) Football/Soccer (10) England (9) Match outcome uncertainty

Weinbach, A. 11 (0) Baseball (6), Hockey (4) USA (9) Fighting, promotions, winning

5 Humphreys, B. 9 (0) None, four different sports USA (9) Match outcome uncertainty

6 Rascher, D. 6 (1) None, four different sports USA (6) None, i.e., multiple questions

Watanabe, N. 6 (1) None, three different sports China (3), USA (3) None, i.e., multiple questions

8 Coates, D. 5 (0) None, four different sports USA (4) Match outcome uncertainty

Forrest, D. 5 (0) Football/Soccer (5) England (5) None, i.e., multiple questions

Thomas, D. 5 (0) Football/Soccer (3) England (4) Match outcome uncertainty

DeSchriver, T. 5 (1) Football/Soccer (4) USA (5) None, i.e., multiple questions

Note. Authors with a minimum of five empirical contributions to our sample;

In alphabetical order, theme based on title analysis.

235 empirical contributions/studies in 195 manuscripts.

In Figure 1, we summarize

our complete search process by providing a detailed flow chart.

Data Collection Process, Resulting Data Items and Synthesis of Results

For all these 195 manuscripts, we read the title, the abstract, and the methodological

section in the necessary detail and extracted several data points. In addition, as we

will describe below, in all these manuscripts, we ran multiple searches for a wide

range of specific keywords.

In sum, we initially extracted 13 different data points, which we grouped as

follows: First, the number, names and gender of all authors as mentioned on the

title page;

second, the title of the manuscript; third, the name of the journal that had

published the manuscript, including the year of publication in print;

fourth, the

sport(s), league(s), and market(s) explored by the authors;

fifth, the observation

period, including the total number of matches/observations; sixth, the page count;

and seventh, whether the manuscript’s title page offered author keywords.

In addition, at a later stage of the data collection process, we also extracted more

detailed methodological information. Primarily, we collected information on the

Figure 1. Flow diagram of the systematic literature search process.

Schreyer and Ansari 7

dependent variable(s), including information on its composition, for example,

whether the authors discussed and/or addressed limitations arising from the distri-

bution of free and/or season tickets, and also the methodological approach. More

specifically, we were interested in whether, and if so how, the authors addressed the

problem of capacity constraints in markets where demand frequently (or occasion-

ally) exceeds supply.

After this initial data collection process, we added only few supplementary infor-

mation to the data set. First, for all manuscripts exploring European football, for

instance, we added information on whether a study explored cup or league football,

along with the respective information on the observed level of the football pyramid.

Here, those studies exploring English Premier League data, for example, were

labeled as “football league” on level “1” of the English football pyramid. In this

respect, Spanish La Liga, German Bundesliga, Italian Serie A and French Ligue 1

are all coded the same, while a study analyzing stadium attendance demand for

English Championship football would be labeled as “football league” on level

“2”. In contrast, those manuscripts exploring data from either domestic or interna-

tional tournaments were not coded as analyzing a football league. Second, to gain an

understanding on the most influential manuscripts, we also added information of the

total accumulated citation as of August 31, 2020, using Google Scholar.

Results and Discussion

Influential Authors, Well-Cited Manuscripts, and Potential Target Journals: A

Matthews Effect?

In Table 1, we first provide an overview of the 11 most productive authors operating

in the field of stadium attendance research, that is, according to our scope. In sum,

our database contains 297 different authors, most of which, about 80%, however

only contributed one manuscript to this particular literature stream. Interestingly, as

can also be seen from this table, most of these authors seem to have developed both a

specific profile, that is, an interest in some key sports and markets, sometimes even

particular research questions, and, not shown in the table, an established set of

natural co-authors over time (e.g., Paul/Weinbach: 11; Buraimo/Simmons: 6; Wata-

nabe/Soebbing: 4).

Intriguingly, these 11 authors contributed to 64 different manuscripts and, thus,

generated a total of 3,577 citations. As such, these highly productive authors, being

responsible for about one third of all manuscripts in our sample, account for roughly

35%of all accumulated citations. It is, however, worth noting that only about 11 out

of the 25 most-cited manuscripts stem from this particular group of authors, most

probably because a fair share of 17 of these 64 contributions were published rather

recently, that is, between 2016 and 2020. In fact, as can be seen from Table 2, among

the most frequently cited publications in our data set, only two manuscripts were

8Journal of Sports Economics XX(X)

Table 2. Most Frequently Cited Manuscripts in Our Database.

# Author Year Short title Journal Citations

Citations/

Year

Citations/Total

citations

1 Garc´

ıa/Rodr´

ıguez 2002 The determinants of football match attendance revisited JSE 393 21 3.87%

2 Forrest/Simmons 2002 Outcome uncertainty and attendance demand in sport JRSSD 377 20 3.71%

3 Baimbridge et al. 1996 Satellite television and the demand for football SJPE 336 13 3.31%

4 Szymanski 2001 Income inequality, competitive balance and the attractiveness of

team sports

EJ 335 17 3.30%

5 Knowles et al. 1992 The demand for Major League Baseball TAE 300 10 2.95%

6 Jennett 1984 Attendances, uncertainty of outcome and policy in Scottish league

football

SJPE 281 8 2.77%

7 Czarnitzki/Stadtmann 2002 Uncertainty of outcome versus reputation EE 257 14 2.53%

8 Peel/Thomas 1992 The demand for football EE 246 8 2.42%

9 Peel/Thomas 1988 Outcome uncertainty and the demand for football SJPE 245 7 2.41%

10 McDonald/Rascher 2000 Does bat day make cents? JSM 218 10 2.15%

11 Forrest/Simmons 2006 New issues in attendance demand JSE 180 12 1.77%

12 Buraimo/Simmons 2008 Do sports fans really value uncertainty of outcome? IJSF 180 14 1.77%

13 Borland/Lye 1992 Attendance at Australian Rules football AE 171 6 1.68%

14 Hart et al. 1975 A statistical analysis of association football attendances JRSSC 170 4 1.67%

15 Buraimo/Simmons 2009b A tale of two audiences JEB 166 14 1.63%

16 Falter/Perignon 2000 Demand for football and intramatch winning probability AE 164 8 1.61%

17 Buraimo 2008 Stadium attendance and television audience demand in English

league Football

MDE 158 12 1.56%

18 Hill et al. 1982 The short run demand for Major League Baseball AEJ 156 4 1.54%

19 DeSchriver/Jensen 2002 Determinants of spectator attendance at NCAA Division II football

contests

JSM 154 8 1.52%

20 Pawlowski/Anders 2012 Stadium attendance in German professional football AEL 152 17 1.50%

21 Marcum/Greenstein 1985 Factors affecting attendance of major league baseball SSJ 150 4 1.48%

22 Cairns 1987 Evaluating changes in league structure AE 148 4 1.46%

(continued)

Table 2. (continued)

# Author Year Short title Journal Citations

Citations/

Year

Citations/Total

citations

23 Forrest et al. 2004 Broadcasting, attendance and the inefficiency of cartels RIO 148 9 1.46%

24 Rascher 1999 A test of the optimal positive production network externality in

Major League Baseball

Book 136 6 1.34%

25 Coates et al. 2014 Reference-dependent preferences, loss aversion, and live game

attendance

EI 130 19 1.28%

Note. In sum, we count 33 manuscripts with a minimum of 100 citations, that is, according to Google Scholar. All numbers are rounded. AE/L ¼Applied Economics/Letters; AEJ

¼Atlantic Economic Journal; Book ¼Book chapter; EI ¼Economic Inquiry;EJ¼Economic Journal;EE¼Empirical Economics; IJSF ¼International Journal of Sport Finance;

JEB ¼Journal of Economics and Business;JSM¼Journal of Sport Management;JSE¼Journal of Sports Economics; JRSSC/D ¼Journal of the Royal Statistical Society:Series C/D;

MDE ¼Managerial and Decision Economics;RIO¼Review of Industrial Organization; SJPE ¼Scottish Journal of Political Economy; SSJ ¼Sociology of Sport Journal; TAE ¼The

American Economist.

published later than in 2010 (Coates et al., 2014; Pawlowski & Anders, 2012), while

eleven, that is, almost half of them, were published before the year 2000.

