Determining the price of football: an analysis of matchday ticket prices in the English Premier League

Determining the price of football: an analysis of matchday

ticket prices in the English Premier League

Tommy Kweku Quansah

, Babatunde Buraimo

and Markus Lang

Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland;

Centre for Sport Business,

Management School, University of Liverpool, Liverpool, United Kingdom

ABSTRACT

Research question: Match day revenue is still an essential source of

income for many professional sports clubs worldwide. This paper

studies the determinants of ticket pricing in the English Premier

League and examines whether and to what extent stadium goers

pay a premium for sporting success and spending by clubs in the

players’ labour market.

Research methods: We estimate regression models for the

cheapest and most expensive tickets of clubs playing in the

English Premier League for the ﬁve seasons between 2014/15 and

2018/19 inclusive.

Results and ﬁndings: Our study shows that ticket prices are driven

by several variables including the opponent and local derbies. The

impact of team performance is asymmetric aﬀecting only the most

expensive tickets. Capacity utilisation and total labour cost impact

the prices of both types of tickets.

Implications: This research provides insights for both leagues and

individual clubs. For example, the ﬁndings can be used as a

benchmark to assess the magnitude of price increases that the

market may be willing to bear. Additionally, clubs can explore the

extent to which greater revenues can be extracted from diﬀerent

types of consumers.

ARTICLE HISTORY

Received 28 February 2022

Accepted 10 March 2023

KEYWORDS

Ticket pricing; competitive

strategy; determinants of

price

Introduction

The inﬂux of commercial and broadcasting income for the most prominent sports

leagues in the world meant a gradual decline over the years in the share of match day

revenues as a proportion of total revenue. However, for many sports and leagues world-

wide, match day revenue is still the primary source of income, and even for the largest

sports organisation, the contribution of and size of match day revenue is essential. For

example, in the English Premier League (EPL), in which match day revenue is dominated

by that of broadcasting and represents just 13% of total revenue for the league in 2020,

the absolute value of match day revenue was €683 million, a non-trivial amount and of

great economic relevance (Deloitte, 2021).

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/

licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author

(s) or with their consent.

CONTACT Babatunde Buraimo [email protected]

EUROPEAN SPORT MANAGEMENT QUARTERLY

https://doi.org/10.1080/16184742.2023.2191633

With the end of the government-imposed restrictions on attendance after the Covid-

19 pandemic in the years 2020 and 2021, and the return of fans to stadia, questions of

how sports organisations price their tickets have re-emerged. Whilst the availability of

information on ticket pricing in North American major sports leagues allows empirical

analyses of the factors that inﬂuence ticket prices (Rishe & Mondello, 2003, 2004), the

limited accessibility of match day pricing data for the most European sport make

similar analyses diﬃcult.

The purpose of this paper is two-fold: ﬁrstly, the paper aims to address the question of

whether stadium visitors must pay a premium for the sporting success of teams. Sec-

ondly, whether stadium goers must pay for excesses in the player labour market.

Additionally, we explore other factors that drive ticket prices. From a managerial per-

spective, sports clubs may wish to explore the relationships between ticket prices and

assess how their pricing strategies conform with benchmarks. With additional research

and data, clubs can evaluate potential revenue improvements from changes in the deter-

minants that aﬀect the price.

This data set’s unique feature is the cheapest and the most expensive ticket prices for

each game. We performed the arduous task of collecting the most expensive and cheapest

ticket price data for each English Premier League game for ﬁve years from 2014/15 to

2018/19 inclusive. These were sourced from individual websites and ticketing platforms

of the participating clubs. Accounting for missing observations, there were 3,040 obser-

vations across these two diﬀerent prices.

To analyse the determinants of ticket prices in

the EPL, several possible inﬂuencing factors are considered. These include economic

variables, variables capturing game attractiveness, quality of viewing variables, and

stadium capacity.

We ﬁnd that the relative average weekly earnings, capacity utilisation for the equival-

ent ﬁxture from the previous season, local derbies, total costs of labour, and playing

certain oppositions, the six biggest clubs (Manchester United, Manchester City,

Chelsea, Arsenal, Liverpool, and Tottenham Hotspurs), aﬀect the price of both the cheap-

est and most expensive tickets. The positive externalities on ticket prices for the six

biggest away teams are interesting in that their magnitudes reﬂect their ‘bigness’.

Home teams use these opponents as an opportunity to freeride by signiﬁcantly increasing

ticket pricing for both the cheapest and the most expensive tickets. Additionally, we ﬁnd

that improvements in the home team’s performance from the previous season positively

inﬂuence the price of the most expensive ticket but not the cheapest.

The paper is structured as follows: ﬁrst, we review the relevant literature. Next, we

present the data and empirical model. We then discuss the explanatory variables and

present and analyse the results. We then provide insight into the managerial implications

of our ﬁndings before summarising our conclusions and discussing limitations and

potential areas for future research.

Related literature and theoretical background

Ticket pricing

In 1998, an industry report on English Football noted that for most clubs in the EPL, ‘gate

revenues remain the primary income source that ensures that the clubs’ ﬁnancial

2 T. K. QUANSAH ET AL.

backbone is maintained’ (Deloitte, 1998). Some 20 years later and as a direct consequence

of the growth in commercial and broadcasting income (see Figure 1), match day revenues

represented a mere 13.3% of the clubs ’ total revenues (Deloitte, 2021). However, the

amounts of match day revenues are still of great importance to EPL clubs (Quansah

et al., 2021), even for the biggest clubs in the league, as can be seen in Figure 2, where

ﬁve out of the big six

have a signiﬁcant dependency on match day revenues in the

season 2018/19.

Ticket pricing remains a key tool by which clubs can generate appropriate match day

revenue. Lovelock and Wirtz (2004) assert that club pricing objectives fall into three cat-

egories: revenue, operations, or patronage orientation. While revenue orientation aims to

improve revenues in pursuit of generating proﬁts or accumulating surplus to be spent in

the players’ labour market, operations orientation tries to balance supply and demand to

ensure maximum utilisation of the available capacities at any given time. On the other

Figure 1. Evolution of revenues in the English Premier League.

Notes: Own ﬁ gure based on Deloitte (1998, 2013, 2016, 2019 , 2021). The ﬁgure displays the evaluation of total revenues

for the three main revenue sources in the English Premier League from the season 2005/06 up to season 2019/20 in

addition to total revenues in the founding season of the EPL 1991/92. The three major revenue sources are match

day revenue, broadcasting revenue and commercial revenue.

Figure 2. Match day revenues as a percentage of total revenues by club, 2018/19.

Notes: Own ﬁgure based on Quansah et al. (2021). The ﬁgure displays match day revenues as a percentage of total rev-

enues for each club that played in the English Premier League in the season 2018/19.

EUROPEAN SPORT MANAGEMENT QUARTE RLY 3

hand, patronage orientation aims to maximise a club’s appeal among its supporter base.

Drayer et al. (2012) posit that clubs have maintained a focus on attendance maximisation

in the past without much consideration for revenue orientation, while other authors

suggest that football clubs have the motivation to under-price tickets to maximise attend-

ance and increase ancillary revenues from food and beverages, parking, merchandise and

to enhance the fan experience (Courty, 2003; Fort, 2004). For example, research from

North American intercollegiate athletics suggests a unique attendance-oriented pricing

objective not prominently found in professional sports (Morehead et al., 2021). Apart

from having to cope with heterogeneous stakeholder interests within a university, as

well as with the NCAA’s attendance requirements, the athletic departments have to con-

sider attendance because of media attention, institutional reputation, school spirit, and

the recruitment of talent for their sports teams (Morehead et al., 2021).

