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Journal of Global Sport Management
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rgsm20
No Crowds, No Home Advantage in Football
during the COVID-19 Season: Are Crowds Able to
Manipulate All but the Best Referees’ Behaviour?
Alan Nevill, Alastair Pearson & Tom Webb
To cite this article: Alan Nevill, Alastair Pearson & Tom Webb (2022): No Crowds, No
Home Advantage in Football during the COVID-19 Season: Are Crowds Able to Manipulate
All but the Best Referees’ Behaviour?, Journal of Global Sport Management, DOI:
10.1080/24704067.2022.2136102
To link to this article: https://doi.org/10.1080/24704067.2022.2136102
Published online: 09 Nov 2022.
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JOURNAL OF GLOBAL SPORT MANAGEMENT
No Crowds, No Home Advantage in Football during
the COVID-19 Season: Are Crowds Able to Manipulate
All but the Best Referees’ Behaviour?
Alan Nevill
a
, Alastair Pearson
b
and Tom Webb
b
a
Faculty of Education Health and Well-Being, University of Wolverhampton, Walsall, UK;
b
School of Sport,
Health & Exercise Science, University of Portsmouth, Portsmouth, UK
ABSTRACT
This study confirmed that without crowds there was no home
advantage in association football during the COVID-19 2020-21
season. Consequently, we sort to answer the obvious question,
‘Are crowds influencing referees’ behaviour?’ The number of home
and away red and yellow cards awarded in the ‘no crowd’ COVID-19
2020-21 season (all 4 top English divisions) were compared with
the home and away cards awarded during the previous 10 ‘crowd’
seasons (2010-11 to 2019-20). Results revealed that there was no
home advantage in red and yellow cards awarded by referees in
all 4 English leagues/divisions during the COVID-19 2020-21 season.
Referees awarded significantly more cards to away players when
adjudicating with crowds (seasons 2010-11 to 2019-20). However,
in more recent ‘crowd’ seasons, Premier League referees are less
susceptible to such influences with a narrowing of the gap
between home and away yellow cards, suggesting that their prepa-
ration, management and training provides them with an element
of ‘crowd immunity’. It would appear that home crowds are able
to influence all but the very best referees’ behaviour. These new
insights provide important information for the training and man-
agement of referees.
1. Introduction
The COVID-19 pandemic has provided a unique opportunity to assess the influence
of crowds (or more accurately, their absence) on the home advantage. The evidence
that the absence of crowds has reduced the home advantage is reasonably convincing
(e.g. Sors et al., 2021) although not entirely conclusive, as their results were based
on an incomplete season. The fact that team quality (a far more dominant and
influential effect than home advantage) cannot be satisfactorily removed until both
teams in the league have played each other both home and away (in a balanced
league/season set of fixtures), can the effect of home advantage be more clearly
© 2022 Global Alliance of Marketing & Management Associations (GAMMA)
CONTACT Alan Nevill a.m.nevill@wlv.ac.uk Faculty of Education Health and Well-Being, University of
Wolverhampton, Walsall WS1 3BD, UK.
https://doi.org/10.1080/24704067.2022.2136102
ARTICLE HISTORY
Received 8 March 2022
Revised 13 September
2022
Accepted 17 September
2022
KEYWORDS
Soccer; spectators;
Premier League; sports
ocials; crowd immunity
2 A. NEVILL ETAL.
determined (see Clarke & Norman, 1995). For example, as explained by Clarke and
Norman (1995), if Team A beats Team B by 5-0 at home, but Team B beats Team
A 1-0 at home, both teams benefit from home advantage but team A’s quality is 4
goals better than team B but with an average home advantage of 3 goals. These
insights and calculations can only be achieved when the COVID-19 2020-21 season’s
set of fixtures was completed. Now that this COVID-19 season finally ended on
20th May 2021, the evidence is much clearer and convincing. The Premier League
final results table ended with 144 home wins and 153 away wins (83 draws), sug-
gesting that home advantage was absent in this strange recent COVID-19 season
(see Table 1).
The COVID-19 pandemic appears to help us to answer one of the enduring
questions in sport, that being ‘what is the cause of home advantage?’ Clearly the
absence of crowds in the COVID-19 2020-21 season appears to have influenced the
outcome of games, although we cannot be certain whether this ‘crowd’ effect has
influenced the behaviour of either the players or referees. In an attempt to under-
stand and answer this intriguing question as to whether crowds can influence referees’
behaviour (via their judgements) objectively, we can observe whether the number
or proportion of home and away red and yellow cards changed when referees were
adjudicating behind closed doors (without crowds during the complete COVID-19
season) or in front of crowds (in 10 complete seasons prior to COVID-19). It is
this gap in the literature that the current study will attempt to solve. Hence, the
purpose of the current article is to assess the impact of crowds (their presence or
absence) on home advantage, by comparing the number of red and yellow cards
awarded in the recent no crowd COVID-19 2020-21 season with the number of red
and yellow cards awarded in the 10 previous crowd seasons 2010-11 to 2019-20
across the four professional divisions in England.
2. Literature Review
2.1. Referees Decisions and Home Advantage
Covid-19 presented a unique opportunity to examine football fixtures and referee
decision making without the presence of crowds. This decision-making process,
often considered through the lens of home advantage in sport, can operate with
referees performing in high stress, high consequence professional sporting environ-
ments, such as those with crowds (Grabowski & Sanborn, 2003). However, because
of the high stress performance environment, there can be unconscious errors in
thinking and decision making, namely cognitive bias, which can be applied to
describe people’s systematic but purportedly flawed patterns of responses to judgment
and decision problems (Wilke & Mata, 2012).
In individuals, cognitive bias can be related to decision-making as people learn
and develop thinking patterns. Often, these patterns are positive and reflect rational
decision-making behaviour. However, other patterns can lead to poor choices and
compromised decision making (Phillips-Wren etal., 2019a). Individuals can overcome
some of their biases through learning new decision-making approaches or techniques,
although there is an individual difference component that influences how people
JOURNAL OF GLOBAL SPORT MANAGEMENT 3
Table 1. End-of-season table for the English Premier League, for the COVID-19 2020-21 season.