What is more, we observe a notable absence of female authors from this group of

highly productive authors. Somewhat similarly, broadening our scope beyond this

particular group, only roughly 15%of all those authors included in our data set are

female, most of which, 40 out of 44, have only contributed to the literature once. As

such, the vast majority of all 195 contributions, about 78%, lack a female contribu-

tion, some might even argue perspective, which, at least to a certain degree, might

help to understand the somewhat limited scope in the previous research better.

In Table 2, we present an overview of the most frequently cited publications in

our data set. Intriguingly, these 25 publications alone account for about 53%of all

counted citations. In fact, we observe that there seem to exist a few, apparently well-

known manuscripts that are, on average, cited much more frequently than others.

That is, while all 195 manuscripts in our database were, on average, cited about four

times per year, the work of six author teams accumulated, on average, more than 15

citations per year (e.g., Garc´ıa & Rodr´ıguez, 2002; Forrest & Simmons, 2002; Cox,

2018; Coates et al., 2014; Pawlowski & Anders, 2012; Szymanski, 2001), three of

them for a period of about 20 years.

In contrast, we count a total of 47 manuscripts,

roughly a quarter of all manuscripts, that were, on average, cited once per year at

most since their publication in print.

As indicated earlier, for those authors exploring the potential determinants of

stadium attendance demand there seem to exist a handful of preferred publication

outlets. In fact, we observe that 75 out of all 195 manuscripts, that is, about 38%of

our sample, were published in only four journals: The JSE (31 manuscripts) and the

IJSF (18), that is, the two journals dedicated to publishing research in the field of

sports economics, AE (15), and AEL (11). Further, we observe that a significant

number of manuscripts were published in ESMQ (6), MDE (6), and, somewhat

surprisingly, also in the form of a book chapter (9; e.g., Buraimo, 2014). Two

dedicated sport management journals, SBM (5) and the Journal of Sport Manage-

ment (5), take the next two places.

In all 76 different outlets,

the average page

count was 15 pages, though this is naturally also dependent on journal formatting. In

this regard, we observe no significant trend in the number of pages per contribution

per year, recently; that is, even when excluding the one journal, AEL, exclusively

publishing short letters.

Dominant and Emerging Markets of Investigation: European Football

Dominates Our Sample, No Previous Interest in Modeling Niche Sport

Demand

In Figure 2, we present an overview of the development of stadium attendance

demand publications over time. As can be easily seen from that figure, research

interest in empirical studies analyzing matchday demand has gradually increased in

recent years.

Schreyer and Ansari 11

Naturally, as can be inferred from Figure 2, this gradual increase in the number of

publications on stadium attendance demand research is closely associated with the

foundation of the JSE in 2000, which, over time, became a natural home for authors

exploring the determinants of stadium attendance demand. In fact, according to our

database, since 2006, the journal has had at least one such manuscript in print per

year, sometimes even three (e.g., in 2012: Beckmann et al., 2012; Coates &

Humphreys, 2012; Leeds & Sakata, 2012) or more (e.g., in 2018: Cox, 2018;

Gropper & Anderson, 2018; Lewis & Yoon, 2018; Martins & Cr ´o, 2018). Accord-

ingly, between 2000 and August 2020, about every fifth empirical study modeling

stadium attendance demand was published in the JSE.

Interestingly, the authors of roughly every second study explored spectator

demand in the US market (112), followed by the United Kingdom (47), Germany

(8), Australia, France, and Scotland (6). In addition, 22 countries are represented at

least once, while we also observe four manuscripts featuring data from international

tournaments (e.g., Chiang & Jane, 2013; Krumer, 2020; Valenti et al., 2020), and

one manuscript in which the authors analyzed aggregated data generated in multiple

countries/leagues (Serrano et al., 2015).

In Table 3, we present a concise overview of all competitions in our data set. In

sum, we note 195 manuscripts featuring a total of 235 studies on competitions in

13 different sports, including Hockey (e.g., Coates & Humphreys, 2012), Ultimate

Fighting (e.g., Watanabe, 2015), Handball (Storm et al., 2018), NASCAR racing

(Berkowitz et al., 2011) and Tennis (Chmait et al., 2020), amongst others. Despite

this apparent diversity, it is, however, interesting that there seems still to exist no

manuscript exploring the spectator demand for such otherwise popular sports as

Athletics, Badminton, Boxing, Cycling, Golf, Field hockey, Formula 1 racing, Gym-

nastics, Skiing, Snooker, Swimming, and Volleyball.

As most of those associa-

tions managing the aforementioned sports are still heavily dependent on generating

matchday income, this observation is not only a bit surprising but it also offers an

Figure 2. Development of study frequency over time.

12 Journal of Sports Economics XX(X)

Table 3. Dominant Sports and Sport Leagues in Stadium Attendance Demand Research.

Sport # Tier # Exemplary Keyword(s) # Exemplary Associated Reference

Football/Soccer 107 First 76 English Premier League 17 Hart et al. (1975)

Major League Soccer 9 Sung/Mills (2018)

French Ligue 1 6 Falter et al. (2008)

German Bundesliga 6 Pawlowski/Anders (2012)

Chinese Super League 5 Watanabe/Soebbing (2015)

Spanish La Liga 5 Buraimo/Simmons (2009b)

Scottish Premier League 5 Cairns (1984)

Italian Serie A 5 Di Domizio/Caruso (2015)

Remaining 18 Pawlowski/Nalbantis (2015)

Second 10 English Championship 9 Forrest/Simmons (2006)

Irish First Division 1 Jena/Reilly (2016)

Third 5 English Football League One 5 Peel/Thomas (1988)

Fourth 6 English Football League Two 5 Peel/Thomas (1992)

German Regionalliga 1 Wallrafen et al. (2019)

Fifth 1 English National League 1 Buraimo (2014)

Multiple (aggregated) 5 Serrano et al. (2015)

Cup 4 Baimbridge (1997)

Baseball 64 Major League Baseball 32 Coates et al. (2014)

Minor League Baseball 23 Paul/Weinbach (2013)

Other leagues 9 Gitter (2017)

American Football 22 National Football League 5 Watanabe/Cunningham (2020)

College Football 17 Falls/Natke (2014)

Hockey 19 National Hockey League 8 Coates/Humphreys (2012)

Remaining 11 Paul et al. (2016)

Basketball 7 National Basketball Association 6 Jane (2016)

College Basketball 1 McEvoy and Morse (2007)

Rugby 5 Super League 3 Baimbridge et al. (1996)

Remaining 2 Hogan et al. (2017)

Other

11 Diverse 11 Watanabe (2015)

Category subsumes manuscripts on Australian Rules Football (3), Cricket (2), Handball (1), Horse racing (1), NASCAR Racing (1), Tennis (1), and Ultimate Fighting (2).

interesting path for future stadium attendance demand research. Further, to the best

of our knowledge, there as yet exists no study on stadium, or perhaps better hall/

venue, attendance demand for electronic sports, including League of Legends,

Defense of the Ancients (Dota) 2, and Counter-Strike: Global Offensive, despite

an increasing global interest in these emerging sports, in particular among the youth.

As can be easily seen from that table, about half of all 235 studies centered on

stadium attendance demand for European football. Only three more sports, Baseball

(64) and, already far behind, American Football (22) and Hockey (19), attracted

notable interest from the group of authors analyzing stadium attendance demand.

Apparently, as we show in Figure 3, this gap is likely to widen in the future, as the

field’s interest in studying football stadium attendances appears to be increasing

successively, and has only peaked recently; that is, in 2018.