In the early 2000s, football clubs in England started to implement advanced price

policies, such as price banding (i.e. the notion of premium pricing for match day

tickets against certain opponents) and price bundling (i.e. oﬀering combinations of

two or more match day tickets in a package to be purchased together in one trans-

action) (Clowes & Clements, 2003). However, it is not so much the implementation

of demand-based pricing strategies that causes resentment among English football

fans but the perceived or actual excessive high ticket pricing, particularly in the EPL

(Conn, 2014).

Determinants of ticket pricing

We borrow from the rich tradition of demand studies but place ticket prices at the centre

stage therefore adopting an inverse demand function (Ferguson et al., 1991). The inverse

demand function has price as the dependent variable. The analysis of ticket prices as the

dependent variable and its determinants helps us explore several relationships. Research

on the determinants of ticket pricing compared with attendance is relatively new (Diehl

et al., 2016), and studies are relatively scarce, especially those focusing on ticket pricing in

European sports. Whereas pricing data on North American major league sports have

often been obtainable, historic match-by-match ticket price data for European football

have been challenging to acquire (Coates & Humphreys, 2007).

In an exploratory study on ticket prices in the NFL, Reese and Mittelstaedt (2001)

were the ﬁrst to explore the criteria used to establish pricing strategies in the North

American primary market. They found that the previous year’s team performance,

revenue needs, and socio-demographic factors, such as income, market size, population,

and having a new stadium, inﬂuenced match day ticket prices in American Football.

Rishe and Mondello (2003) empirically investigated price determinants in the NFL

and the four major North American sports leagues, respectively. While their studies

suggest that the factors that inﬂuence ticket prices in the NFL are the previous

season’s team performance, fan income, population, as well as stadium age, the exist-

ence of a new stadium was found to be the most important driver of ticket prices in

all four major league sports. Population size and the previous season’s capacity utilis-

ation were signiﬁcant and positively associated with ticket prices in three of the four

leagues, except for the NFL. Interestingly, their evidence suggests that a team’s

payroll does not inﬂuence ticket prices in the NFL or NHL but does so in the NBA

4 T. K. QUANSAH ET AL.

and MLB (Rishe & Mondello, 2004). Furthermore, prior season success only marginally

aﬀects ticket prices in most North American major league sports. According to a more

recent study on football spectator attitudes in Brazil, club speciﬁcs, the form of the

ticket purchase, and stadium quality further determined the price, according to a

more recent study on football spectator attitudes (Forti & De Lima, 2021).

While traditional sports demand studies have considered ticket pricing as one of many

variables aﬀecting spectator demand, few studies have examined the factors inﬂuencing

ticket pricing in European sports markets. Kemper and Breuer (2016a, 2016b) have

focused on dynamic ticket pricing applications in European sports, Kemper and Breuer

(2015) have studied the secondary ticket market in the German Bundesliga by analysing

ticket prices on ebay.de, and Nufer and Fischer (2013) have conducted a descriptive

study on ticket prices in European football. In a sports marketing study, Woratschek

et al. (2020) analysed secondary ticket market premia for German women’s national foot-

ball using conjoint analysis. Solberg (2001) examined ticket price optimisation strategies

during the 1999 World Hockey Championship in Norway by utilising price bundling.

To the best knowledge, the factors that determine ticket prices in the English Premier

League have not been previously investigated. Nor have the determinants of ticket

prices in the primary market of any other sports league in Europe been examined.

Another factor inﬂuencing ticket pricing is the opponent. As Neale (1964) notes, the

production process in professional team sports diﬀers from other industries. While the

elimination of competition can beneﬁtaﬁrm in most markets, in sports, a single team

is unable to produce a marketable product without opponents. This peculiarity necessi-

tates a joint production process where multiple teams participate in producing the ﬁnal

product. Furthermore, not only is the existence of other teams essential, but also their

relative strengths are crucial for the sport’s marketability. The relationship between the

quality of competition and a team’s revenue is not linear in professional team sports

(Michie & Oughton, 2004). As the opposing team’s strength increases, it can lead to

an increase in attendance and revenue as fans are drawn to more competitive and excit-

ing matches. However, if the opposing team becomes too dominant, it may result in a

decline in attendance and revenue, as fans may lose interest in one-sided matches.

According to Berri and Schmidt (2006):

Despite the joint nature of sports production, the compensation of talent in the industry is

organized as if the individual ﬁrms were largely independent […] teams negotiate with

players, and these negotiations result in a salary that is paid by the individual team.

Hence, if a player generates revenue for his opponent, such revenue generation is largely

uncompensated. Therefore, to the extent that individual team-revenue streams are increased

by the quality of players on other teams’ rosters, an externality exists.

For instance, Hausman and Leonard (1997 ) found that Michael Jordan generated US$53

million for teams other than his team, the Chicago Bulls. The evidence for such extern-

alities also extends to Major League Soccer (Jewell, 2017; Lawson et al., 2008).

Our study addresses the presence of externalities by incorporating the six most

popular teams as dummy variables and evaluating their eﬀect on ticket prices. Using

ticket prices as a measure, home teams can take advantage of the positive externalities

of popular opposing teams. Neglecting to do so may result in the home team not fully

realising the potential beneﬁts.

EUROPEAN SPORT MANAGEMENT QUARTE RLY 5

Data and empirical model

Our unit of observation is the match day, and we consider two dependent variables: the

cheapest and most expensive price for a match day ticket. We manually collected data on

weekly ticket prices charged for games to the EPL for the ﬁve seasons between 2014/15

and 2018/19 inclusive. Due to promotion and relegation, the number of diﬀerent clubs

observed over the observed period is 26, with some clubs appearing in the dataset only

once and others in every single season. Every one of the 20 eligible clubs in a given

season had 19 home and 19 away matches, with a season comprising 380 matches, bring-

ing the total to 1,900 matches for the ﬁve seasons. As the prices collected for each match

were the cheapest and the most expensive, the total number of observations across both

types of tickets was 3,800. However, some ticket prices were unavailable: Manchester City

and Everton for all ﬁve seasons; Newcastle United’s most expensive tickets for four

seasons; and Hull City’s ticket for the 2014/15 season. After omitting these and account-

ing for other missing data, the observations were further reduced to 1,520 for each of the

cheapest and the most expensive tickets. During the sample period, all but four teams:

Huddersﬁeld Town; AFC Bournemouth (2015/16, 2016/17); Swansea City (2014/15,

2015/16); and West Bromwich Albion (2014/15, 2016/17) applied a seat-location

approach, a price discrimination strategy based on the perceived quality of seat.

Diﬀerences between the lowest and the highest match day ticket prices reveal substan-

tial disparities in the additional premium per match charged by each club. Watford, for

example, demanded another £6 for tickets based on seat location. Chelsea charged an

additional £26.00, while Arsenal charged £31.50 more for their most expensive ticket

price than the cheapest seat.

The principles of price banding – price discrimination based on the perceived attrac-

tiveness of the opponent – are an integral part of most clubs’ pricing decisions. Only six

out of the 26 teams in the sample – Manchester United (all seasons), AFC Bournemouth

(2015/16, 2016/17, 2017/18, 2018/19), Watford (2015/16, 2016/17, 2017/18, 2018/19),

Liverpool (from 2016/17 onwards), Burnley (2016/17, 2017/18, 2018/19), and Hudder-

sﬁeld Town (2017/18, 2018/19) – abstain from applying price banding. Everton aban-

doned price banding in the season 2019/20 to reduce and simplify ticket pricing

(Everton, 2019 ). As noted earlier, price banding potentially extracts any additional

surplus consumers are willing to pay and leads to higher income for a club (Rascher

et al., 2007). According to a survey by Clowes and Clements (2003), most clubs that

do not apply price discrimination based on opponents do so consciously as they

believe it is unfair to discriminate between teams in the Premier League or because

they do not see the need to do so. There are also likely to be administration costs associ-

ated with price discrimination.