P HW HD HL Hf Ha HGD AW AD AL Af Aa AGD GD Pts hi ui
Manchester City 38 13 2 4 43 17 26 14 3 2 40 15 25 51 86 0.04 1.26
Manchester United 38 9 4 6 38 28 10 12 7 0 35 16 19 29 74 −0.51 0.99
Liverpool 38 10 3 6 29 20 9 10 6 3 39 22 17 26 69 −0.46 0.88
Chelsea 38 9 6 4 31 18 13 10 4 5 27 18 9 22 67 0.21 0.45
Leicester City 38 9 1 9 34 30 4 11 5 3 34 20 14 18 66 −0.57 0.74
West Ham United 38 10 4 5 32 22 10 9 4 6 30 25 5 15 65 0.27 0.25
Tottenham Hotspur 38 10 3 6 35 20 15 8 5 6 33 25 8 23 62 0.38 0.39
Arsenal 38 8 4 7 24 21 3 10 3 6 31 18 13 16 61 −0.57 0.69
Leeds United 38 8 5 6 28 21 7 10 0 9 34 33 1 8 59 0.32 0.04
Everton 38 6 4 9 24 28 −4 11 4 4 23 20 3 −1 59 −0.40 0.18
Aston Villa 38 7 4 8 29 27 2 9 3 7 26 19 7 9 55 −0.29 0.38
Newcastle Und 38 6 5 8 26 33 −7 6 4 9 20 29 −9 −16 45 0.10 −0.44
Wolves Wanderers 38 7 4 8 21 25 −4 5 5 9 15 27 −12 −16 45 0.43 −0.61
Crystal Palace 38 6 5 8 20 32 −12 6 3 10 21 34 −13 −25 44 0.04 −0.64
Southampton 38 8 3 8 28 25 3 4 4 11 19 43 −24 −21 43 1.49 −1.26
Brighton & Hove A 38 4 9 6 22 22 0 5 5 9 18 24 −6 −6 41 0.32 −0.31
Burnley 38 4 6 9 14 27 −13 6 3 10 19 28 −9 −22 39 −0.23 −0.43
Fulham 38 2 4 13 9 28 −19 3 9 7 18 25 −7 −26 28 −0.68 −0.31
West Bromwich A 38 3 6 10 15 39 −24 2 5 12 20 37 −17 −41 26 −0.40 −0.82
Sheeld United 38 5 1 13 12 27 −15 2 1 16 8 36 −28 −43 23 0.71 −1.43
Totals 144 83 4 153 83 --4 0.21 0
Note that in the titles of columns in Table 1, we use the abbreviations; H = home; A = away; W = win; D = draw; L = loss; f = goals for; a = goals against; GD = goal dierence;
h
i
= each individual team’s HA; u
i
= each individual team’s team rating.
4 A. NEVILL ETAL.
process and use information (Phillips-Wren etal., 2019b). Moreover, ensuring effective
decision-making means making decisions that result in attaining desired goals from
an initial or original decision in order to understand bias in judgments and how to
prevent or minimise any such bias (Plessner & Haar, 2006). Recent research has
attempted to further develop this area of literature. The decision-making variability
of officials has been linked to expertise, with the importance of game/situation based
opportunities for decision-making practice identified as essential (Russell et al., 2022).
Moreover, Raab etal. (2021) have introduced a threshold process model in officiating,
derived from Decision Field Theory. This model attempts to shift the discussion
towards a dynamic model, focused on the intra-individual and inter-individual level,
and contends that referees move from applying the rules of the game to ‘managing’
the game when a subjective threshold of aggressive play is reached.
Consideration of decision making in officiating has examined different sports
(Hancock et al., 2021; Samuel et al., 2021). Research has considered referee and
sports official bias in football (Albanese et al., 2020; Erikstad & Johansen, 2020).
Moreover, O’Brien and Mangan (2021) focused on the potential causes of uncon-
scious bias between 1978 and 2019 in Australian rugby league. This research iden-
tified that home advantage was the most likely indicator of any unconscious bias,
even among the elite professional rugby league referees. In addition, home advantage
varied widely around the average or expected values and that clubs fared significantly
better or worse under particular referees.
The introduction of technology has also affected the decision-making and per-
formance of sports officials, although not always in a positive manner. Dawson etal.
(2020) contended that there were unintended consequences associated with the
introduction, and subsequent extended role of the television match official (a
technology-aided referee system in rugby union) in connection to the incidence of
sanctionable offences in the group stages of the European Rugby Cup (ERC) and
European Rugby Champions Cup (ERCC) over 15 seasons from 2000/01 to 2015/16.
The role of technology and the introduction of the television match official has
increased the number of yellow cards awarded to away teams, whilst also increasing
home bias or advantage (Dawson et al., 2020). Dawson et al. (2020, p. 452) state
that the constant scrutiny of officials and their judgements during fixtures in rugby
union, ‘…has prompted the use of the television match official to help to remove
subjective judgment’. However, in football, Video Assistant Referees (VAR) were
introduced in the Premier League in 2019, but the home advantage gap had nar-
rowed long before this date (Nevill et al., 2013).
2.2. Empirical Evidence
The phenomena of home advantage has been studied from a range of academic
disciplines, and over a number of years (see Courneya & Carron, 1992; Nevill &
Holder, 1999; Schwartz & Barsky, 1977), although there are still significant gaps in
understanding. Literature has historically focused on the reasons for the existence
of any home advantage. For example, a number of studies have considered concepts
such as the familiarity with playing surroundings (Pollard & Pollard, 2005), crowd
effects on players and match officials (Nevill et al., 1996, 2002) and also potential
JOURNAL OF GLOBAL SPORT MANAGEMENT 5
travel fatigue (Pollard, 1986; Pollard & Pollard, 2005). Many of these studies have
identified that crowds do influence referees’ decision making (Nevill et al., 1996,
Nevill & Holder, 1999, Nevill et al., 2002), although, until now, researchers have
not been able to substantiate these claims in stadia where crowds were absent.
Research on home advantage has also taken place across various sports and in
a number of different countries. For example, declines in home advantage have been
observed in basketball and ice hockey in the United States and association football
in England (Koyama & Reade, 2009; Pollard & Pollard, 2005), as well as home
advantage in the summer and winter Olympic Games held in various countries
(Balmer et al., 2001; 2003). Some explanations regarding declining home advantage
have been attributed to trends around travel and the familiarity of playing surround-
ings (Pollard & Pollard, 2005) and also the increasing coverage of English Premier
League (EPL) matches on television. This suggests that, due to this increased cov-
erage, players are likely to put in as much effort away from home as they do during
home fixtures, because supporters can follow every fixture irrespective of location
and stadia (Koyama & Reade, 2009). Ramchandani et al. (2021), meanwhile, con-
sidered the relationship between team ability on the home advantage of teams over
24 seasons from 1995/96 to 2018/19, including 48,864 matches across the four
professional divisions in England, with statistically significant home advantage found
in all four divisions and for teams of all abilities within each division.