Interestingly, in European football, most authors have chosen to explore English

football data. More specifically, we counted a total of 42 studies,

most of which

used data generated in either the English Premier League (17 studies), including its

predecessors, or the English Football League (EFL) Championship (9). In contrast,

only a few authors explored data from the EFL One (5), the EFL Two (5) or even the

National League (1). It is worth noting, however, that these three lower-tier leagues

never rise beyond a mere supporting role in those manuscripts featuring them, as

they are, without any exception, merely added as a supplement to the initial analysis

of the two top-tier leagues in the country. Further, in this particular environment,

manuscripts that center around domestic (1; Szymanski, 2001) or international

Figure 3. Development of study frequency by sport over time.

14 Journal of Sports Economics XX(X)

tournaments (1; Baimbridge, 1997) are still scarce, as are manuscripts exploring

women’s football demand (0).

Surprisingly, the narrow analytical interest of those authors modeling English

football stadium attendance demand (e.g., Cox, 2018; Jewell, 2011; Walker, 1986) is

largely representative for what we observe among the work of those authors analyz-

ing the remaining football markets. That is, in all football markets represented in our

sample, we note a lack of studies modeling spectator demand for domestic cup

competitions, lower-tier football, including amateur football, women’s sports, and

also youth football.

Here, Wallrafen et al. (2019), modeling fourth-tier stadium

attendances in Germany, as well as both Meier et al. (2016) and LeFeuvre et al.

(2013), modeling stadium attendance demand for women’s football in Germany and

France, respectively, are notable exceptions that all prove this rule.

While most authors have explored English Premier League data, in general, we

observe a keen interest in European first-tier football leagues, as well as in both

Major League Soccer (MLS) and the Chinese Super League. More specifically, we

note a strong interest in analyzing stadium attendance demand for the MLS (9 manu-

scripts), the German Bundesliga and the French Ligue 1 (6), the Chinese Super

League, the Italian Serie A, the Scottish Premier League, and the Spanish La Liga

(5). Further, reflecting the many different facets of the previous research, there

already exist empirical studies on markets as diverse as Austria (Pawlowski &

Nalbantis, 2015), Brazil (e.g., Madalozzo & Berber Villar, 2009), Denmark (Nielsen

et al., 2019), Finland (Iho & Heikkila¨, 2010), Malaysia (Wilson & Sim, 1995), Peru

(Buraimo et al., 2018), and Russia (Coates et al., 2017). In contrast, evidence from

emerging football markets such as Colombia, Egypt, India, Indonesia, Morocco and

the Philippines are currently largely missing from the English-language literature.

Also, there’s still no manuscript on football stadium attendance demand in smaller

European markets such as Czechia, Hungary, Poland, Portugal and Romania, and

also on future FIFA World Cup hosts such as Qatar, Mexico and Canada.

Dominant and Emerging Manuscript Themes: Outcome Uncertainty, Star

Power, and ...Air Pollution

To understand the existence of both dominant and emerging themes in the already

rich stadium attendance demand literature better, we performed a systematic manu-

script title analysis. More specifically, we first went through all 195 manuscript titles

and, based on the information presented in these titles, decided whether (or not)

authors of a given manuscript were focusing on one or more specific determi-

nant(s).

Then, second, whenever we observed such an author focus, we extracted

the appropriate keywords. This somewhat cumbersome procedure was necessary

because the use of keywords is a relatively recent development, the JSE, for exam-

ple, only introduced the use of keywords in 2003, and even today, still not all

publishers have decided to implement them. Having extracted a total of 189

keywords, we categorized all of them based on whether the determinants were

Schreyer and Ansari 15

proxying consumer preferences (e.g., club characteristics, habit persistence, and

exogenous shocks such as a public health crisis), economic factors (e.g., macroeco-

nomic factors, market characteristics, and the ticket price), the quality of viewing

(e.g., promotions, quality of seating and the timing of the contest), or the sporting

contest (i.e., the quality of the contest and, in particular, the role of competitive

balance and the resulting match outcome uncertainty) – as such, here, our approach

closely resembles that of Borland and MacDonald (2003). Further, to increase the

robustness of the chosen approach, in Figure 4, we also provide two word-clouds

generated from both the title and the abstract of the 195 manuscripts in our data set.

In Table 4, we present both the descriptive information on the distribution of the

extracted keywords and illustrative examples. Interestingly, we observe that the

authors of about three out of four studies seem to have drafted their manuscripts

with a specific theme in mind. More specifically, most authors focused on the

potential role of the sporting contest characteristics, most notably match outcome

uncertainty, in shaping stadium attendance demand. That is, we observe that the

titles of roughly every fourth study include a reference to the concept of competitive

balance and the resulting match outcome uncertainty. Interestingly enough, these

references are surprisingly diverse, and include, for example, championship uncer-

tainty (e.g., Pawlowski & Nalbantis, 2015), competitive intensity (e.g., Bond &

Addesa, 2020), loss aversion (e.g., Besters et al., 2019), outcome uncertainty

(e.g., Martins & Cr´o, 2018), uncertainty (e.g., Serrano et al., 2015), uncertainty of

outcome (e.g., Hogan et al., 2017), and uncertainty of results (e.g., Cox, 2018).

Further, as the debate on the validity of Rottenberg’s (1956) so-called uncertainty of

outcome hypothesis has not yet been laid to a final rest, we note a relatively con-

sistent output of such studies in the last decade; that is, in 2010 (3), 2011 (1), 2012

(4), 2013 (4), 2014 (2), 2015 (4), 2016 (2), 2017 (1), 2018 (4), 2019 (2), and 2020 (3).

Somewhat similarly, we observe a continuous interest in the potential role of stars,

including, for example, local heroes (Yamamura, 2011), marquee players (Jewell,

2017), star pitchers (Ormiston, 2014), and top-drafted rookies (Kelley, 2020), in

modeling stadium attendance demand. In contrast, we only count a few studies that

concentrate on team success (e.g., Paul et al., 2019; Pinnuck & Potter, 2006; Wata-

nabe & Soebbing, 2017) and contest significance.

Although most authors centered their manuscripts around sporting event charac-

teristics, some authors have emphasized the potential role of economic factors, the

quality of viewing, and also emerging consumer preferences in explaining the var-

iance in stadium attendance demand. As such, there already exists a significant body

of literature on the question of whether television broadcasts serve as a substitute for

stadium attendance (e.g., S. Allan, 2004; Baimbridge et al., 1996; Barajas et al.,

2019; Kringstad et al., 2018; Nielsen et al., 2019), whether promotions such as

bobblehead giveaways, fireworks, and even marching bands, are effective in

increasing stadium attendance demand (e.g., Boyd & Krehbiel, 2003; Kappe

et al., 2014; McDonald & Rascher, 2000; Natke & Thomas, 2019; Paul et al.,

2013), and whether consumer preferences alter in the aftermath of exogenous shocks

16 Journal of Sports Economics XX(X)

Figure 4. Dominant themes in stadium attendance demand research. Note. We generated these two word-clouds from the title (left) and the

abstract (right) of the 195 manuscripts in our data set using the services from monkeylearn.com because it automatically detects multiword

keywords. Here, string size corresponds to string frequency.

Schreyer and Ansari 17

Table 4. Dominant Themes in Stadium Attendance Demand Research.