Of the clubs that applied price banding, there are diﬀerences in the number of cat-

egories, the opponents that trigger a premium, and the magnitude of the premium

charged. While most clubs divide the opponents into three categories (e.g. A, B, and C

or A+, A and B), clubs like Fulham, Middlesbrough, Swansea City, and Wolverhampton

Wanderers prefer only two categories. On the other hand, Norwich City had ﬁve diﬀerent

categories for the 2015/16 season.

But there are also considerable diﬀerences concerning the magnitudes of price premia

based on the opposition. Arsenal charged £27 for the cheapest ticket category in a game

6 T. K. QUANSAH ET AL.

against AFC Bournemouth in 2018/19, while the price for the same seat was £65.50

against Chelsea. On the other hand, Chelsea charged £52 for the cheapest ticket

against AFC Bournemouth and £61 for the same seat against Arsenal. The ticket price

range of EPL clubs during the 2018/19 season is displayed in Table 1.

To procure the option of buying match day tickets early, membership is required at

many clubs, with only limited discounts for these tickets, if any. Several clubs also

oﬀer top category A or A+ match tickets contingent on either being a club member,

having collected bonus points in previous games, or buying the tickets as price-

bundles together with tickets for other less popular games. There is no publicly available

data on the number of seats provided at these prices or within these schemes.

To model ticket prices, the dependent variables are the logarithm of prices. The log

values are preferred as the coeﬃcients can be interpreted as a proportional rather than

an absolute change. This approach is an obvious choice since the impact of a change

in any of the explanatory variables will likely have diﬀerent eﬀects at diﬀerent price

levels. Additionally, the use of logarithms will mitigate against any skewness in the dis-

tribution of prices which range from a low of £9 (£25) to a high of £70 (£97) for the

cheapest (most expensive) ticket. The models are estimated using linear regressions esti-

mated by ordinary least squares (OLS) and ﬁxed eﬀects, with each home club-season

having its own intercept term. The values of each intercept term do not vary across a

home club-season and capture a set of unobserved factors that are constant within

each panel.

For the two types of ticket prices, the models can be presented as

LogP

type

ijt

ijt−1

+ 1

ijt

(1)

The subscripts i, j, and t denote home club, away club, and season respectively; type rep-

resents the two types of tickets being modelled. LogP

type

ijt

is the log of ticket price,

is a

constant, and

is a vector of coeﬃcients to be estimated with respect to X

ijt−1

, a vector of

explanatory variables. The subscript t-1 is highly relevant as the prices of tickets for the

current season are modelled on information from the previous season that just

Table 1. EPL ticket price range for the 2018/19 season (in £).

Lowest ticket price Highest ticket price

Arsenal 27 97

Chelsea 52 87

West Ham United 30 80

Tottenham 20 75

Fulham 25 75

Brighton 30 65

Liverpool 37 59

AFC Bournemouth 32 55

Manchester United 31 53

Crystal Palace 38 53

Southampton 33 52

Newcastle United 34 50

Leicester City 26 50

Cardiﬀ City 27 42

Watford 36 42

Wolverhampton Wanderers 22 40

Burnley 30 40

Huddersﬁeld 30 30

EUROPEAN SPORT MANAGEMENT QUARTE RLY 7

concluded. Finally, 1

ijt

is the error term, which is independent, normally distributed with

a mean value of 0 and constant variance. As an additional robustness check, we also esti-

mate the models using a ﬁxed eﬀect with the home club-season panel. In this instance, the

intercept

is eﬀectively replaced by

ijt

, a vector of intercepts representing a home club

season.

Our choice of explanatory variables captures a range of categories. The ﬁrst relates to

the economic wealth of the clubs’ locations and the labour costs faced by clubs. The

second category captures a range of sporting factors, including, for example, matches

involving local rivals, club brands, and sporting performances. Finally, time elements

are also used, including weekends and public holidays. Each of the explanatory variables

is described in more detail below:

Following Reese and Mittelstaedt (2001), we include relative average weekly workplace

earnings, which is calculated as the average weekly workplace earnings over the previous

season, as an indicator of the region’s economic conditions where the home club is

located. The data source is the Centre for Cities, an independent, non-partisan urban

charity-registered policy research unit in England. Lower values of weekly workplace

earnings may indicate lower purchasing power (Solberg & Turner, 2010). However,

the nature of supporting a team can often mean a lifelong following irrespectively of

income (Cox, 2012). Fans from economically challenged, working-class regions are

often considered the most loyal. To account for the impact of inﬂation on earnings

over time, we use relative values, which are calculated as the average weekly earnings

divided by the mean value for that season. As such, the mean value of this variable

during any given season is 1. Additionally, we include the square of the term to

capture any non-linear relationship between relative average weekly workplace earnings

and ticket price.

In contrast to research that focuses on the inﬂuences on stadium demand, where

stadium quality is a determinant (Quansah, 2022) and the stadium capacity is a limiting

factor (Borland & Macdonald, 2003), capacity utilisation from the previous season might

actually be a determinant of ticket pricing. Stadium capacity determines the maximum

number of seats a club can oﬀer, which might lead to a market shortage in the face of

excess demand. As a response to the demand, clubs might raise ticket prices to

manage excess demand. For this reason, the study investigates the impact of stadium

capacity on ticket prices. For each match, the capacity utilisation from the equivalent

match from the previous season is used. Arguably, this gives the decision-makers insights

into the likely demand. The quadratic of capacity utilisation from the previous season is

also included to capture non-linearity. One might expect prices to increase at an increas-

ing rate. For those matches that can’t be paired with the prior season’s equivalent because

of promotion to the league, capacity utilisation takes a value of 0, and a dummy variable,

home promoted, is used to capture this phenomenon.

Studies by Forrest and Simmons (2002) and Forrest et al. (2004), among others, indi-

cate that games of historical and local rivalry tend to generate higher demand, ceteris

paribus. Such matches take on a special status over and above regular league matches,

with teams and supporters vying for local bragging rights. To test whether derby

matches signiﬁcantly aﬀect ticket prices, the data capture 19 historical rivalries among

the 26 clubs, which occurred in 86 matches in the ﬁve seasons observed. The variable

derby match takes the value of 1 for such matches and 0 otherwise.

8 T. K. QUANSAH ET AL.

Undoubtedly, prices for the biggest clubs in the EPL, based on revenue, reputation,

and league success, are greater than their smaller counterparts. As noted above, such

clubs have a stronger legacy, and their cumulative performances over past seasons are

likely to aﬀord them greater popularity which will be reﬂected in ticket prices. Taking

the six biggest teams can be viewed as somewhat arbitrary. We could have easily con-

structed a variable capturing the biggest four. However, in the following analysis,

dummy variables for away clubs registered signiﬁcance for the top six clubs, with the

seventh biggest club not signiﬁcantly diﬀerent from the others. Therefore, it seemed

appropriate to choose the six biggest clubs and test their pricing behaviours relative to

the rest. Furthermore, in a list of signiﬁcant club dummies for televised matches in the

EPL study by Buraimo et al. (2022), these were the top six clubs that attracted the greatest

audiences. For this reason, we include a dummy variable, Big Six at home, for Arsenal,

Chelsea, Liverpool, Manchester City, Manchester United, and Tottenham Hotspur

home matches.