Recently, research has focused on the impact of COVID-19 and any associated
impact from the absence of crowds. For example, Matos et al. (2021) focused on
the home advantage score in the last ten rounds in the 2019–2020 Portuguese season
with the first 24 rounds in same season, identifying that the absence of a crowd in
the last 10 rounds of the season, due to the COVID-19 pandemic, did not affect
home advantage. Other scholars have also considered the role of COVID-19 on
home advantage. Wunderlich et al. (2021) identified factors contributing to home
advantage, analysing over 40,000 matches before and during the pandemic and more
than 1,000 matches without spectators across the European football leagues. Findings
supported crowd-induced referee bias, but only a non-significant decrease in home
advantage was discovered, meaning that spectators did not appear to be the principal
factor of home advantage. Moreover, McCarrick et al. (2021) compared team per-
formance and referee decisions pre-COVID and during-COVID across 15 European
leagues, identifying that home advantage was significantly reduced during the COVID
impacted season. Furthermore, in games without fans, the home team created fewer
attacking opportunities and referee-bias was reduced with the number of fouls and
yellow cards for away teams reduced and there were no effects observed for red
cards (McCarrick et al., 2021).
Further evidence suggests that the absence of crowds has reduced home advantage
(e.g. Reade et al., 2021; Sors et al., 2021), with the support of the crowd identified
as a considerable cause of home advantage when measured from a variety of aspects
(points, goals, shots), although this advantage is almost halved when matches are
behind closed door (Scoppa, 2021). Thus, the COVID-19 pandemic appears to be
helping us to answer one of the enduring questions in sport, ‘what is the cause of
home advantage?’ Clearly, the absence of crowds seems to have influenced the out-
come of games, although to date we have been unable to ascertain whether or how
6 A. NEVILL ETAL.
crowds influence the referee in this scenario. Nevill et al. (2013) identified a sys-
tematic decline in home advantage in the professional English and Scottish leagues
post-WW2, with the steepest decline identified in lower divisions with smaller
crowds. Nevill et al. postulated that of the factors that are thought to influence
home advantage, that crowd support appeared the most plausible explanation, with
crowds thought to influence referees’ decisions and lead to favouritism towards the
team playing at home. However, Nevill et al. (2013) also focused on the role of
referees within this home advantage relationship, contending that the improved
training and development of referees since World War II has contributed to an
improved ability to make objective decisions and, therefore, a greater resilience to
crowd influence, therefore clarifying the decline in home advantage and the steeper
decline observed with smaller crowds.
The purpose of this research was to develop our understanding of the subject of
home advantage further through confirming the absence of home advantage in
association football during the COVID-19 2020-21 season, and to explain whether
large crowds might influence referees’ behaviour when awarding red and yellow cards.
3. Materials and Methods
3.1. Home Advantage Calculation
Home advantage was calculated for each team at the end of the season using the
end-of-season tables, methods described in detail by Clarke and Norman (1995)
and more recently by Nevill et al. (2013). Recognizing the need to separate the
teams’ ability from their home advantage, Clark and Norman proposed the following
mathematical model to describe the goal difference at the end of each game wij
(the home and away team effects being identified by the subscripts i and j
respectively),
wijuihiujeij,
where ui is the home team’s ability (uj being the away team’s ability), hi is the home
team’s advantage when playing at home and eij is the unexplained random error.
The proposed calculations to obtain the hi and ui for each team in the league using
the Premier League’s end-of-season table for the COVID-19 2020-21 season are
given in 4 steps below.
Step 1. Observed the number of teams in the league (N)
Step 2. Calculate H, the total of all the team’s HGD-column, divided by N - 1,
given by H = (sum HGDi)/(N - 1)
Step 3. Calculate for each team hi = (HGDi - AGDi - H)/(N - 2) (note that the
subscript i refers to an individual team).
Step 4. Calculate for each team ui = (HGDi - (N - 1) * hi)/N
Note that in the titles of columns in Table 1, we use the abbreviations; H = home;
A = away; W = win; D = draw; L = loss; f = goals for; a = goals against; GD = goal
JOURNAL OF GLOBAL SPORT MANAGEMENT 7
difference; hi = each individual team’s HA; ui = each individual team’s team rating
e.g. For Manchester City’s results in row 1 Table 1, where HGDi = 26 and AGDi = 25.
Step 1: We observe the number of teams in the league, N = 20
Step 2: We calculate H = (sum HGD)/(N - 1) = 4/19 = 0.21 (common to all
teams in the league table)
Step 3: We calculate hi = (HGDi - AGDi - H)/(N - 2) = (26 – 25 -
.21)/18 = 0.04 (where i = 1, Manchester City)
Step 4: We calculate ui = (HGDi - (N - 1) * hi)/N = (26 - (19) * .044)/20 = 1.26
(where i = 1, Manchester City)
We used the home advantage estimates (hi) from the four top English Leagues
during the most recent COVID-19 2020-21 season (Premier League details in Table
1) and compared the results with the top four English Leagues seasons from 1946-7
to 2019-20, data obtained from Statto.com (n.d.) and more recently Sportsmole.co.uk
(n.d.). In all, we calculated a total of 5644 home advantage estimates (hi) and team
ability estimates (ui) over the four leagues and 74 seasons since WW2.
3.2. Statistical Methods
One sample t-tests were used to assess the difference between home advantage using
the estimated hi values and the null hypothesis taken as zero from all four top
English leagues/divisions (Premier League, Championship, Division 1 and Division
2) for the 2020-21 season.
These results were also compared with the results obtained from the four top
English Leagues seasons from 1946-7 to 2019-20. Including the most recent COVID-19
season, a total of 5644 home advantage estimate (hi) and team ability estimates (ui)
over the four leagues and 74 seasons since WW2 were calculated.
Since all teams appear more than once over the range of seasons, the hi obser-
vations are unlikely to be independent (repeated measures). For this reason, multilevel
modelling was used to explore trends and differences in the hi data using seasons
(by decade) and the 4 divisions as independent variables. Multilevel modelling is
an extension of ordinary multiple regression and ANCOVA where data have a
hierarchical or clustered structure. The hierarchy consist of units or measurements
grouped at different levels. Multilevel analyses were performed using Statistical
Software MLwiN version 3.05, allowing the different teams to be the level 2
(between-team) variation and their repeated performances over the various seasons
to be the level 1 (within-team) variation.
To evaluate whether the presence (vs absence) of crowds can influence referees’
behaviour, we recorded the number of home and away red and yellow cards awarded
by referees in the four top English divisions during the ‘crowds absent’ COVID-19
season (2020-21) and compared them with the equivalent red and yellow cards
awarded in the 4 top divisions during the 10 previous ‘crowds present’ seasons
2010-11 to 2019–2020. These data were obtained from Statbunker.com (n.d.).
The chi-square tests of independence (see Bland, 2015) were used to compare
the home vs away red and yellow cards-by-seasons (the ‘no crowd’ COVID-19
8 A. NEVILL ETAL.
2020-21 season vs the 10 ‘crowd present’ seasons, entered either separately or com-
bined/summed). We also compared the proportion of red and yellow cards awarded
against the away players (as a proportion of the total number of red and yellow
cards) when referees were adjudicating with crowds (seasons 2010-11 to 2019-20),
and compared these with the proportion of red and yellow cards when referees were
adjudicating without crowds (COVID-19 2020-21 season), using the test of inde-
pendent proportions in Christensen (1996).