Manuscript theme # # # Exemplary Keyword(s) Exemplary Associated Reference

Manuscripts with specific themes 143

Consumer preferences 16

Exogenous shocks 8 H1N1 Gitter (2017)

Club characteristics 5 Reputation Czarnitzki/Stadtmann (2002)

Habit formation, persistence 3 Habit formation Ge et al. (2020)

Economic factors 36

Availability of substitutes 14 Substitution Wallrafen et al. (2019)

Market composition, size 10 Market size Buraimo/Simmons (2009a)

Economic impact, legacy 4 World cup effect LeFeuvre et al. (2013)

Macro-economic factors 3 Economic crisis Hong et al. (2013)

Ticket price 3 Pricing Watanabe/Soebbing (2017)

Travel cost 2 Fan travel Humphreys/Miceli (2020)

Quality of viewing 30

Quality of stadium 20 Promotions Cebula et al. (2009)

Timing of the contest 9 Weather Cairns (1984)

Kick-off times Krumer (2020)

Air Pollution Watanabe et al. (2019)

Quality of seating 2 Soccer-specific stadiums DeSchriver et al. (2016)

Characteristics of the sporting contest 82

Uncertainty of outcome 45 Outcome uncertainty Peel/Thomas (1988)

Competitive balance Coates/Humphreys (2010)

Competitive intensity Scelles et al. (2013)

Quality of the contest 30 Superstars Humphreys/Johnson (2020)

Rivalries Wooten (2018)

Violence Jones et al. (1993)

Success of competing teams 16 Team success Davis (2009)

Significance of the contest 4 Interleague play Butler (2002)

Manuscripts without specific themes 52

Note. Manuscript categorization is based exclusively on the manuscript title. Multiple allocations are allowed.

as diverse as a corruption scandal (Buraimo et al., 2016), an influenza virus outbreak

(Gitter, 2017), college football player protests (Watanabe et al., 2019), increasing

terror alert levels (Kalist, 2010), or the announcement of performance-enhancing

drug violations (Cisyk & Courty, 2017). Interestingly, though frequently included as

control variables, only a few authors have centered their study on the potential

effects of match fixtures/scheduling (Goller & Krumer, 2020; Krumer, 2020; Paul

et al., 2004) or both environmental effects and the weather (e.g., Cairns, 1984;

Ge et al., 2020; Ito et al., 2016). Among the latter, Watanabe et al. (2019), for

instance, explored the potential role of air pollution in shaping stadium attendance

demand in the Chinese Super League, thus paving an interesting new way towards a

better understanding of how spectators respond to potential health threats on

matchday – a theme that will certainly gain more importance in the future

(e.g., Reade et al., 2020b).

Methodological Challenges: A Logarithmic, Aggregated Dependent Variable

and Tobit Models

As is evident in the literature, those authors modeling stadium attendance demand

face several methodological challenges. Forrest et al. (2005), for example, noted two

such challenges potentially leading to biased estimates: first, the existence of capac-

ity constraints in leagues that are permanent in high demand, such as the English

Premier League, and, second, the use of aggregated data across different spectator

groups (e.g., matchday ticket holders and season ticket holders; Dobson & Goddard,

2011). However, these aggregated data typically subsume two different subsequent

decisions from multiple stakeholder groups, including not only paying customers but

also owners of free tickets, whose determinants might differ significantly: first, an

individual’s decision whether to accept/purchase a ticket for a match; and second,

the ticket holder’s subsequent decision whether to attend this particular match

(e.g., Schreyer et al., 2016).

Below, we summarize how previous authors have

addressed these two potential limitations.

In general, most authors explored aggregated attendance data, preferably in log

form (e.g., Chupp et al., 2007; Martins & Cr´o, 2018; Storm et al., 2018; Watanabe &

Soebbing, 2015; Yamamura, 2011). In fact, only a few authors were primarily

interested in analyzing alternative attendance demand proxies such as the stadium

utilization (i.e., the ratio of distributed tickets to the existing stadium capacity; e.g.

Hong et al., 2013; Jane, 2016; Lawson et al., 2008) or a rather simple sellout-dummy

(e.g., Brandes et al., 2013), although some authors used the former information to

present an additional robustness check (e.g., Paul et al., 2016).

Despite the early criticism from Forrest et al. (2005), only a few authors have

explored disaggregated attendance information. In fact, somewhat surprisingly, the

authors of only about 30 manuscripts discussed, often briefly, the potential limita-

tions that may arise from the use of such data generated across both matchday ticket

holders and season ticket holders (e.g., Barajas et al., 2019; Buraimo et al., 2018;

Schreyer and Ansari 19

Paul et al., 2019). As such, it is perhaps not surprising that the authors of only about a

handful of manuscripts explicitly explored the behavioral intentions of matchday

ticket holders (e.g., Allan & Roy, 2008; Benz et al., 2009; Bond & Addesa, 2020),

that is, by analyzing attendance data after having subtracted season ticket holder

data, or added such season ticket holder data as an explanatory variable (e.g., Chmait

et al., 2020).

Perhaps one potential reason for this is that most authors still wrongly

consider season ticket holders to be behavioral loyal (e.g., Schreyer et al., 2018).

Somewhat similarly, only a few authors either have excluded (e.g., Chmait et al.,

2020) or added information on free tickets (e.g., Anthony et al., 2014), and even

fewer considered this a limitation of their study (e.g., Besters et al., 2019).

In this particular context, it is interesting to see that there seems to exist only very

little research modeling the stadium attendance demand for different stadium sec-

tions (e.g., Dobson & Goddard, 1992), including, for example, home and away

sections and, economically more important, also business seats in the hospitality

sections; that is, those seats that, at least in European professional football, currently

account for about 62%of the ticket revenues (ESSMA, 2019). While such disag-

gregated data, that is, information on the number of season ticket holders, the

distribution of free/complimentary tickets, and/or sectoral differences, are, and will

still be, usually hard to obtain (e.g., Kringstad et al., 2018), we believe that the field

would benefit from a more detailed discussion of both the nature of the dependent

variable, typically proxying behavioral intentions to attend, and its potential limita-

tions in the future.

While only a few authors have explored disaggregated data, most authors expli-

citly addressed (and discussed) the potential problems arising from stadium capacity

constraints.

More precisely, we count that about 60%of all authors/author teams

addressed this important methodological issue, some even although it is, according

to their own statement, not an urgent issue in their specific environment (e.g., Forrest

& Simmons, 2006; Reilly, 2015; Watanabe, 2015); for example, because frequent

sellouts are less likely in developing markets and lower tiers. Those authors that,

however, observed a fair share of right-censored observations typically employ a

tobit model (e.g., Besters et al., 2019; Bond & Addesa, 2020; Cox, 2018), either

primarily or as an additional robustness check, although we also observe an increas-

ing use of censored regression (e.g., Hong et al., 2013; Meehan et al., 2007; Ormis-

ton, 2014), or the exclusion of potentially truncated cases (e.g., Denaux et al., 2011),

among others. In fact, only a few authors discussed capacity constraints as a limita-

tion without taking any methodological action against it.

On a side note, it is, perhaps, interesting that the explored period of investigation

has significantly increased over time, most likely due to improved data availability.

In this context, we observe, on average, a period of investigation of about 5.50

seasons/years per study, ranging from a minimum of one (e.g., Hill et al., 1982)

to a maximum of 48 (Fullerton & Miller, 2017). However, about 80%of all manu-

scripts in our data set feature a study exploring data of a maximum of five years/

seasons, most of them, about 43%, even of only one. On the other hand, we only

20 Journal of Sports Economics XX(X)

count ten manuscripts exploring data sets containing 20 or more years/seasons.

Further, and perhaps unsurprisingly, we observe a strong, positive and significant

correlation between the period of observation and the number of total, that is,

aggregated, analyzed matches in the data. In general, authors explored data sets

containing, on average, roughly 3,619 matches, though this mean is somewhat

artificially inflated by those authors exploring data generated in the two rather long

league tournaments, that is, in Major League Baseball (M¼11,077, SD ¼17,694)

and in the NBA (M¼14,065, SD ¼16,349). It is, therefore, not surprising that the

former competition is the focus of the manuscript with the largest data set, an

ultimate sample size of 88,825 matches (c.f., Ormiston, 2014), in our sample.