Like other sports labour markets, the market for football players follows the rule of

supply and demand. The best players are highly sought-after, with the consequent

eﬀects on the clubs’ wage bills and the high transfer fees in many instances. Assuming

eﬃcient or quasi-eﬃcient markets, a club’s wage bill is likely to be a predictor of a

team’s strength and a proxy for team quality (Szymanski, 2003). However, the value of

the contract last for a speciﬁed period. A club may acquire a player for a high salary

and transfer fee, only to realise that the player does not quite fulﬁl expectations.

Changes in form and injuries are likely to weaken the relationship between labour

costs and player quality. However, the motivation is not to test or assert the strength

of the relationship between labour costs and quality but to test whether higher total

labour costs lead to higher ticket prices, or in other words, to what extent do clubs

pass on their player expenses to fans? Besides the club wage bill, we also considered

net transfer fees deriving total labour costs. A club’s wage bill will also include non-

playing staﬀ. However, the proportion of total labour costs for non-playing staﬀ is

likely to be minor, and regardless, such costs are part of the clubs’ overheads. The

measure used for this explanatory variable, ln(adjusted total labour costs), is the log of

clubs’ inﬂation-adjusted total labour costs (sum of the wages and net transfer fee).

There is evidence in favour of positive eﬀects on demand for games in which teams

with widespread national and international reputation participate (Forrest et al., 2005).

According to Czarnitzki and Stadtmann (2002), in a study on the determinants of

match attendance in the German Bundesliga, reputation and allegiance appear to be

more critical determinants of demand than the uncertainty of outcome. The EPL is

characterised by a small number of teams that dominate the league in areas such as

playing success, wage bills, team revenues, and market size. In a recent study by

Buraimo et al. ( 2022), in which they explore television audiences in the EPL, they ﬁnd

that audiences have strong preferences for teams in a particular order, having controlled

for other factors, including player quality. We thus test for the possibility that these clubs,

considered the largest clubs in the league (Arsenal, Chelsea, Everton, Liverpool, Manche-

ster City, Manchester United, and Tottenham Hotspur), might drive prices by including

dummy variables, big six (as visiting teams), for games played against any of them.

In contrast to club brand and reputation built over time, a team’s position in the pre-

vious season is used to measure its short-term performance. Following the ‘

basking-in-

EUROPEAN SPORT MANAGEMENT QUARTE RLY 9

reﬂected-glory’ phenomenon (Cialdini et al., 1976), the home- and away teams’ short-

term success could spark demand, which might inﬂuence the home team’s pricing

policy (Reese & Mittelstaedt, 2001). As such, the models use the home and away

teams’ positions at the end of the previous season as explanatory variables denoted by

the previous season’s performance. The expectation is that better performances in the

last season may positively impact the prices charged for tickets in the coming season.

In contrast to weekends, where people tend to have more leisure time, weekday games

are usually played in the evening and attract fewer attendees (Carmichael et al., 1999). To

capture these eﬀects, a dummy variable, weekend, is included and is set to 1 if the match is

scheduled for the weekend and 0 otherwise. Similarly, people tend to have more leisure

time during Bank Holidays. A dummy variable, public holiday, is included and takes the

value of 1 if the match is scheduled on a public holiday and 0 otherwise. The greater

leisure time will induce greater demand, all things being equal. However, football

clubs may respond to this expected increase in demand with increased prices. This

may, in turn, depend on the stadium’s capacity. Descriptive statistics for the variables

are presented in Table 2.

Finally, we check for collinearity by inspecting the correlation coeﬃcient matrix. The

correlation coeﬃcient values, presented in Table 3, do not cause concern and collinearity

is not considered to be an issue in the modelling.

Results and discussion

In this section, we present the ﬁndings of our empirical analysis, and we discuss the

results by highlighting the key takeaways and their implications for the research question

and the broader ﬁeld of study. Table 4 displays the results from the models.

Models (1) and (2) are linear models estimated by ordinary least squares (OLS),

whereas Models (3) and (4) are estimated using ﬁxed eﬀects with home club-season as

the panel. In the models estimated using ﬁxed eﬀects, variables that are constant

across home club-season panels are omitted, and the explanatory variables that remain

Table 2. Summary Statistics for continuous and dummy variables (n = 1,520).

Variable Mean Standard deviation Minimum Maximum

Ticket price low (in £) 35.039 9.164 9.000 65.500

Ticket price high (in £) 48.295 14.376 25.000 97.000

Relative mean weekly workplace earnings 0.997 0.192 0.747 1.283

Capacity utilisation from previous season 0.656 0.438 0 1.000

Derby matches 0.057 0.231 0 1.000

Adjusted total labour costs (2015 prices in millions) 162.638 100.714 39.756 490.810

Previous season’s home performance (league position) 11.388 6.723 1.000 25.000

Previous season’s away performance (league position) 10.970 6.557 1.000 25.000

Away team is Manchester United 0.049

Away team is Liverpool 0.049

Away team is Arsenal 0.049

Away team is Chelsea 0.049

Away team is Manchester City 0.053

Away team is Tottenham Hotspur 0.049

Away team is Everton 0.053

Weekend 0.794

Public holiday 0.037

Notes: The variable ‘capacity utilisation from previous season’ includes 0s for promoted club in which there are no match-

ing matches from the previous seasons.

10 T. K. QUANSAH ET AL.

Table 3. Correlation matrix for independent variables (n = 1,520).

Variable number

1 234567891011121314151617

1 Relative average

weekly

workplace

earnings

1.000

2 Capacity

utilisation from

season

0.105 1.000

3 Home promoted −0.135 −0.689 1.000

4 Big Six team at

home

0.460 0.229 −0.311 1.000

5 Derby matches 0.130 0.118 −0.083 0.277 1.000

6 ln(adjusted total

labour costs)

0.301 0.217 −0.280 0.782 0.240 1.000

7 Away team is

Manchester

United

0.007 0.065 0.007 −0.023 0.076 −0.028 1.000

8 Away team is

Manchester

City

0.000 0.067 0.000 0.000 0.006 0.000 −0.054 1.000

9 Away team is

Liverpool

0.007 0.069 0.007 −0.023 0.076 −0.019 −0.052 −0.054 1.000

10 Away team is

Tottenham

Hotspur

−0.022 0.059 0.007 −0.023 0.076 0.000 −0.052 −0.054 −0.052 1.000

11 Away team is

Chelsea

−0.022 0.061 0.007 −0.023 0.168 −0.022 −0.052 −0.054 −0.052 −0.052 1.000

12 Away team is

Arsenal

−0.022 0.060 0.007 −0.023 0.141 −0.019 −0.052 −0.054 −0.052 −0.052 −0.052 1.000

13 Away team is

Everton

0.000

0.068 0.000 0.000 0.006 0.000 −0.054 −0.056 −0.054 −0.054 −0.054 −0.054 1.000

14 Previous season’s

home

performance

−0.330 −0.526 0.742 −0.713 −0.182 −0.620 0.015 0.000 0.015 0.017 0.017 0.017 0.000 1.000

(Continued)

EUROPEAN SPORT MANAGEMENT QUARTERLY 11

Table 3. Continued.