4. Results
The lack of home advantage associated with minimal crowds for the COVID-19
2020-21 season was confirmed when the hi values were assessed using a one-sample
t-test (Null hypothesis assumes home advantage (hi) = zero) for the mean hi from
all four divisions (Table 2).
The trend in the decline in home advantage (in goals per game, hi) since-WW2
can be seen in Figure 1. Observing the mean in the final COVID-19 2020-21 season
compared with the previous decades, there is strong evidence that home advantage
has dropped further (approximately 0.1 goals per game) when almost all of the
fixtures were played without crowds behind closed doors.
The multilevel regression analysis of hi is presented in Table 3. The multilevel
regression analysis of hi adopted the Premier League (2020-21) as its baseline or
intercept term, estimated to be hi = 0.0995 (SE = 0.056, p > 0.05). This confirms that
there was no significant home advantage in the Premier League during COVID-19
Table 2. Mean home advantage (hi), standard deviation (SD) and standard error of estimate (SEE)
plus the one-sample t-test results for the home advantage (hi) for the 4 English leagues for the
COVID-19 2020-21season.
League N Means h
i
SD SEE t (df) Sig. (2-tailed)
Premier 20 0.0100 0.534 0.120 0.084 (19) 0.934
Championship 24 0.196 0.511 0.104 1.88 (23) 0.073
Div 1 24 0.074 0.510 0.104 0.71 (23) 0.482
Div 2 24 0.161 0.423 0.086 1.87 (23) 0.075
Note that the raw Premier league h
i
data are reported in Table 1.
Figure 1. The decline in home advantage by decade since WW2 including the most recent
COVID-19 season.
JOURNAL OF GLOBAL SPORT MANAGEMENT 9
2020-21 season. Although the other 3 leagues were marginally higher, e.g. Championship
(2020-21) Δ hi = 0.0215 (0.0191), there was still no significant home advantage in
all 4 divisions during COVID-19 2020-21 season. Note that the home advantage of
the Championship (2020-21) can be calculated as hi = 0.0995 + 0.0215 = 0.121. This
absence of home advantage was also confirmed by our t-tests in Table 2. In complete
contrast, significant home advantage was identified in all season/decades prior to
2020-21 when crowds were present, e.g. seasons (1946-49) Δ hi = 0.5428 (SE =
0.0641, p < 0.001)), and more recently, in seasons (2010-19) Δ hi = 0.1789 (0.0566,
p < 0.001). Once again, we can calculate the home advantage for the seasons/decade
(1946-49) as hi = 0.0995 + 0.5428 = 0.6423, and for seasons/decade (2010-19) as hi =
0.0995 + 0.1789 = 0.2784. Note that home advantage was at its greatest soon after
WW2, after which there was a systematic decline by seasonal/decade until the most
recent season in 2020-21. This decline can be seen clearly in Figure 1.
The number of red and yellow cards awarded by referees during the ‘no crowds’
COVID-19 2020-21 season to both home and away teams for all four divisions,
together with the equivalent home and away red and yellow cards awarded in the
10 previous ‘crowds’ seasons are illustrated in Figure 2 and presented in Table 4
(red) and Table 5 (yellow) respectively. With crowds, the difference in home and
away yellow cards (Table 5) is clear and consistent (in all 10 seasons), in marked
contrast to little or no difference in home and away yellow cards in the COVID-19
2020-21 season (in the absence of crowds).
The chi-square test of independence values (comparing the COVID-19 2020-21
season with all 10 seasons separately and compared with the 10 season summed)
are reported at the foot of Tables 4 and 5. For yellow cards, the significant chi-square
test of independence results in Table 5 suggest the ratio of home vs. away yellow
cards is significantly different (fewer away card) with no crowds (season 2020-21)
compared with either the 10 crowd seasons combined (more away cards) (1 df) or
separately (with 10 df).
The proportion of red cards awarded against the away players by referees adjudi-
cating without crowds was 142/263 = 0.54 (see row 1 of Table 4). The proportion of
Table 3. The multilevel regression-analysis parameters for h
i
, using 4 English league divisions and
seasons by decade.
Fixed Explanatory Variables Estimate ±SE p
Premier League (2020-21) 0.0995 0.0560 >0.05
Championship (2020-21) 0.0215 0.0191 >0.05
Div1 (2020-21) 0.0120 0.0210 >0.05
Div2 (2020-21) 0.0225 0.0216 >0.05
Season (1946-49) 0.5428 0.0641 <0.001
Season (1950-59) 0.6198 0.0578 <0.001
Season (1960-69) 0.5803 0.0564 <0.001
Season (1970-79) 0.4893 0.0564 <0.001
Season (1980-89) 0.4216 0.0565 <0.001
Season (1990-99) 0.3196 0.0565 <0.001
Season (2000-09) 0.2616 0.0567 <0.001
Season (2010-19) 0.1780 0.0566 <0.001
Random variation
Level 2 (between teams) 0.0031 0.0010 <0.001
Level 1 (within teams) 0.2681 0.0045 <0.001
The baseline group were the h
i
data from Premier league 2020-21 season, from which all other leagues and seasons
were compared, indicated by ().
10 A. NEVILL ETAL.
red cards awarded against the away players by referees adjudicating with crowds was
1871/3160 = 0.592 (see Total crowds row at the bottom of Table 4). The difference in
these proportions is (0.52 − 0.592) = −0.052 or 5.2% assuming a normal approximation
z = −1.63 (p = 0.051). Note that the proportion of away red cards in the Premier League
during the COVID-19 2020-21 season 28/48 = 0.58, which was identical to the pro-
portion of away red cards awarded by Premier League referees in the previous 10
season (with crowd) p = 312/537 = 0.58 (see Table 4). Crowds appear to have little or
no effect on the top Premier League referees to penalize the away side (either with
or without crowds) when awarding red cards (χ
2
= 0.001; p = 0.975).
Note that the chi-square tests of independence reported at the foot of the Table
5 were more significant in lower divisions, with the least significant χ
2
effect being
in the Premier League (χ
2
= 5.552, p = 0.018) with greater significant χ
2
effects
observed in the Championship (χ
2
= 17.74, p < 0.001), Division 1 (χ
2
= 30.5, p < 0
.001) and Division 2 (χ
2
= 21.07, p < 0.001). This effect can be seen clearly in
Figure 3 when the number of yellow cards awarded to the home and away players
are plotted for the Premier League vs the Championship over the past 11 seasons.