A Note on Stadium Attendance Demand Research in the

Journal of Sports Economics

As a cornerstone of the more recent stadium attendance demand research, the JSE,

according to our data, has published a total of 31 manuscripts modeling stadium

attendances. In general, and perhaps not surprisingly, these articles are largely

reflective of the discipline’s scope, as indicated above. For instance, we observe a

strong tendency to explore empirical data generated in the US market (18; e.g.,

Berkowitz et al., 2011; Butler, 2002; Humphreys & Johnson, 2020), and also Europe

(10; e.g., Falter et al., 2008, Garc´ıa & Rodr´ıguez, 2002; Martins & Cr ´o, 2018), but

almost no evidence from Africa, Asia, or South America. Somewhat similarly, about

84%of all manuscripts, analyzed data from baseball and football/soccer (13 manu-

scripts each), with only a handful of manuscripts exploring other sports; for instance,

basketball (e.g., Jane, 2016), hockey (Coates & Humphreys, 2012), and racing

(Berkowitz et al., 2011). As such, currently, about every third stadium attendance

demand study published in the JSE marks an exploration of the US baseball market

(e.g., Ge et al., 2020; Lemke & Tlhokwane, 2010; Ormiston, 2014).

Despite the apparent preference for analyzing MLB attendances, it is, however,

important to note that JSE has continuously published manuscripts broadening the

discipline’s previous scope. For instance, according to our sample, LeFeuvre et al.

(2013), analyzing the potential effect of the 2011 FIFA Women’s World Cup on

Women’s Professional Soccer attendance, were the first to explore stadium atten-

dance demand for women’s football. Wallrafen et al. (2019), primarily interested in

understanding substitution effects in sports better, were among the few authors

modeling stadium attendance demand for amateur football and the first to do so

outside of the English football market. In fact, many football markets, including

Brazil (Madalozzo & Berber Villar, 2009), Finland (Iho & Heikkila, 2010), and

Portugal (Martins & Cr´o, 2018), in particular, were explored first in the JSE.

In terms of dominant themes, we note that most JSE authors, roughly three out of

four, seem to have drafted their manuscript with a specific theme in mind, a third of

Schreyer and Ansari 21

them explicitly referring to the concept of competitive balance/intensity and the

resulting match outcome uncertainty (e.g., Andreff & Scelles, 2015; Cox, 2018;

Meehan et al., 2007). Therefore, it is perhaps not surprising that most manuscripts

published in the JSE center around the effect of sporting characteristics on spectator

interest, with only a few manuscript titles explicitly referring to consumer prefer-

ences (e.g., Cisyk & Courty, 2017), economic factors (e.g., Wallrafen et al., 2019),

and the quality of viewing (e.g., Ge et al., 2020).

Methodologically, in the JSE, we observe a similar preference for exploring

aggregated attendance data across different spectator groups, preferably in the log

form. As such, only a handful of authors excluded season tickets from their sample

(e.g., Bond & Addesa, 2020; Falter et al., 2008; Garc´ıa & Rodr´ıguez, 2002), while

the potential existence of complementary/free tickets is typically not discussed. In

contrast, and once more in line with the field’s general scope, the vast majority of all

authors discussed (and addressed) the potential problems arising from stadium

capacity constraints.

Conclusions, Limitations, and Potential Future Research

Avenues

Throughout our analysis of the empirical literature modeling the behavioral inten-

tions to attend a sporting event in a stadium (or a hall/venue), we have noted several

emerging patterns that may serve as a starting point for future stadium attendance

research. Below we summarize these patterns.

First, we observed that, despite an already rich and continually growing body of

empirical literature modeling the determinants of stadium attendance research, there

are numerous sports that, for unclear reason(s), have so far been neglected from a

proper analysis. That is, while most authors have explored data generated in Amer-

ican football, baseball, European football and hockey, there as yet exists no research

on regional quite popular niche sports, including badminton, field hockey, and

volleyball. Further, perhaps due to data availability issues, our understanding of

what shapes the interest for such popular sporting events as cycling (e.g., the Tour

de France), golf (e.g., the PGA tour) and Formula 1 racing is literally non-existent in

the literature, as it is the case for emerging sports such as the increasingly popular

Kabaddi, Lacrosse, and also electronic sports that regularly attract large crowds.

For those sports that have, however, previously generated the field’s regular

attention, our understanding of the overall robustness of the observed effects is

rather limited, as previous research has primarily explored only a few domestic

competitions, typically top-tier leagues, within only a few markets. As such, we

believe that future stadium attendance demand research should profit tremendously

from widening its narrow focus to consider a significantly more diverse set of

objects of investigation. In particular, this is true for studies exploring data generated

22 Journal of Sports Economics XX(X)

in both domestic and international cup competitions (e.g., the UEFA Champions

League), lower-tier competitions, and, last but by no means least, women’s sports,

despite some notable first attempts (e.g., Valenti et al., 2020). Naturally, it would, for

instance, be highly interesting to see whether there are significant differences

between the effectiveness of certain determinants (e.g., match outcome uncertainty,

player talent/stars, and the weather) across not only men’s and women’s sports but

also across different tournament formats.

On a related note, we believe it’s about time to explore African, Asian, and South

American competitions in more detail. While exploring these environments can

certainly help us improving our understanding of the robustness of previously

explored determinants, thus increasing confidence in the generalizability of mechan-

isms across different cultures, analyzing such alternative markets might also help

mitigate the methodological problems discussed earlier. Accordingly, few authors

have already begun exploring data from South American leagues, which, unlike their

European counterparts, often still refrain from distributing season tickets to fans

(e.g., Buraimo et al., 2018; cf. section 3.4). However, analyzing the stadium atten-

dance demand in leagues with frequently altering calendars and/or league formats

such as the Argentine Primera Divisi´on over time might also offer additional insights

for questions relating to effective tournament design.

Second, concerning the manuscript focus, we noted a strong interest on studies

exploring the specific characteristics of the sporting events, particularly the role of

varying competitive balance and the resulting match outcome uncertainty. In con-

trast, only a few manuscripts centered around exogenous shocks (e.g., health emer-

gencies, terrorism), macroeconomic factors, and, somewhat surprisingly, also the

ticket price (e.g., Watanabe & Soebbing, 2017). While the former aspects are typi-

cally rare and only offer limited managerial implications for the top management,

the latter, in particular, might become more prominent as disaggregated, that is,

individual, data become available in the future. In fact, although we observe several

notable attempts to approximate the ticket price in the literature (e.g., revenue

divided by attendance), endogeneity concerns still remain, among others, which,

in turn, ultimately affect our understanding of the price elasticity of stadium atten-

dance demand.

Somewhat similarly, employing such individual data seems

imperative to correctly estimate the effect of such standard attendance explanatory

variables as income in the future, which has previously often been approximated by

employing annual data, despite some notable exceptions (c.f., Hong et al., 2013).

Third, we observed that the field has previously refrained from exploring disag-

gregated data, despite the early criticism from Forrest et al. (2005). While this

practice is not only common in the literature, and primarily driven by limited data

availability, but also historically grown, we believe that future research might profit

from excluding season ticket holder information and, perhaps even the number of

free tickets, from the dependent variable while discussing the potential pitfalls in

more detail if the former is not possible. Alternatively, as technological progress

allows for generating better information, exploring behavioral stadium attendance

Schreyer and Ansari 23

demand, that is, the actual matchday turnout, might be a profitable approach (e.g.,

Schreyer et al., 2016; Schreyer & Torgler, 2021).

While we believe that our systematic approach, that is, excluding both those

manuscripts whose authors explored behavioral data and data containing annual/

average information, as well as survey data, results in a comprehensive scoping of

the existing literature modeling behavioral intentions to attend a sporting event,

naturally, there are a number of limitations to our approach. For example, although

we have taken great care while selecting the 195 manuscripts in our data set, the

sheer volume of the existing literature in combination with our manual approach

might lead to the omission of a few manuscripts; for example, those that, in abstract,

keywords or the title, are not explicitly linked to stadium attendance research.

However, while such a potential omission might slightly alter the effective results

provided above, we believe that our comprehensive approach allows summarizing

key tendencies, nevertheless. Further, as discussed earlier in this section, our self-

limitation on those studies modeling matchday demand might, to some degree,

underestimate the field’s interest in factors that are stable across one or multiple

season(s) and, therefore, more likely to be explored using annual attendance data

(e.g., the stadium quality). Finally, as it is common when conducting scoping

reviews, we largely refrain from an in-depth analysis of all effect sizes and prefer-

ably present existing (and emerging) themes. As such, future research might benefit

from additional literature reviews focusing on specific aspects in stadium attendance

research, for example, the relationship between the football interest and the weather,

or even meta-analyses.