Variable number

1 234567891011121314151617

15 Previous season’s

away

performance

0.018 −0.457 −0.040 0.038 −0.171 0.034 −0.216 −0.315 −0.214 −0.252 −0.241 −0.240 −0.093 −0.054 1.000

16 Weekend −0.016 −0.015 0.016 −0.039 0.005 −0.039 0.011 −0.033 −0.034 −0.004 −0.034 0.018 −0.004 0.022 0.032 1.000

17 Public holiday 0.011 0.008 −0.004 0.025 −0.041 0.025 −0.020 0.014 −0.020 −0.020 −0.020 −0.001 0.014 −0.026 0.028 −0.252 1.000

12 T. K. QUANSAH ET AL.

Table 4. Regression results: dependent variable is ln(ticket prices).

(1) (2) (3) (4)

Cheapest Most expensive Cheapest Most expensive

Coeﬃcient t Coeﬃcient t Coeﬃcient t Coeﬃcient t

Relative average weekly workplace earnings 0.438*** (15.115) 0.393*** (15.481)

Capacity utilisation from previous season −0.864*** (7.314) −0.250** (2.418) 0.047 (0.418) 0.006 (0.062)

Capacity utilisation from previous season squared 0.928*** (7.946) 0.289*** (2.829) −0.038 (0.330) 0.019 (0.179)

Home promoted 0.001 (0.040) 0.165*** (6.572)

Big Six team at home 0.032 (1.427) 0.080*** (4.108)

Derby matches 0.105*** (4.490) 0.115*** (5.649) 0.084*** (5.979) 0.108*** (8.433)

ln(adjusted total labour costs) 0.057*** (3.469) 0.158*** (11.035)

Away team is Manchester United 0.149*** (5.647) 0.146*** (6.327) 0.159*** (10.002) 0.148*** (10.173)

Away team is Manchester City 0.142*** (5.118) 0.140*** (5.785) 0.147*** (8.817) 0.142*** (9.373)

Away team is Liverpool 0.116*** (4.386) 0.125*** (5.425) 0.144*** (9.052) 0.133*** (9.160)

Away team is Tottenham Hotspur 0.136*** (5.032) 0.120*** (5.042) 0.132*** (8.098) 0.123*** (8.248)

Away team is Chelsea 0.112*** (4.121) 0.115*** (4.829) 0.143*** (8.706) 0.128*** (8.537)

Away team is Arsenal 0.101*** (3.717) 0.102*** (4.299) 0.109*** (6.657) 0.100*** (6.728)

Away team is Everton 0.017 (0.714) 0.017 (0.835) 0.024* (1.690) 0.019 (1.507)

Previous season’s home performance −0.003* (1.655) −0.012*** (8.271)

Previous season’s away performance −0.002 (1.445) −0.001 (1.002) −0.002** (2.401) −0.001 (1.493)

Weekend 0.019 (1.488) 0.020* (1.766) 0.012 (1.510) 0.016** (2.232)

Public holiday 0.029 (1.049) 0.025 (1.053) 0.026 (1.582) 0.022 (1.432)

Season 2015–16 −0.036** (2.244) −0.066*** (4.671)

Season 2016–17 −0.088*** (5.547) −0.104*** (7.459)

Season 2017–18 −0.102*** (6.220) −0.100*** (6.974)

Season 2018–19 −0.112*** (6.856) −0.059*** (4.093)

Constant 2.033*** (6.530) 0.538** (1.975) 3.449*** (158.715) 3.740*** (188.806)

Observations 1520 1520 1520 1520

Adjusted r

0.422 0.646 0.318 0.335

Season dummies Yes Yes

Home-season ﬁxed eﬀects Yes Yes

Notes: Models (1) and (2) use linear regression estimated using OLS whilst models (3) and (4) are ﬁxed eﬀects with home club-season as the panel. Absolute t statistics in parenthesis. * p < 0.10,

** p < 0.05, *** p < 0.01.

EUROPEAN SPORT MANAGEMENT QUARTERLY 13

are those that vary within the panel. Comparing the coeﬃcients of these variables from

the ﬁxed eﬀects models with the OLS equivalent reveals that the coeﬃcients are statisti-

cally similar. As such, much of the attention of this section will be on the OLS model.

Each of the explanatory variables is discussed in turn across the two models.

The ﬁrst of the explanatory variables is the relative average weekly workplace earnings.

The quadratic for the variable was not signiﬁcant and thus dropped. The earnings vari-

able is statistically signiﬁcant and suggests that earnings over the examined period aﬀect

pricing. Higher earnings positively impact the prices of both the cheapest and most

expensive tickets for all clubs. This result suggests that clubs are aware of the level of

wealth in their market and adjust their prices accordingly. The coeﬃcient for the most

expensive ticket is greater than that of the cheapest ticket, which is to be expected.

However, the two coeﬃcients are not substantially diﬀerent at the 5% level. Still, the

greater coeﬃcient for the most expensive ticket is suggestive that the more aﬄuent con-

sumers are asked to pay a greater increase in ticket prices.

The results demonstrate that stadium utilisation matters and that the capacity utilis-

ation of the same match in the previous season aﬀects prices in the following season. The

quadratic value suggests that the relation with ticket prices is a U-shape. However, the

turning point of the curve is below the minimum values for capacity utilisation. This

result suggests that the negative portion of the curve is redundant and that as capacity

increases, prices increase but at an increasing rate. This result highlights that as

demand increases (measures taken from last season’s match) and fewer seats remain,

prices rise at an increasing rate, and the asking prices for both types of tickets are

increased.

For those teams who are newly promoted to the EPL, there is no capacity utilisation

since the equivalent match did not occur last season. The ticket prices for these clubs,

based on the coeﬃcient of the home promoted variable, is an increase of 17.9% for the

most expensive tickets. For the cheapest ticket, there is no signiﬁcant impact.

However, this is based on the utilisation of 0, given that there was no equivalent

match from the previous season. However, for an incumbent club with mean capacity

utilisation, the price increase for the most expensive ticket would be 25.4%. Thus, the

price increase by promoted teams for the most expensive tickets is 7.5% less than the

average incumbent team. This result suggests that newly promoted teams are looking

to extract surplus from the most aﬄuent part of their markets but do not do so to the

same extent as incumbent teams (Dietl et al., 2015). Perhaps they lack the commercial

conﬁdence to increase prices to the same degree, or such increases are likely to be too

high, given that the previous season’s prices are for the division below.

The coeﬃcient for the variable Big Six at home is signiﬁcantly diﬀerent from 0 for the

most expensive tickets. For the cheapest tickets, the prices of the Big Six are not dissimilar

to other clubs, although what is unclear is the proportion of seats available at diﬀerent

prices. For Big Six clubs, the most expensive tickets are 8.3% more expensive than

their non-Big Six counterparts, controlling for other factors, which is expected, given

their historical success, commercial might, and global strength. This result complements

that of Buraimo et al. (2022), who highlight the big teams generate the bulk of the interest

in televised football in the EPL.

Derby matches of local interest have a positive impact on the cheapest and most

expensive tickets, which are estimated to be 10.5% and 11.5%, respectively. Clubs in

14 T. K. QUANSAH ET AL.

the EPL are keen to charge a premium for local rivalry, and this is to be expected since

such matches tend to be anticipated by fans and oﬀer the chance to gain local bragging

rights. Whilst the increase for the most expensive ticket prices is higher than that of the

cheapest ticket, the diﬀerence is not statistically signiﬁcant, but the magnitude indicates

that the more expensive tickets experience a higher price increase because of derby

matches. Furthermore, whilst the percentage increase is equivalent, the absolute increases

will be higher for the most expensive tickets, given the higher mean price. This result is

interesting since many studies of football demand (e.g. Buraimo et al., 2011) show that

derby matches positively inﬂuence stadium attendance. As such, there may be greater

scope for increasing prices for derby matches since the price increases do not seem to

diminish the additional stadium attendance. The ﬁndings also support observations

made on the pricing eﬀects of derbies in intercollegiate athletics (Sanford, 2016).