The effect of crowds had considerably less influence on top Premier League referees
(with a significant narrowing of the gap between home and away yellow cards in
more recent seasons, chi-square test for linear trend χ
2
= 12.1; p = 0.001). These
were compared with the gap observed with Championship referees (chi-square test
for linear trend χ
2
= 0.52; p = 0.47), both assessed over the 10 ‘crowd’ seasons.
The proportion of yellow cards awarded against the away players by referees
adjudicating without crowds was 2984/6016 = 0.496 (see row 1 of Table 5). The
proportion of yellow cards awarded against the away players by referees adjudicating
with crowds was 34800/62951 = 0.553 (see Total crowds row Table 5). This difference
in proportions is −0.057 or 5.7% assuming a normal approximation is z = −8.42
(p < 0.001). The number of yellow cards awarded to away players without crowds
Figure 2. The number of red and yellow cards (H and A) awarded by referees during the ‘no
crowds’ COVID-19 2020-21 season and in the 10 previous ‘crowds’ seasons.
JOURNAL OF GLOBAL SPORT MANAGEMENT 11
Table 4. The number of red cards (H and A) awarded by referees during the ‘no crowds’ COVID-19
2020-21 season and in the 10 previous ‘crowds’ seasons.
Number of red cards
Divisions Premier Championship Division 1 Division 2 Total
Seasons H A H A H A H A H A
2020-21 (no crowds) 20 28 34 37 32 31 35 46 121 142
2019-20 (crowds) 22 23 27 38 29 37 31 43 109 141
2018-19 (crowds) 18 29 21 48 30 50 33 57 102 184
2017-18 (crowds) 17 22 30 52 40 43 51 48 138 165
2016-17 (crowds) 21 20 30 58 36 63 43 46 130 187
2015-16 (crowds) 25 34 40 38 25 47 37 49 127 168
2014-15 (crowds) 26 45 43 45 36 55 40 61 145 206
2013-14 (crowds) 16 37 26 70 36 59 51 59 129 225
2012-13 (crowds) 26 26 32 37 27 50 38 59 123 172
2011-12 (crowds) 25 40 38 49 42 52 38 60 143 201
2010-11 (crowds) 29 36 37 62 40 62 37 62 143 222
Total (crowds) 225 312 324 497 341 518 399 544 1289 1871
χ² p-value χ² p-value χ² p-value χ² p-value
Chi-square (df = 10) 8.383 0.59 21.82 0.02 9.13 0.52 8.89 0.54
Chi-square (df = 1) 0.001 0.98 1.93 0.17 3 0.08 0.03 0.88
during the COVID-19 2020-21 season (p = 2984/(3032 + 2984)=0.496) is the same
as those awarded to home players (p = 3032/(3032 + 2984)=0.504 see Figure 2),
suggesting that referees penalize home and away players equitablyCrowds appear
to be able to influence referees’ behaviour to favour the home team, although the
Premier League referees appear to be less vulnerable to such influences.
5. Discussion
5.1. The Decline in Home Advantage and Crowds’ Inuence on Referees’
Behaviour
This paper has focused on the sustained decline of home advantage in the four top
English leagues (including the old English First Division League), seasons from 1946-7
Table 5. The number of yellow cards (H and A) awarded by referees during the “no crowds”
COVID-19 2020-21 season and in the 10 previous “crowds” seasons.
Number of yellow cards
Divisions Premier Championship Division 1 Division 2 Total
Seasons H A H A H A H A H A
2020-21 (no crowds) 548 559 791 787 883 843 810 795 3032 2984
2019-20 (crowds) 612 687 820 1006 644 763 661 802 2737 3258
2018-19 (crowds) 588 651 858 1025 808 1059 761 967 3015 3702
2017-18 (crowds) 573 606 885 1068 739 912 768 955 2965 3541
2016-17 (crowds) 677 732 855 1087 826 1032 850 1017 3208 3868
2015-16 (crowds) 551 655 791 985 762 943 736 864 2840 3447
2014-15 (crowds) 626 770 831 1015 749 931 652 915 2858 3631
2013-14 (crowds) 548 682 753 1000 725 910 699 844 2725 3436
2012-13 (crowds) 511 660 727 909 702 858 657 853 2597 3280
2011-12 (crowds) 523 654 695 879 688 866 715 856 2621 3255
2010-11 (crowds) 541 702 729 885 645 923 670 872 2585 3382
Total (crowds) 5750 6799 7944 9859 7288 9197 7169 8945 28151 34800
χ² p-value χ² p-value χ² p-value χ² p-value
Chi-square (df = 10) 21.38 0.019 21.58 0.017 39.44 <0.001 31.38 0.001
Chi-square (df = 1) 5.552 0.018 17.74 <0.001 30.5 <0.001 21.07 <0.001
12 A. NEVILL ETAL.
to 2020-21 and whether crowds are more likely to influence referees or players. Findings
suggest that not only has home advantage continued to decrease up to and including
the 2020-21 season, there was no significant home advantage in the recent COVID-19
2020-21 season (see Table 2) when the majority of games were played behind closed
doors, without crowds. The absence of crowds is clearly associated with little or no
home advantage as previously anticipated (Nevill et al., 1999; 2002), with the analysis
of referee’s behaviour in awarding home and away red and yellow cards in front of,
and in the absence of crowds, contributing to our understanding of the reduction of
home advantage without crowds. This effect can be seen clearly in Figure 2. The fact
that we identified no significant home advantage in any of the four English leagues
during the COVID-19 2020-21 season, that the Premier League demonstrated negative
home advantage in the same season, and that the chi-squared tests of independence
and test for trend showed all but the very best referees operating in the Premier League
awarded significantly more red and yellow cards to away players when performing in
front of crowds (see Figure3), suggests that the training, development and preparation
of these Premier League referees contributes to their enhanced decision making. Across
the four divisions, referees awarded 5.7% more yellow (p < 0.001) and 5.2% more red
cards (p = 0.051) against away players when officiating in front of crowds (seasons
2010-11 to 2019-20), although the Premier League referees showed less bias, awarding
fewer yellow cards (3.7%, p = 0.01) and fewer, indeed no difference in red cards (0.0%,
p = 0.98) in front of crowds. As such top Premier League referees were considerably
less likely to be influenced when officiating in front of crowds (see Tables 4 and 5).
Our findings suggest that the better referees are more likely to avoid any home
advantage favouritism. As the data suggests, the Premier League referees have given
less advantage to the home team through red and yellow cards, and in the case of red
cards, the difference between matches with crowds and without crowds was minimal.
Therefore, it is evident that Premier League referees have been able to resist much of
the influence of crowds, especially in more recent years (χ
2
test for linear trend = 12.1:
p = 0.001), see Figure 2. They are less likely to be intimidated and it is less likely that
crowd behaviour will impact upon their decision making. Conversely, in the
Figure 3. The number of yellow cards (H and A) awarded by the Premier League vs the
Championship over the past 11 seasons.