Author’s Note

Payam Ansari is now affiliated with DCU Business School, Dublin City University, Dublin,

Ireland.

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.

ORCID iDs

Dominik Schreyer https://orcid.org/0000-0003-1129-7789

Payam Ansari https://orcid.org/0000-0001-7880-586X

24 Journal of Sports Economics XX(X)

Notes

1. The relative share of matchday income to a professional sporting clubs’ annual turnover

varies significantly between not only different sports but also between sporting leagues

within a particular sport. In Germany, for example, this relative share is currently about

13 and roughly 17 in Bundesliga and Bundesliga 2, while it is circa 11, 20, 26, and 32%,

in Volleyball, Basketball, Handball, and Hockey, respectively (cf., Horky, 2020).

2. At the time of writing, that is, in September 2020, already more than 5,000 scoping

reviews have been published in ISI-indexed journals.

3. The exact search string is: “stadi* attend*” OR “game attend*” OR “attend* game” OR

“attendan* demand” OR “spectator demand” OR “spectator attendance” OR “ticket

holder” OR “game visit*” OR “stadi* visit*” OR “visit* game” OR “visit* match”.

4. Despite notable methodological weaknesses (cf. Forrest et al., 2005), perhaps most likely

because behavioral data on stadium attendances are often still scarce, exploring this

particular information, that is, attendance data usually announced by the home club and,

then, distributed to the public by the media, has a long tradition, in particular in the field

of sports economics (cf., Dobson & Goddard, 2011). Thus, Tainsky and Winfree (2010),

for example, argue that “[i]t is customary among researchers and practitioners to report

the number of tickets sold as the attendance”. As Pawlowski and Nalbantis (2015), for

example, argue, this data typically refrains from distinguishing between season and

matchday ticket holders.

5. Although we carefully separated those studies modeling behavioral intentions from those

analyzing actual behavior, we have to admit that our result on that matter was incon-

clusive now and then. Whenever in doubt, we added the study to our sample. Thus, those

excluded manuscripts from authors exploring behavior typically offer an explicit cue

such as “The numbers reported are based on those who actually turn up as opposed to

tickets sold” (Owen & Weatherston, 2004: 352), “Our dependent variable, Attendance, is

the number of spectators who officially entered the venue for the match” (Sacheti et al.,

2016: 124), or “our key dependent variable, is the absolute number of ticket holders that

have decided not to attend a particular Bundesliga game” (Schreyer & Da¨uper, 2018,

p. 1,476) in their study.

6. As such, our comprehensive data set does not only exclude those studies using annual/

seasonal attendance data (e.g., Alvarado-Vargas & Zou, 2019; Lee, 2018; Mills & Fort,

2018), sometimes also expressed as average stadium attendances per match (e.g.,

Feddersen & Maenning, 2009), but also most studies analyzing cricket attendances

(e.g., Sacheti et al., 2014). Although cricket matches are often played for several con-

secutive days with varying interest, it seems customary in the field to proxy stadium

attendance demand using average daily attendances or aggregate match attendance

which, for us, would complicate the comparability to the remaining studies in the data

set. However, we allow for cricket studies modelling, for example, 1-day league cricket

demand (e.g., Morley & Thomas, 2007). Further, we included those few studies whose

authors model the stadium attendance demand for annual, one-time events such as the

Melbourne Cup (Narayan & Smyth, 2003).

Schreyer and Ansari 25

7. In sum, we screened Google Scholar for (and within the) available profiles of the 25 most

active colleagues in the field, that is, those authors who, according to our temporary

sample, had published the most studies on stadium attendance demand.

8. As such, our sample does not include more recent studies that attempt to model the

stadium attendance demand during a public health emergency, such as the recent

COVID-19 pandemic (e.g., Reade & Singleton, 2020). Naturally, a small number of

manuscripts exploring stadium attendance demand were published after we had finished

our search process (e.g., Paul et al., 2020; Reade et al., 2020b; Wallrafen et al., 2020).

9. In contrast, Serrano et al. (2015), for example, consider data across four European leagues

without presenting separate regression results for each of these four leagues individually.

We, therefore, count this study as one manuscript with one rather than four empirical

studies. Perhaps it’s important to note that presenting such evidence across different

leagues is not a rarity (e.g., Marcum & Greenstein, 1985; Forrest & Simmons, 2002;

Peel & Thomas, 1996).

10. Here, perhaps it is worth noting that we refrain from excluding manuscripts based on, for

example, disciplines. As such, besides contributions from the field of both sports eco-

nomics and sports management, our data set also contains manuscripts that were pub-

lished in journals in fields as diverse as accounting and finance (e.g., Paul et al., 2016),

applied geography (Griffith, 2010), management science (Kappe et al., 2014), operational

research (e.g., Goller & Krumer, 2020), and statistics (e.g., Hart et al., 1975).

11. While some manuscripts in our database come with short author profiles and allow for

quick extraction of such gender information, we had to obtain most of this information

from additional web searches. Despite intensive efforts, we failed to identify the gender

of two authors. However, we believe this omission to have only a modest effect on our

results reported below (if any).

12. For those manuscripts published in journals that are available exclusive online (e.g.,

Watanabe & Cunningham, 2020), also known as “online only”, we consider the year

of online publication as the year of the publication “in print”. Further, for one very recent

publication (i.e., Kelley, 2020) that is currently available “online first” and, therefore, not

yet available “in print,” we refrain from differentiating between the two statuses.

13. To reduce complexity, we made a few adjustments, perhaps better labelled as simplifica-

tions, throughout our data collection process. That is, whenever we observed analysis of

either the National League and/or the American League, we noted this as Major League

Baseball (MLB), and considered this to be one study, although separate regressions might

have been available. Further, in particular with regard to studies exploring stadium

attendance demand for National Hockey League and/or National Basketball Association

matches, we noted the United States of America (USA) as the market under investigation,

although some few teams are being located in Canada. Somewhat similarly, if an inter-

national tournament was hosted in one country, we considered this country to be the

market under investigation, although many spectators might have flown in from abroad to

attend a match (or more). In addition, we also adjusted the label of domestic sporting

leagues according to their current equivalent whenever necessary. For example, we

decided to mark those studies exploring English Division 1 data that were conducted

26 Journal of Sports Economics XX(X)

before the English Premier League was introduced in the season 1992-93 as “English

Premier League” nevertheless, because, back then, the Division 1 was the first tier

professional football league in the country.

14. In science, the so-called Matthew effect, stemming from the parable of the talents

(Matthew 25: 14-30), one of the parables of Jesus, “consists in the accruing of greater

increments of recognition for particular scientific contributions to scientists of consider-

able repute and the withholding of such recognition from scientists who have not yet

made their mark” (Merton, 1968, p. 58).

15. As we observe a strong negative correlation between a manuscript’s year of publication

and its subsequent number of accumulated citations (r¼-0.5867, p< 0.001) in our data

set, this last observation might also help to explain why those many contributions pub-

lished in the JSE, today a natural home for stadium attendance demand research, are

largely absent from this particular list, despite two notable exceptions ranked one (Garc´ıa

& Rodr´ıguez, 2002) and eleven (Forrest & Simmons, 2006). Intriguingly, both manu-

scripts were among the first empirical contributions exploring stadium attendance

demand that were published by the journal. Founded only in the year 2000, the JSE has

published a total of 31 manuscripts on stadium attendance demand research, according to

our data set, since then, which were, on average, cited about 55 times.

16. As editor Dennis Coates has rightfully noted during the review process, it may take an

increase in the number of both female authors and also authors from non-Western soci-

eties studying sports economics and sport management issues to broaden the scope of

stadium attendance demand. That is, in particular, true for adding previously unexplored

(niche) sports in the future.