As noted earlier, labour costs are the most signiﬁcant costs faced by football clubs,

and these generally take the form of transfer fees needed to acquire players and their

wages once recruited.

We note that the labour costs of clubs are associated with a

change in price for the most expensive tickets and the cheapest ones. The positive

and signiﬁcant signs of the coeﬃcient of ln(adjusted total labour costs) for both

ticket types suggest that clubs look to recoup at least some of the costs they face in

the labour market; much of these are passed on to those fans paying the highest

prices. The magnitude of the coeﬃcient for the most expensive tickets is nearly three

times that of the cheapest ticket highlighting the extent to which the more expensive

tickets are used to generate more revenue. However, we again highlight that without

knowledge of how the seats are apportioned across diﬀerent price points, it is imposs-

ible to explore which tickets generate greater revenue. Of important note is that most of

the revenue to EPL comes from the broadcast market. For the 2019–2020 season, the

revenues from the broadcast market for the league made up 52% of total revenue

(Deloitte, 2021). Hence, clubs are likely to pass on a more modest but signiﬁcant

amount of the players’ labour costs to fans in the stadium, given that the broadcast

market can bear most of these costs.

The next set of explanatory variables is the visiting teams. In the analysis, we ﬁnd that

the six clubs, Manchester United, Manchester City, Liverpool, Tottenham Hotspur,

Chelsea, and Arsenal, as visiting teams on average elicit price increases for the most

expensive tickets, and in that descending order of magnitude. However, the diﬀerences

across the clubs are negligible and not statistically diﬀerent. The club next on the list

was Everton, however, matches in which Everton was the visiting team did not generate

price increases that were any diﬀerent from those other clubs in the sample. In essence,

any of the Big Six clubs as the visiting team elicit price increases by the host clubs for both

types of tickets. Additionally, the price increases were consistently applied across the two

types of tickets indicating that clubs adopt a consistent approach to extracting surplus

from the cheapest and most expensive tickets when based on the visiting Big Six clubs.

However, since the coeﬃcients can be interpreted as percentage changes, a greater absol-

ute amount will come from the most expensive tickets. For example, the coeﬃcients for

Liverpool as the away team for the cheapest and most expensive prices are 12.3% and

13.3%, respectively. Given the mean prices of £40 and £58 charged for the cheapest

and most expensive tickets, respectively, by a team hosting Liverpool in the 2018–2019

season, the additional increases in prices are estimated to be £4.64 and £7.25 for the

EUROPEAN SPORT MANAGEMENT QUARTERLY 15

cheapest and most expensive tickets respectively and clearly a higher margin for the most

expensive ticket in contrast to what the coeﬃcients might suggest.

Furthermore, it is evident that host clubs take advantage of the positive externalities

oﬀered to them, which means that fans not only get to see well-known teams, but also

high-level players, as elite clubs and superior player quality tend to go hand in hand. Fur-

thermore, this underlines the tension between the Big Six clubs and their failed attempt to

establish a European Super League. The positive externalities that arise from hosting

these clubs are high. Any threat to the current setting will most likely be met with resist-

ance from the clubs accruing these externalities, which is a relevant issue. Still, any dis-

cussions and analyses of the European Super League are outside the scope of this study.

The performances of the teams in the previous season oﬀer some interesting results.

That of the home team has no (meaningful) signiﬁcant impact on the cheapest tickets but

inﬂuences the most expensive ones. An improvement in home team performance of one

place in the league standings from the previous season increases prices for the most

expensive tickets by 1.2%. To put this into context, if we take two hypothetical identical

home teams with the only diﬀerence being their ﬁnishing position last season and that

diﬀerence being a one standard deviation diﬀerence (6.7 places), the home club is esti-

mated to charge an extra £3.88 for the most expensive ticket. The impact on revenue

will depend on the number of tickets available at this price. These results can be put

in contrast to ﬁndings from the North American sports market, where team performance

was one of the most critical drivers of primary and secondary ticket pricing (Diehl et al.,

2016; Reese & Mittelstaedt, 2001; Rishe & Mondello, 2003 ).

The coeﬃcient of the variable weekend is not signiﬁcantly diﬀerent from 0 for the

cheapest tickets. However, there is a hint of signiﬁcance for the most expensive tickets,

especially in the ﬁxed eﬀects model. One can generally assume that the prices of

tickets across the weekend, weekdays and public holidays are very similar.

Managerial implications

There are some lessons and implications from these results for managers at clubs and

similarly at the league level. From the perspective of clubs, the results from the model

can be used as a benchmark when considering price increases. Whilst the prices set by

ﬁrms can be determined using diﬀerent approaches, as mentioned earlier in this study,

the extent to which fans can bear and are willing to accept price increases is a factor.

In conventional markets, ﬁrms can readily increase prices, and consumers can either

accept such increases or stop consumption, presumably to seek an alternative. Football

attendance, however, is not a conventional market since fans and supporters have

strong aﬃnities and such aﬃnities spill over into ownership even if such ownership is

not legal and more psychological. As such, there are limits to price increases that consu-

mers will ﬁnd acceptable. Beyond this limit, relations between clubs and their fans and

supporter can become strained. In some instances, the outcomes can be unpalatable.

For example, in a Premier League ﬁxture in 2016 between Liverpool and Sunderland at

Anﬁeld, over 10,000 Liverpool supporters left the match in protest over proposed

increases in ticket prices. The club announced that prices for the most expensive

match day ticket for the following season would be increased to £77, up from £59.

The supporters duly got up and walked out of the stadium in the 77th minute; this

16 T. K. QUANSAH ET AL.

was in addition to ﬂying black ﬂags, rather than the usual red ones, and chants accusing

the owners of greed (Press Association, 2016). The club owners apologised, and the price

increase was cancelled. Arsenal and Tottenham Hotspur have also, in the past, retreated

over price increases.

The relevance of the models in this study is that the prices used are actual prices borne

by attendees, and therefore, the coe ﬃ cients of the variables capture price increases that

the market is willing to pay. Thus, the models can be used to benchmark acceptable price

increases. This is not to suggest that fans and supporters will not accept price increases

that are even greater but to suggest that beyond these, there is the risk of conﬂict. The

extent of any proposed price increases will depend on the magnitude of the signiﬁcant

variables in the models and the initial prices at which the increases are being applied.

Furthermore, this study has implications for sports leagues regarding revenue allo-

cation and distribution. While some leagues redistribute revenues, particularly from

broadcast markets, they may struggle to ﬁnd the optimal distribution mechanism. It’s

important to note that clubs have the ability to design their own pricing strategies,

which can serve as a form of quasi-redistribution. Since all clubs host every member

of the Big Six (except the Big Six, who will have just ﬁve such occasions), the positive

externality imposed by the Big Six eﬀectively reassigns resources from the bigger, weal-

thier teams to the smaller ones. This type of revenue redistribution is desirable in sports

leagues and can be achieved through eﬀective pricing strategies, with success depending

on the clubs’ in-house management expertise. This quasi-redistribution approach to

stadium revenue has the advantage of not requiring league intervention and allowing

clubs to retain their own stadium revenue.