JOURNAL OF GLOBAL SPORT MANAGEMENT 13
Championship and lower professional leagues, where accomplished referees still operate
although not the very best, our results demonstrate that referees are more likely to be
susceptible to the influence of the crowds (see Figure 3). Moreover, Dawson et al.
(2020) discuss the notion of video technology and the introduction of such advance-
ments into rugby union in order to remove subjectivity from the decision making of
the on-field referee. VAR was introduced into the Premier League in 2019, but our
findings suggest that the home advantage gap reduced long before this intervention
(approx. 2016-17) and, as such, VAR cannot be the catalyst for this further reduction
in home advantage (see Figure 3). However, as discussed in the theoretical contribution
section, unconscious bias may well be initiated by the crowd providing an audio cue
to reinforce referees’ decisions to penalize the away side (Nevill et al., 2002).
The training of elite referees, predominantly those operating in the Premier League
in England, and the development and evolution of this training has been well doc-
umented (Webb, 2014, 2017; Webb etal., 2016; Webb & Thelwell, 2015). For example,
in the Premier League psychologists have been introduced to work with the referees
(Nevill et al., 2013), training is constantly evaluated in order to improve the pro-
vision to the referees (Webb, 2017) and technological innovations such as goal line
technology and video assistant referees (VAR) have been introduced to assist referees
in their performance (Webb, 2022). Some of these advancements have considerably
assisted the elite Premier League referees. For example, the introduction of a full-time
psychologist to support the referees, it could be argued, has had a direct influence
on home advantage. The psychologist works on the decision making of referees and
how to make decisions under pressure, including pressure from the crowd in the
stadium. This psychological provision is provided on a full-time basis for the Premier
League referees that operate in Select Group 1, the same full-time support is not
available for the referees that officiate in the Championship, based in Select Group
2. This could help to explain why the Premier League referees have not been influ-
enced as much as referees at other professional levels, in terms of favouring the
home team. They receive greater and sustained psychological support, whilst those
referees in the lower leagues do not receive the same full-time psychological support.
Furthermore, the growth of the Premier League has led to increased investment
in the professional referees and the potential for a wider gap to emerge between
these referees and those referees operating within the professional game in the lower
leagues (Webb, 2022). This has led to investment in the Professional Game Match
Officials Limited (PGMOL—the organisation that manages and trains the referees
in the Premier League and Championship predominantly) that included £10.2 m
from the Premier League, £5.3 m from the Football League and £3.7 m from the
FA, according to the 2018/2019 season annual accounts (PGMOL., 2019), a signif-
icant investment in professional refereeing in England.
The Premier League referees are also used to officiate high profile fixtures, whereas
those referees at lower levels are not used to the same exposure at fixtures with
such profile. Therefore, this means that it is difficult to train and prepare for high
profile fixtures for these referees at lower levels. Matches at lower levels are not as
well attended, stadiums are smaller and therefore crowd size is reduced, media
exposure is also not as big (Webb, 2018). This means that referees at this level, not
only do not have the same psychological support, but are not used to repeatedly
14 A. NEVILL ETAL.
performing in front of bigger crowds and blocking these crowds out during the
decision-making process. In short, it is difficult to train, replicate and prepare for
high profile matches without larger crowds at lower levels.
As with all studies, we must acknowledge some possible limitations. It is possible
that the crowd can affect the home/away players’ on-field performance, as well as
the referee’s decisions simultaneously. This might also lead to the observed differ-
ences in red and yellow cards. For example, crowds might influence away players
to become more aggressive, justifying the referees to be more likely to penalize the
away player compared with home players. Clearly, this is a topic for future research.
5.2. Managerial Implications and Recommendations
The findings that have been uncovered as part of this research provide a number of vital
training points and implications for referee managers, referee administrators, governing
bodies, and leagues. The findings demonstrate how advanced and effective the training
provision is for referees that operate in the Premier League. A sentiment echoed in
previous research (Nevill et al., 2013; Webb, 2017). However, clearly there are training
implications for referees at the levels below the Premier League, and this also extends
to the wider referee talent identification and talent development pathway (Webb et al.,
2021). First, training on the subject of home advantage specifically and decision making
should be introduced for referees who are part of the talent development pathway and
who operate in the professional game. The introduction of this type of training could
lead to further reductions in home advantage within the professional leagues in England,
and in other countries around the world as knowledge is shared and disseminated.
Second, referees at lower levels and in the talent development pathway should
be trained to shut out the crowd, or at least minimise the impact of the crowd on
their performance (Webb et al., 2021) and to deal with hostile crowds when offi-
ciating. The concept of crowds affecting performance has been considered previously,
but the focus has been on player performance, rather than that of referees (Boyko
etal., 2007; Nevill et al., 2002; Unkelbach & Memmert, 2010). If referees can achieve
this, evidence demonstrates that it could help to improve their performance away
from home, lead to reduced home advantage in the Championship, League 1 and
League 2 and therefore improve the authenticity of the leagues in England, as well
as the quality of referees being provided to the Premier League.
Third, referees at these lower levels should be part of a wider and more structured
mentoring programme. This would involve greater engagement with more experienced
officials, teams of officials and placement at matches in higher leagues as part of
the development process. Referees operating in League 2 or League 1 could be placed
in the Championship or the Premier League as part of the match-day team of referees
in order to observe the preparation, performance and techniques utilised to deal
with decision making and the noise of the crowd. This would mean that when ref-
erees were promoted to the Championship and the Premier League, the environment
would not be alien to them. The benefits of such mentoring programmes are well
noted within pertinent literature (e.g. Ridinger et al., 2017; Slack et al., 2013).
Fourth, in order to achieve the continuous improvement in training and conse-
quently performance related to home advantage, further financial investment is
JOURNAL OF GLOBAL SPORT MANAGEMENT 15
required in refereeing. Since the formation of the Premier League in 1992 financial
investment in refereeing has increased, particularly after the professionalisation of
refereeing in 2001 (Webb, 2017). This investment should continue and be targeted
to ensure that referees continue to develop their decision-making skills. Further
psychological support could be provided for referees operating in League 1 and
League 2. Currently full-time psychologists work with Select Group 1 and the referees
that operate in the Premier League, but to better prepare referees for officiating in
the Premier League, psychologists could also be employed full time at lower levels.
Moreover, further research into the reduction or removal of subjectivity in referee
decision making (see Raab etal., 2021), how this effects the game and the associated
emotional intensity (Dohmen, 2008), particularly following the COVID-19 pandemic,
would be beneficial to further enhance understanding of the role of the referee.