17. However, the values might be inflated, at least to a certain degree, as some manuscripts

might have been available earlier to publication in print (e.g., due to online first publi-

cations or earlier working papers).

18. In addition, we count three different journals with four appearances, five journals with

three appearances, three additional journals with two appearances, and also 56 journals

that only appear once in our database.

19. In this context, we count the distribution through a book chapter as one outlet.

20. Although some of these omissions can, perhaps, be best explained by apparent measure-

ment problems (e.g., in professional cycling, where no stadium exists and spectators,

therefore, often gather along the route) or a simple lack of data. However, an alternative

explanation might be that most authors operating in the field originate from (or currently

work in) Western society, where the predominant spectator sports are American football,

baseball, basketball, football/soccer, and hockey (in alphabetical order). We thank the

editor for adding this latter point.

21. In three out of these 42 studies, the authors analyse data aggregated across several leagues

(Dobson & Goddard, 1992; Forrest & Simmons, 2002; Forrest et al., 2005).

22. In general, this observation holds true beyond football stadium attendance demand

research.

Schreyer and Ansari 27

23. This is, in particular, noteworthy as analysing data from such less developed football

markets might haven methodological benefits, for example, the absence of season tickets

(e.g., Barajas et al., 2019; Buraimo et al., 2018; cf. section 3.4)

24. Those authors that have refrained from focusing on one (or more) specific determinant

typically employ titles such as “Attendance demand in a developing football market: the

case of the Peruvian first divisions” (Buraimo et al., 2018), “The Demand for League of

Ireland Football” (Reilly, 2015), and “The Determinants of Football Match Attendance

Revisited” (Garc´ıa & Rodr´ıguez, 2002). In addition, some authors emphasize methodo-

logical contributions (e.g., Schmidt, 2012). Other authors, however, do not explicitly note

that their manuscript adds to the literature on stadium attendance demand research in their

title (e.g., Coates et al., 2017).

25. It is, perhaps, important that we note that this does not mean that only roughly every fourth

study explores the role of competitive balance and/or intensity in modelling stadium

attendance demand. In fact, almost all authors exploring stadium attendance data add at

least one of the innumerable proxies of uncertainty as a control variable to their models.

26. Further, the use of such publicly displayed information always contains the danger that

data were either unintentionally wrong or intentionally inflated, for example, to cater to

investor needs. Modeling NASCAR racing attendance numbers, Berkowitz et al. (2011,

p. 268), for example, summarize that “the general lack of statistically meaningful rela-

tionships between attendance and the other variables in the model might suggest that the

attendance data are not accurate, that NASCAR simply reports track capacity instead of

attendance, or simply reflect the fact that the decision to attend is often made in advance

and therefore contemporaneous values of the explanatory variables would have no sta-

tistical relationship with reported attendance”.

27. It is perhaps important to note that as football clubs, in particular, increasingly even

bundle matchday tickets across several matches, typically a combination of a top match

and several other matches, even subtracting the number of distributed season tickets

would not help to perfectly proxy matchday interest for a certain match.

28. While the effective stadium attendance demand in professional sporting leagues such as

the English Premier League is typically hard to observe because many games are sell-

outs, in some markets, most notable in Iran, it is because some spectators are currently not

allowed to enter the stadium; in this specific context women.

29. Interestingly, however, the most extensive data set is likely to emerge in professional

football. That is, Reade (2020), in a current working paper not yet included in our

analysis, explores the determinants for English football employing match-level data on

165,105 football matches played since 1888.

30. In fact, thanks to the cultural phenomenon that Harry Potter is, today, even Quidditch,

which is organized by 39 national governing bodies and played by about 600 teams world-

wide (MuggleNet,2020), might be an interesting future object of investigation for Muggles.

31. Similarly, we observe only a limited number of studies exploring the role of manageable

stadium-quality aspects that, however, are likely to only vary between seasons and are,

thus, more likely to be a common theme in the complementary literature exploring

28 Journal of Sports Economics XX(X)

annual/average attendance data to document long-term trends (e.g., Clapp & Hakes,

2005; Feddersen & Maenning, 2009; Leadley & Zygmont, 2005).

32. Once more, we’d like to thank the editor for pointing this out.

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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 particular, he takes a keen research interest in analyzing

stadium attendance demand (e.g., football spectator no-show behavior). His Twitter handle is

@schreyerforscht.

Payam Ansari is a Post-Doctoral Research Fellow at DCU Business School, Dublin City

University, Ireland. His current research focus is on fan behavior/engagement, in particular in

women’s football. His Twitter handle is @payam_ansari.

40 Journal of Sports Economics XX(X)

Domestic vs . foreign football matches: are viewers interested in the same features?

Article

Sep 2024

Mapping the determinants of stadium attendance in Norwegian women's Toppserien soccer league

Preprint

Full-text available

Aug 2024

Research question: This study is motivated by the scarcity of research on attendance demand in women's soccer. We aimed to investigate the determinants that shape stadium attendance in Norwegian Toppserien women's league. Research methods: Data was used from 12 seasons between 2010 and 2023. We employed a multivariate OLS regression with more than 30 independent variables to investigate their relationship with attendance. Results and findings: Despite a relatively low attendance, it has increased significantly over the examined period. Consumers of Toppserien appear to be loyal to women's soccer, however, they respond positively to the level of game quality and the team's performance. Surprisingly, the size of the municipality negatively correlates with attendance, and spillover effects from successful men's teams play only a minor role. Finally, the first round of the season attracts larger crowds. Implications: The results suggest that the fans of women's and men's Norwegian soccer teams have rather distinct consumer profiles, highlighting the need for tailored marketing strategies for women's teams. To maximize attendance, it also seems advisable to avoid scheduling collisions with mega events and to schedule more games on public holidays. Finally, home games of the first round should be more evenly allocated between the teams.

Targeted investment in elite sports: an empirical lesson from professional fencing

Article

Full-text available

Nov 2024

Attendance

Chapter

Sep 2024

Cristiano of Arabia: Did Ronaldo increase Saudi Pro League attendances?

Article

Full-text available

Aug 2024

In December 2022, Cristiano Ronaldo, five‐time Ballon d’Or winner and the most‐followed person on Instagram, signed for Al‐Nassr in the Kingdom of Saudi Arabia. This marked one of several expensive, recent interventions by the kingdom in global sports markets. We exploit the timing of this event to estimate superstar effects. Ronaldo alone increased stadium attendance demand, even before the influx of further stars in the summer of 2023. On average, Ronaldo filled an additional 20% of the seats in his home team's stadium when he played, 15% in the stadiums he visited, and 3% where he did not even play.

Premium seating in MLS: game attendance motives of luxury suites and club level seats

Article

Jan 2025

Tournament design: A review from an operational research perspective

Article

Nov 2024
EUR J OPER RES

Outcome Uncertainty and Viewer Demand for Basic Cable Boxing

Article

Feb 2023
J SPORT ECON

We examine the uncertainty of outcome hypothesis and consider direct demand for professional boxing using a new dataset for basic cable broadcasts. Our analysis covers 103 broadcasts in the United States from February 2017 to February 2021 for Premier Boxing Champions (FOX) and Top Rank Boxing (ESPN). Using a generalized linear model, we estimate a demand function for basic cable broadcasts and place specific emphasis on adopting alternative measures of outcome uncertainty. We find no evidence that increasing balance between boxers increases viewership figures on basic cable broadcasts. Consistent with pay-per-view boxing viewership, our results demonstrate that bout quality impacts demand for cable broadcasts. Furthermore, we find no evidence of a declining viewership trend, a finding relevant to the industry given the general decrease in viewership trends for main events and pay-per-view broadcasts. Fans continue to demonstrate a preference for the welterweight division.