Conclusion

This paper examines the factors inﬂuencing ticket pricing in the English Premier League

(EPL). It ﬁnds that the cheapest and most expensive ticket prices are inﬂuenced by factors

such as local earnings, stadium capacity utilisation from the previous season, whether the

team is a ‘Big Six’ club, the status of the opponent and team rivalries. These variables

perform as expected in the models. The study shows that clubs set prices based on the

market’s ability and willingness to pay higher prices. Clubs are responsive to local pros-

perity, which is reﬂected in ticket prices. Additionally, clubs take advantage of limited

stadium capacity by increasing prices as capacity decreases, forcing consumers to

compete on price and pay higher premiums as capacity approaches zero.

While the Big Six clubs of the league are the driving force behind the EPL’s popularity

in international markets and its high broadcasting income, positive externalities accrue to

smaller teams who can freeride within both the broadcast and stadium markets. In

response to the takeover of Newcastle United in 2021 by the sovereign wealth fund of

Saudi Arabia, the remaining Premier League clubs passed a temporary ban on related

party transactions, a move to restrict clubs from agreeing on sponsorship deals with com-

panies linked to their owners. As Financial Fair Play regulations oblige clubs to balance

football-related expenditures, such as transfer fees and wages, with income, the rule

hinders clubs with wealthy shareholders, such as Newcastle United, in buying the best

players, building up a strong reputation through sporting success, and becoming a big

club. Paradoxically, restricting or limiting clubs from transitioning from small and

EUROPEAN SPORT MANAGEMENT QUARTERLY 17

medium to big clubs constrains the extent to which other clubs will be able to generate

additional revenue and freeride, given the ﬁndings from this study.

Clubs look to charge stadium attendees a price premium for short-run home sporting

success (at least for the most expensive tickets). The study’s results also suggest that clubs

tend to recoup some player costs by passing these on to the cheapest and most expensive

ticket prices (and possibly on all ticket prices in between). Whist the broadcast market for

the EPL does an extraordinary job of providing resources to clubs to spend in the labour

market, clubs are still compelled to extract resources from fans given their labour costs.

We further conclude that ticket prices and, consequently, match day revenues serve as

a means of redistributing resources from larger to smaller clubs. This is because clubs

adjust their prices based on the reputation and recent success of the teams they are sched-

uled to play against.

There are limitations and assumptions associatedwith this study. First, the study uses only

two price points: the lowest and the highest available ticket prices of the home club in a par-

ticulargame.The numberof price points will diﬀeramongclubs, and this is oftennot publicly

available. Thus, clubs can use ticket prices as a marketing tool, oﬀering very limited seats in

their cheapest advertised category. Second, the study focuses on how EPL clubs price their

match day tickets. While this reveals the relationship between covariates and prices, the

analysis does not provide an answer to how much fans are willing to pay. Third, the study

cannot answer the question of whether price decisions by clubs are well-founded or

whether clubs copy pricing methods from each other. For this reason, there is scope for com-

bining the research with a qualitative angle by including interviews with club managers to

examine the nature of their decision-making process, objective-setting, and pricing tools.

Notes

1. Wage costs and ticket prices of a few teams were not available for all years, such as those for

Manchester City and Everton for all ﬁve seasons, Newcastle United’s most expensive match

day tickets for the four seasons they played in the EPL, as well as Hull City’s ticket match day

pricing data for the 2014/15 season.

2. There is empirical logic to the notion of Big Six. In a study of television audience demand by

Buraimo et al. (2022), the six biggest clubs boosted demand signiﬁcantly compared with the

others. As such, the reputation of the Big Six can be supported empirically.

3. Additionally, we check for multicollinearity using variance inﬂation factors for each vari-

able. The results suggest that multicollinearity is not an issue, as the highest value is 5.49,

well within the threshold value of 10. Furthermore, we use the condition index. The con-

dition number of 28.61 again conﬁrms that the models perform well against this test.

4. See Quansah et al. (2021) for an examination of the importance of club revenues for player

salaries and transfer expenses in the EPL.

Disclosure statement

No potential conﬂict of interest was reported by the author(s).

ORCID

Babatunde Buraimo http://orcid.org/0000-0003-3928-5624

Markus Lang

http://orcid.org/0000-0002-6837-790X

18 T. K. QUANSAH ET AL.

References

Berri, D. J., & Schmidt, M. B. (2006). On the road with the National Basketball Association’ssuperstar

externality. Journal of Sports Economics, 7(4), 347–358. https://doi.org/10.1177/1527002505275094

Borland, J., & Macdonald, R. (2003). Demand for sport. Oxford Review of Economic Policy, 19(4),

478–502. https://doi.org/10.1093/oxrep/19.4.478

Buraimo, B., Forrest, D., McHale, I. G., & Tena, J. d. D. (2022). Armchair fans: Modelling audience

size for televised football matches. European Journal of Operational Research, 298(2), 644–655.

https://doi.org/10.1016/j.ejor.2021.06.046

Buraimo, B., Paramio, J. L., & Campos, C. (2011). The impact of televised football on stadium

attendances in English and Spanish league football. In D. Hassan & S. Hamil (Eds.), Who

owns football? (pp. 129–142). Routledge.

Carmichael, F., Millington, J., & Simmons, R. (1999). Elasticity of demand for Rugby League

attendance and the impact of BskyB. Applied Economics Letters, 6(12), 797–800. https://doi.

org/10.1080/135048599352196

Cialdini, R. B., Borden, R. J., Thorne, A., Walker, M. R., Freeman, S., & Sloan, L. R. (1976). Basking

in reﬂected glory: Three (football) ﬁeld studies. Journal of Personality and Social Psychology, 34

(3), 366–375. https://doi.org/10.1037/0022-3514.34.3.366

Clowes, J., & Clements, N. (2003). An exploration of discriminatory ticket pricing practice in the

English football Premier League. Managing Leisure, 8(3), 105–120. https://doi.org/10.1080/

1360671032000123645

Coates, D., & Humphreys, B. R. (2007). Ticket prices, concessions and attendance at professional

sporting events. International Journal of Sport Finance, 2(3), 161.

Conn, D. (2014). Premier League ticket prices defy the very culture that built the game. Retrieved

August 8, 2021, from https://www.theguardian.com/football/blog/2014/jul/28/premier-league-

ticket-prices-football

Courty, P. (2003

). Some economics of ticket resale. Journal

of Economic Perspectives , 17(2), 85–97.

https://doi.org/10.1257/089533003765888449

Cox, A. (2012). Live broadcasting, gate revenue, and football club performance: Some evidence.

International Journal of the Economics of Business , 19(1), 75–98. https://doi.org/10.1080/

13571516.2012.643668

Czarnitzki, D., & Stadtmann, G. (2002). Uncertainty of outcome versus reputation: Empirical evi-

dence for the First German Football Division. Empirical Economics, 27(1), 101–112. https://doi.

org/10.1007/s181-002-8361-7

Deloitte. (1998). Deloitte & Touche annual review of football ﬁnance. D. T. Tohmatsu.

Deloitte. (2013). Annual Review of Football Finance 2013. Retrieved August 26, 2019, from ARFF

2012 (deloitte.com)

Deloitte. (2016). Annual review of football ﬁnance 2016. Retrieved August 26, 2019, from https://

www2.deloitte.com/content/dam/Deloitte/uk/Documents/sports-business-group/deloitte-uk-a

nnual-review-of-football-ﬁnance-2016.pdf

Deloitte. (2019). Annual review of football ﬁnance 2019. Retrieved August 26, 2019, from https://

www2.deloitte.com/uk/en/pages/sports-business-group/articles/deloitte-football-money-league

.html

Deloitte.(2021). Annual review of footballﬁnance. Retrieved July 20, 2022,from https://www2.deloitte.

com/uk/en/pages/sports-business-group/articles/annual-review-of-football-ﬁnance.html

Diehl, M. A., Drayer, J., & Maxcy, J. G. (2016). On the demand for live sport contests: Insights from

the secondary market for National Football League tickets. Journal of Sport Management, 30(1),

82–94. https://doi.org/10.1123/JSM.2014-0034

Dietl, H., Grossmann, M., Hefti, A., & Lang, M. (2015). Spillovers in sports leagues with promotion

and relegation. Scottish Journal of Political Economy, 62(1), 59–74. https://doi.org/10.1111/sjpe.