Fifth, clearly home advantage has decreased further, demonstrated by our findings
and those of other recent publications (McCarrick et al., 2021; Nevill et al., 2013;
Scoppa, 2021; Sors etal., 2021). This means that it is likely that referee improvement
has continued, and their decision making has been further enhanced, enabling home
advantage to decrease further (Webb et al., 2016). This improvement in home
advantage should be monitored on a season-by-season basis, and any training should
be delivered and adjusted in order to maintain any gains and improvements, as well
as identify and address any increases in home advantage that might become apparent.
6. Conclusion
This paper has presented novel research which builds on our understanding of home
advantage in sport as well as concepts related to the nuances between the perfor-
mances of referees within the professional game in England when in front of crowds
compared to when officiating without crowds. Little or no home advantage exists
in the absence of crowds. However, over the four divisions, referees award 5.7%
more yellow (p < 0.001) and 5.2% more red cards (p = 0.051) against away players
in front of crowds (seasons 2010-11 to 2019-20), compared to when crowds are
absent (season 2020-21). Therefore, it would appear that home crowds are able to
influence all but the very best referees’ behaviour to favour their home team, an
effect that disappears when supporters are absent (season 2020-21), see Figure 2.
These findings have the potential to influence the training and development of
referees at many levels of the game and to provide referee educators with the infor-
mation to focus future training initiatives aimed at reducing home advantage further
in all professional leagues in England.
Disclosure Statement
No potential conict of interest was reported by the authors.
Notes on contributors
Alan Nevill is an Emeritus Professor in the Faculty of Education Health and Wellbeing,
Wolverhampton University (specialization in biostatistics applied to health, sport and exercise
16 A. NEVILL ETAL.
sciences). Alan’s most recent research specializes in home advantage, multilevel and allometric
modelling of large data sets, analysing human health and performance associated with body
size. He is a past Editor-in-Chief of the Journal of Sports Sciences.
Alastair Pearson MSc is a Professional Doctorate student in the School of Sport, Health and
Exercise Science at the University of Portsmouth, UK. Alastair is part of the research team cur-
rently working in collaboration with World Netball on a multi-disciplinary project exploring
development pathways and support structures available to netball match ocials around the
world. Alastair also works as Performance Analyst for Saints Sport at the University of St Andrews.
Dr To m We bb PhD is a Senior Lecturer and researcher in sport management at the University
of Portsmouth, UK. Tom’s research focuses on sports ocials from multi-disciplinary perspectives
including abuse towards sports ocials, mental health and wellbeing, recruitment and retention
of sports ocials around the world and the barriers that women sports ocials can face.
ORCID
Alan Nevill http://orcid.org/0000-0003-0506-3652
Alastair Pearson
http://orcid.org/0000-0002-7725-3976
Tom Webb
http://orcid.org/0000-0003-1216-5307
References
Albanese, A., Baert, S., & Verstraeten, O. (2020). Twelve eyes see more than eight. Referee
bias and the introduction of additional assistant referees in soccer. Plos One, 15(2),
e0227758. https://doi.org/10.1371/journal.pone.0227758.
Balmer, N. J., Nevill, A. M., & Williams, A. M. (2001). Home advantage in the Winter
Olympics (1908-1998). Journal of Sports Sciences, 19(2), 129–139. https://doi.
org/10.1080/026404101300036334.
Balmer, N. J., Nevill, A. M., & Williams, A. M. (2003). Modelling home advantage in the
Summer Olympic Games. Journal of Sports Sciences, 21(6), 469–478. https://doi.org/10.10
80/0264041031000101890.
Bland, M. (2015). An introduction to medical statistics. Oxford University Press.
Boyko, R. H., Boyko, A. R., & Boyko, M. G. (2007). Referee bias contributes to home ad-
vantage in English Premiership football. Journal of Sports Sciences, 25(11), 1185–1194.
https://doi.org/10.1080/02640410601038576.
Christensen, R. (1996). Analysis of variance, design, and regression: Applied statistical methods.
CRC Press.
Clarke, S. R., & Norman, J. M. (1995). Home ground advantage of individual clubs in English
soccer. e Statistician, 44(4), 509–521. https://doi.org/10.2307/2348899
Courneya, K. S., & Carron, A. V. (1992). e home advantage in sport competitions: A
literature review. Journal of Sport and Exercise Psychology, 14(1), 13–27. https://doi.
org/10.1123/jsep.14.1.13
Dawson, P., Massey, P., & Downward, P. (2020). Television match ocials, referees, and home
advantage: Evidence from the European Rugby Cup. Sport Management Review, 23(3), 443–454.
Dohmen, T. J. (2008). e inuence of social forces: Evidence from the behavior of football
referees. Economic Inquiry, 46(3), 411–424. https://doi.org/10.1111/j.1465-7295.2007.00112.x
Erikstad, M. K., & Johansen, B. T. (2020). Referee bias in professional football: Favoritism
toward successful teams in potential penalty situations. Frontiers in Sports and Active Living,
2, 19. https://doi.org/10.3389/fspor.2020.00019.
Grabowski, M., & Sanborn, S. D. (2003). Human performance and embedded intelligent
technology in safety-critical systems. International Journal of Human-Computer Studies,
58(6), 637–670. https://doi.org/10.1016/S1071-5819(03)00036-3
JOURNAL OF GLOBAL SPORT MANAGEMENT 17
Hancock, D. J., Bennett, S., Roaten, H., Chapman, K., & Stanley, C. (2021). An analysis of
literature on sport ociating research. Research Quarterly for Exercise and Sport, 92(4),
607–617. https://doi.org/10.1080/02701367.2020.1756198
Koyama, M., & Reade, J. J. (2009). Playing like the home team: An economic investigation
into home advantage in football. International Journal of Sport Finance, 4(1), 16–41.
Matos, R., Monteiro, D., Antunes, R., Mendes, D., Botas, J., Clemente, J., & Amaro, N. (2021).
Home-advantage during COVID-19: An analysis in Portuguese football league. International Journal
of Environmental Research and Public Health, 18(7), 3761. https://doi.org/10.3390/ijerph18073761
McCarrick, D., Bilalic, M., Neave, N., & Wolfson, S. (2021). Home advantage during the
COVID-19 pandemic: Analyses of European football leagues. Psychology of Sport and
Exercise, 56, 102013. https://doi.org/10.1016/j.psychsport.2021.102013.
Nevill, A. M., Balmer, N. J., & Williams, A. M. (2002). e inuence of crowd noise and
experience upon refereeing decisions in football. Psychology of Sport and Exercise, 3(4),
261–272. https://doi.org/10.1016/S1469-0292(01)00033-4
Nevill, A., Balmer, N., & Williams, M. (1996). Crowd inuence on decisions in association
football. e Lancet, 353(9162), 1416. https://doi.org/10.1016/S0140-6736(99)01299-4
Nevill, A. M., & Holder, R. L. (1999). Home advantage in sport: An overview of studies on
the advantage of playing at home. Sports Medicine (Auckland, N.Z.), 28(4), 221–236. https://
doi.org/10.2165/00007256-199928040-00001.