Demand for offense: designated hitters and MLB attendance

Article

Jan 2024

Determinants of Online Live Esports Viewership With Advanced Data: The Case of League of Legends Champions Korea

Article

Aug 2024

With the rapid advancement of technology, esports have become more accessible and widespread on online platforms. The behavior of fans watching live broadcasts online differs from that of those watching on TV or attending stadium events. To understand the online demand for esports, this study examines the determinants of live esports viewership using advanced data from online platforms. We examined game-level data from the League of Legends Champions Korea (LCK) from the summer of 2019 to the summer of 2022. We analyzed four dependent variables representing esports online demands—hours watched, peak views, concurrent average views, and view drops—based on several pre-game (outcome uncertainty and team quality), in-game (game quality and upset result), and schedule-related (evening game) variables. Using two-way least squares dummy variable regression models, we identified the key determinants. The results suggest that outcome uncertainty, team quality, game quality, and evening games significantly impact esports online viewership. In addition, in-game factors, such as the sum and difference in kills between teams, attracted more viewers. This indicates that online viewers prefer games with more kills and a closer kill difference between teams.

What Can We Learn About Economics from Sport during COVID-19?

Preprint

Full-text available

Jan 2021

The economics of sport and how sport provides insights into economics have experienced exogenous shocks from COVID-19, facilitating many natural experiments. These have provided partial answers to questions of: how airborne viruses may spread in crowds; how crowds respond to the risk and information about infection; how the absence of crowds may affect social pressure and arbitration decisions; and how quickly betting markets respond to new information. We review this evidence and advise how sports economics research could continue to be most valuable to policymakers.

Eliminating Supportive Crowds Reduces Referee Bias

Preprint

Full-text available

Oct 2021

We use a series of historical natural experiments in association football to test whether social pressure from a home stadium crowd affected behaviour and outcomes. The standout effect of an empty stadium was that referees cautioned visiting players less often, by over a third of a yellow card per match or once for every twenty-two fouls committed. Stadium crowds caused referees to favour the home team in their decision making. Empty stadiums appear to have reduced the overall home advantage in the final outcomes of football matches, but we cannot statistically reject no effect.

Demand for public events in the COVID-19 pandemic: a case study of European football

Article

Full-text available

May 2021
Eur Sport Manag Q

This study looks at stadium attendances in elite-level European football, to suggest how people responded to the initial COVID-19 outbreak. This offers insight into how professional sports will emerge from social lockdowns and competition taking place behind closed doors. The analysis focuses on the top leagues of England, Italy, France, Spain and Germany. Using panel data methods and exploiting the variation in day-to-day attendances in these leagues, the impacts on implied spectator demand from the news of confirmed COVID-19 cases and deaths can be quantified. In Italy, England and Germany, stadium attendances were negatively affected by the previous day's newly confirmed domestic cases or deaths. In Spain and France, there was no attendance response to the early stages of the domestic outbreaks. COVID-19 was affecting football match spectator demand before European countries enforced lockdowns and other restrictions to suppress the spread of the disease. This suggests that fans significantly responded to the risk of catching the virus. If this risk remains when stadiums reopen, then sports organisations should expect reduced ticket demand. This suggests that managers should adopt more dynamic and creative pricing strategies, and use their stadiums in more innovative ways, if they are to survive financially in a world where COVID-19 remains a threat to public health.

Stadium attendance demand during the COVID-19 crisis: early empirical evidence from Belarus

Article

Full-text available

Oct 2021
APPL ECON LETT

In this note, we consider early evidence regarding behavioural responses to an emerging public health emergency. We explore patterns in stadium attendance demand by exploiting match-level data from the Belarusian Premier League (BPL), a football competition that kept playing unrestricted in front of spectators throughout the global COVID-19 pandemic, unlike all other European professional sports leagues. We observe that stadium attendance demand in Belarus declined significantly in the initial period of maximum uncertainty. Surprisingly, demand then slowly recovered, despite the ongoing inherent risk to individuals from going to a match.

Outcome Uncertainty and Demand for Baseball: A Single-Game Approach in the Korean Professional Baseball League

Article

Feb 2020
INT J SPORT FINANC

This study tests the uncertainty of outcome hypothesis for single games and playoff appearances in the Korean Professional Baseball League from 2007 to 2015. Our panel data analysis shows that the difference in winning percentages between two teams and playoff uncertainty based on games behind are important factors for increasing game attendance. This study supports the potential importance of analyzing daily game attendance of the literature on diverse sport leagues. It also presents implications for policymakers and league owners—which typically leverage teams as promotional instruments—on improving the self-sustainability of sport teams.

A Test of the Optimal Positive Production Network Externality in Major League Baseball

Chapter

Jan 1999

Dan Rascher

The sports industry presents many unusual and interesting opportunities for the application of economic theory and econometrics. In 15 professional papers, this book addresses current economic issues in the industry, including the problem of competitive balance, the location of professional sports teams and their impact on local communities, managerial decision making, and issues related to labor markets. Extending the previous research in sports economics, the papers reflect the most recent applications of economic theory in this area. The book will be a valuable resource for professional economists working on sports economics topics. In two opening chapters on competitive balance, the contributors develop a model for college football and examine the impact of balance on attendance in major league baseball. In a section on the location of professional sports teams, the chapters then develop a model to predict the location of expansion teams, make econometric estimates of the impact of Super Bowls on the host city, and analyze the ownership of stadiums and arenas. Managerial decision making is discussed in chapters that examine alternative econometric models of production in baseball, use a production function model to analyze technological change in Major League Baseball, examine the management of team streaks, consider the competitive balance between American and National Leagues, analyze the efficiency of player trades in the National Basketball Association, and estimate the impact of participation in inter-collegiate sports on academic performance. In the final section on labor markets, the contributors estimate the impact of owner collusion on baseball players' salaries, consider the impact of the new collective bargaining agreement in Major League Baseball, analyze the impact of being a union representative, and examine the impact of the National Football League's salary cap on player's salaries.

A framework for diversification decisions in professional football

Chapter

Jun 2018

In order to guide football clubs in their growth development, we introduce the Growth Strategy (GS) framework, which is rooted in diversification literature. Overall, the 3×3 matrix entails nine distinct prin-cipal strategies along the two dimensions – business proximity and regionality. The GS framework is intended to support football clubs in making profound decisions about where to allocate their resources and select the most suitable diversification path in order to stimulate further growth. Case examples illustrate the applicability of the GS framework.

Football attendance over the centuries

Chapter

Oct 2021

J James Reade

Predicting season ticket holder no-show behavior: More nuanced evidence from Switzerland

Preprint

Feb 2021

The Impact of Live Broadcasting on Stadium Attendance Reconsidered: Some Evidence from 3rd Division Football in Germany

Article

Sep 2020

If a sports competition is broadcasted live, consumers may opt for substituting gate attendance with watching that game live on TV (or online). This might be worrisome for teams, particularly those in lower divisions, whose game day revenues typically exceed broadcasting revenues. So far, however, the literature testing this claim empirically is inconclusive. We examine whether (at least parts of) this confusion might be traced back to shortcomings in the econometric modelling process. We use attendance data for 1,138 games in German third division football from the 2015/16 to 2017/18 seasons and compare results for our demand equations between ordinary least squares (OLS) and endogenous treatment regressions (ETR). ETRs explicitly account for any selection bias, that is, the broadcasters’ preference to select the most attractive games for live broadcasting (which are expected to also attract comparably larger gate attendances). While OLS models reveal a significant positive impact of live broadcasts on gate attendance, this effect reverses when estimating ETRs. Even though there is suggestive evidence for postponing ticket demand to some extent to later games, the overall negative effect remains robust and large. Our findings highlight the relevance of controlling for the selection bias when analysing the impact of live broadcasting on stadium attendance. From a managerial point of view, our findings suggest that increasing the number of games broadcasted live in German third division football might not be advisable, since additional broadcasting revenues may not exceed predicted losses in ticket revenues. https://www.tandfonline.com/eprint/6P2XXZSWNHKJNMYCZQUM/full?target=10.1080/16184742.2020.1828967

Stadium Attendance Demand Research: A Scoping Review

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