12060

Drayer, J., Rascher, D. A., & McEvoy, C. D. (2012).

An examination of underlying consumer

demand and sport pricing using secondary market data. Sport Management Review, 15(4),

448–460. https://doi.org/10.1016/j.smr.2012.03.005

EUROPEAN SPORT MANAGEMENT QUARTERLY 19

Everton. (2019, November 7). Everton announces simpliﬁed match-by-match ticket prices.

Retrieved August 15, 2021, from https://www.evertonfc.com/news/1276887/everton-

announces-simpliﬁed-match-by-match-ticket-prices

Ferguson, D. G., Stewart, K. G., Jones, J., & Le Dressay, A. (1991 ). The pricing of sports events: Do

teams maximize proﬁt? The Journal of Industrial Economics, 39(3), 297–310. https://doi.org/10.

2307/2098521

Forrest, D., & Simmons, R. (2002). Team salaries and playing success in sports: A comparative per-

spective. Zeitschrift für Betriebswirtschaft, 72(4), 221–238.

Forrest, D., Simmons, R., & Buraimo, B. (2005). Outcome uncertainty and the couch potato audi-

ence. Scottish Journal of Political Economy, 52(4), 641–661. https://doi.org/10.1111/j.1467-9485.

2005.00360.x

Forrest, D., Simmons, R., & Szymanski, S. (2004). Broadcasting, attendance and the ineﬃciency of

cartels. Review of Industrial Organization, 24(3), 243–265. https://doi.org/10.1023/B:REIO.

0000038274.05704.99

Fort, R. (2004). Inelastic sports pricing. Managerial and Decision Economics, 25(2), 87–94. https://

doi.org/10.1002/mde.1108

Forti, P., & De Lima, L. M. (2021). Quanto vale o show? O ingresso de futebol sob uma perspectiva

hedônica. ReMark – Revista Brasileira de Marketing, 20(3), 105–134. https://doi.org/10.5585/

remark.v20i3.15802

Hausman, J. A., & Leonard, G. K. (1997). Superstars in the National Basketball Association: Economic

value and policy. Journal of Labor Economics, 15(4), 586–624. https://doi.org/10.1086/209839

Jewell, R. T. (2017). The e

ﬀect

of marquee players on sports demand: The case of US Major League

Soccer. Journal of Sports Economics, 18(3), 239–252. https://doi.org/10.1177/1527002514567922

Kemper, C., & Breuer, C. (2015). What factors determine the fans’ willingness to pay for

Bundesliga tickets? An analysis of ticket sales in the secondary market using data from

ebay.de. Sport Marketing Quarterly, 24(3), 142–158.

Kemper, C., & Breuer, C. (2016a). Dynamic ticket pricing and the impact of time – an analysis of

price paths of the English Soccer Club Derby County. European Sport Management Quarterly,

16(2), 233–253. https://doi.org/10.1080/16184742.2015.1129548

Kemper, C., & Breuer, C. (2016b). How eﬃcient is dynamic pricing for sport events? Designing a

dynamic pricing model for Bayern Munich. International Journal of Sport Finance, 11(1), 4–25.

Lawson, R. A., Sheehan, K., & Stephenson, E. F. (2008). Vend it like Beckham: David Beckham’s

eﬀect on MLS ticket sales. International Journal of Sport Finance, 3(4), 189.

Lovelock, C. H., & Wirtz, J. (2004). Services marketing: People, technology, strategy.

Michie, J., & Oughton, C. (2004). Competitive balance in football: Trends and eﬀects. The

Sportsnexus London.

Morehead, C. A., Shapiro, S. L., Reams, L., McEvoy, C., & Madden, T. M. (2021). A qualitative

exploration of ticket-pricing decisions in intercollegiate athletics. Journals of Issues in

Intercollegiate Athletics, 14, 547–573. https://csri-jiia.org/a-qualitative-exploration-of-ticket-

pricing-decisions-in-intercollegiate-athletics/

Neale, W. C. (1964). The peculiar economics of professional sports: A contribution to the theory of

the ﬁrm in sporting competition and in market competition. The Quarterly Journal of

Economics, 78(1), 1–14. https://doi.org/10.2307/1880543

Nufer, G., & Fischer, J. (2013). Ticket pricing in European football-analysis and implications. Sport

and Art, 1(2),

49–60. https://doi.org/10.13189/saj.2013.010205

Press Association. (2016). Liverpool fans’ walkout protest: around 10,000 leaves in the 77th minute

over ticket prices. The Guardian. Retrieved January 20, from https://www.theguardian.com/

football/2016/feb/06/liverpool-fans-walkout-thousands-ticket-price-protest

Quansah, T., Frick, B., Lang, M., & Maguire, K. (2021). The importance of club revenues for player

salaries and transfer expenses—how does the coronavirus outbreak (COVID-19) impact the

English Premier League? Sustainability, 13(9), 5154. https://doi.org/10.3390/su13095154

Quansah, T. K. (2022). New sports stadia for Africa? The impact of sportscape features on attend-

ance intentions in sub-Saharan African club football. European Sport Management Quarterly,

1–24. https://doi.org/10.1080/16184742.2022.2125549

20 T. K. QUANSAH ET AL.

Rascher, D. A., McEvoy, C. D., Nagel, M. S., & Brown, M. T. (2007). Variable ticket pricing in

Major League Baseball. Journal of Sport Management, 21(3), 407–437. https://doi.org/10.

1123/jsm.21.3.407

Reese, J. T., & Mittelstaedt, R. D. (2001). An exploratory study of the criteria used to establish NFL

ticket prices. Sport Marketing Quarterly, 10(4), 223–230.

Rishe, P., & Mondello, M. (2004). Ticket price determination in professional sports: An empirical

analysis of the NBA, NFL, NHL, and Major League Baseball. Sport Marketing Quarterly, 13(2),

104–112.

Rishe, P. J., & Mondello, M. J. (2003). Ticket price determination in the National Football League:

A quantitative approach. Sport Marketing Quarterly, 12(2), 72–79.

Sanford, K. S. F. (2016). Assessing the intensity of sports rivalries using data from secondary

market transactions. Journal of Sports Economics, 17(2), 159–174. https://doi.org/10.1177/

1527002514527112

Solberg, H. A. (2001). Finding a price policy that maximises the ticket revenues at sporting events:

A focus on price bundling1. European Sport Management Quarterly, 1(4), 300–319. https://doi.

org/10.1080/16184740108721904

Solberg, H. A., & Turner, P. (2010). Exporting sports rights to overseas markets: The case of

European football. Sport in Society, 13(2), 354–366. https://doi.org/10.1080/17430430903523119

Szymanski, S. (2003). The economic design of sporting contests. Journal of Economic Literature, 41

(4), 1137–1187. https://doi.org/10.1257/jel.41.4.1137

Woratschek, H., Kaiser, M., Durchholz, C., & Strobel, T. (2020). Preisaufschläge für die sportliche

relevanz. German Journal of Exercise and Sport Research, 50(3), 406–416. https://doi.org/10.

1007/s12662-020-00661-y

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