Nevill, A. M., Webb, T., & Watts, A. S. (2013). Improved training of football referees and
the decline in home advantage post WW2. Psychology of Sport and Exercise, 14(2), 220–227.
https://doi.org/10.1123/jsep.32.4.483
O’Brien, K. A., & Mangan, J. (2021). e issue of unconscious bias in referee decisions in
the National Rugby League. Frontiers in Sports and Active Living, 3, 739570. https://doi.
org/10.3389/fspor.2021.739570.
PGMOL. (2019). 2018–2019 end of year nancial statements. Companies House.
Phillips-Wren, G., Jeerson, T., & McKni, S. (2019). Cognitive bias and decision aid use
under stressful conditions. Journal of Decision Systems, 28(2), 162–184. https://doi.org/10.
1080/12460125.2019.1643695
Plessner, H., & Haar, T. (2006). Sports performance judgments from a social cognitive per-
spective. Psychology of Sport and Exercise, 7(6), 555–575. https://doi.org/10.1016/j.
psychsport.2006.03.007
Pollard, R. (1986). Home advantage in soccer: A retrospective analysis. Journal of Sports
Sciences, 4(3), 237–248. https://doi.org/10.1080/02640418608732122.
Pollard, R., & Pollard, G. (2005). Long terms trends in home advantage in professional team
sports in North America and England. Journal of Sports Sciences, 23(4), 337–350. https://
doi.org/10.1080/02640410400021559.
Raab, M., Avugos, S., Bar-Eli, M., & MacMahon, C. (2021). e referee’s challenge: A thresh-
old process model for decision making in sport games. International Review of Sport and
Exercise Psychology, 14(1), 208–228. https://doi.org/10.1080/1750984X.2020.1783696
Ramchandani, G., Millar, R., & Wilson, D. (2021). e relationship between team ability and
home advantage in the English football league system. German Journal of Exercise and
Sport Research, 51(3), 354–361. https://doi.org/10.1007/s12662-021-00721-x
Reade, J. J., Schreyer, D., & Singleton, C. (2021). Eliminating supportive crowds reduces
referee bias. Economic Inquiry, 60(3), 1416–1436. https://doi.org/10.2139/ssrn.3743972
Ridinger, L. L., Kim, K. R., Warner, S., & Tingle, J. K. (2017). Development of the referee
retention scale. Journal of Sport Management, 31(5), 514–527. https://doi.org/10.1123/
jsm.2017-0065
Russell, S., Renshaw, I., & Davids, K. (2022). Sport arbitration as an emergent process in a
complex system: Decision-making variability is a marker of expertise in national-level
football referees. Journal of Applied Sport Psychology, 34(3), 539–563. https://doi.org/10.1
080/10413200.2020.1831651
Samuel, D., Tenenbaum, G., & Galily, Y. (2021). An integrated conceptual framework of
decision-making in soccer refereeing. International Journal of Sport and Exercise Psychology,
19(5), 738–760. https://doi.org/10.1080/1612197X.2020.1766539
18 A. NEVILL ETAL.
Schwartz, B., & Barsky, S. F. (1977). e home advantage. Social Forces, 55(3), 641–661.
https://doi.org/10.1093/sf/55.3.641
Scoppa, V. (2021). Social pressure in the stadiums: Do agents change behavior without crowd
support? Journal of Economic Psychology, 82, 102344. https://doi.org/10.1016/j.joep.2020.102344
Slack, L. A., Maynard, I. W., Butt, J., & Olusoga, P. (2013). Factors underpinning football
ociating excellence: Perceptions of English Premier League referees. Journal of Applied
Sport Psychology, 25(3), 298–315. https://doi.org/10.1080/10413200.2012.726935
Sors, F., Grassi, M., Agostini, T., & Murgia, M. (2021). e sound of silence in association
football: Home advantage and referee bias decrease in matches played without spectators.
European Journal of Sport Science, 21(12), 1597–1605. https://doi.org/10.1080/17461391.20
20.1845814
SportsMole. (n.d.). Football news, previews, predictions, transfers. https://www.sportsmole.co.uk/
football/premier-league/2018-19/table.html.
Statbunker. (n.d.). Football stats, team stats, player stats, soccer stats. https://www.statbunker.
com.
Statto. (n.d.). Compare stats, teams and odds. http://www.statto.com/football/stats/england/
premier-league/2011-2012/true-table/full.
Unkelbach, C., & Memmert, D. (2010). Crowd noise as a cue in referee decisions contributes
to the home advantage. Journal of Sport & Exercise Psychology, 32(4), 483–498. https://
doi.org/10.1123/jsep.32.4.483.
Webb, T. (2014). e emergence of training and assessment for referees in Association
Football: Moving from the side-lines. e International Journal of the History of Sport,
31(9), 1081–1097. https://doi.org/10.1080/09523367.2014.905545
Webb, T. (2017). Elite soccer referees: Ociating in the Premier League, La Liga and Serie A.
Taylor & Francis.
Webb, T. (2018). Referees and the media: A dicult relationship but an unavoidable neces-
sity. Soccer & Society, 19(2), 205–221. https://doi.org/10.1080/14660970.2015.1133414
Webb, T. (2022). e evolution of referees in the English Premier League. Soccer & Society,
23(4–5), 400–410.
Webb, T., Dicks, M., elwell, R., & Nevill, A. (2016). e impact of referee training:
Reections on the reduction of home advantage in Association Football. Soccer & Society,
19(7), 1–14. https://doi.org/10.1080/14660970.2016.1267626
Webb, T., O’Gorman, J., & Markham, L. (2021). Striving for excellence: Talent identication
and development in English football refereeing. European Sport Management Quarterly,
1–19. https://doi.org/10.1080/16184742.2020.1864748
Webb, T., & elwell, R. C. (2015). “He’s taken a Dive”: Cultural comparisons of elite refer-
ee responses to reduced player behaviour in Association Football. Sport, Business and
Management: An International Journal, 5(3), 242–258. https://doi.org/10.1108/SBM-04-
2014-0019
Wilke, A., & Mata, R. (2012). Cognitive bias. In V. S. Ramachandran (Ed.), Encyclopedia of
human behavior (pp. 531–535). Academic Press.
Wunderlich, F., Weigelt, M., Rein, R., & Memmert, D. (2021). How does spectator presence
aect football? Home advantage remains in European top-class football matches played
without spectators during the COVID-19 pandemic. PloS One, 16(3), e0248590. https://
doi.org/10.1371/journal.pone.0248590
