This literature review summarizes previous research on referee favoritism in soccer leagues. Several studies found evidence that referees display home team favoritism by awarding more penalties and yellow/red cards to away teams (Boyko et al., 2007; Buraimo et al., 2010). Referees also tended to add more injury time when the home team was losing by one goal compared to winning or drawing (Sutter and Kocher, 2004; Garciano et al., 2005; Scoppa, 2008). However, two more recent studies found only marginal evidence of these biases in the Premier League and Bundesliga (Neil and Witt, 2008; Riedl et al., 2015). The literature suggests crowd effects can

1. 1
An investigation of referee favoritism when allocating added time in
English Premier League 2013 to 2015
Name: Rory O’Riordan
Student Number: 113421072
Date: 03-05-2016
Module: EC3144
Supervisor: Dr. Robert Butler
Research Question: Do referees behave favorably towards certain
principals in a football match in the English Premier League?

2. 2
(I) Table of Contents Page
List of Figures 3
List of Tables 3
Abstract 4
Chapter 1: Introduction 5
Chapter 2: Literature Review 8
Chapter 3: Data Collection 14
Chapter 4: Methodology 16
4.1 Home Favouritism 17
4.2 Big Club Favouritism 19
Chapter 5: Results 21
Chapter 6: Discussion & Conclusions 30
References 34

3. 3
(II) List of Figures
2.1-Extra timy by score margin (German Bundesliga 01/02)
(III) List of Tables
3.1- EPL 2013-2015 Descriptive Statistics
5.1 The Determinants of Additional Time in the EPL 2013-2015
5.2 The Determinants of Additional Time – Club Size 2013-2015
5.3 Determinants of Additional Time - Close Matches 2013-2015
5.4 Determinants of Additional Time - Close Matches & Club Size 2013-2015

4. 4
(IV) ABSTRACT
This paper questions and examines the impartiality the decision making of referees regarding
FIFA’s Law 7- The Duration of the Match. This research includes all 760 games played in the
English Premier League over the course of two season; 2013/2014 and 2014/2015. We
investigate to see if home favouritism or a ‘big’ team bias exists when referees allocate
additional time at the end of a game. We found weak evidence that suggests referees display
favourable behaviour towards the home teams but we can confirm that there is a significant
bias towards ‘big’ clubs, suggesting that Fergie Time truly exits in the EPL. We furthered our
research by investigating close games (goal margin ≤1 at 90 minutes) and found no evidence
suggesting Fergie Time was present in these games. The results from this paper suggest that
while the concept of Fergie Time exists, its ability to change a match outcome is low.

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1.INTRODUCTION
This dissertation will investigate referee decision making when allocating added
time/injury time at the end of games in the English Premier League (EPL). This paper
particularly focuses on whether EPL officials display a recurring bias in favour of the home
team and/or in favour of the ‘big’ clubs, defined by their financial and footballing performance.
It investigates the existence of this favouritism over the course of 760 EPL matches form
August 2013 until May 2015. There have been a number of empirical studies carried out
examining the existence of referee bias in top leagues around the world (Boyko, et al., 2007,
Buraimo, et al., 2010, Garciano, et al., 2005, Scoppa, 2008, Sutter & Kocher, 2004, Pollard,
2008, Pollard, 2006, Pollard & Pollard, 1876-2003). This paper focuses on referee behaviour
strictly in the EPL. As well as focusing if referees displayed home favouritism, this paper will
investigate whether or not Fergie Time actually exists in the EPL.
The referees officiating matches in any league do not have total control over how much
added time is to be allocated. The Féderation Internationale de Football Association (FIFA),
the head authority in football, give guidelines to referees on how to calculate and, therefore,
grant the correct amount of time to be added at the end of each half. FIFA’s Law 7-The
Duration of the Match is dedicated to give direction to match officials on how to award the
appropriate amount of time. The Law states that:
“An allowance is made in either period for the time lost through: substitutions,
assessment of players injuries, the removal of injured players form the field of play for
treatment, time-wasting, when the play is to stop for different reasons (e.g. critical
weather conditions, goalpost broken, floodlight failure. Many stoppages are natural
(e.g. throw-ins, goal kicks). An allowance is to be made only when these delays are

6. 6
excessive. The referee shall not compensate for a timekeeping error during the first half
by increasing or reducing the length of the second half.
The announcement of the additional time does not indicate the exact amount of time
left in the match. Time may be increased if the referee considers it appropriate (i.e. if
there is time wasting during injury time) but never reduced” (FIFA, 2014, p.29).
The first line stated by FIFA on Law 7 states “The referee decides on the time lost in
each period” (FIFA, 2014, p.29). This clarifies that he allocates the amount of time his
discretion, not that of the linesmen, fourth officials or any other body officiating the game.
The media and previous research provide the reasoning for carrying out this
investigation on EPL referee behaviour. Refereeing decision making comes under constant
scrutiny by players, managers, pundits, journalists and basically, anyone with an interest in
football on a regular basis. They are often accused of giving decisions to the ‘big’ teams. Many
managers of the so-called lesser teams feel that the decisions seem to go against them too
regularly. This is where the coinage Fergie Time comes into context. Fergie Time is used to
describe the favouritism referees display towards ‘big’ teams when allocating added time. The
phrase is reference to former Manchester United Manager Sir Alex Ferguson who often
pressured and arguably intimidated match officials for greater amounts of added time. The
perception was that if his United teams weren’t winning, there would be enough time added on
to ensure they score a late decisive goal. This is a real life example of the principal-agent
problem, where the principal is the football team and the agent is the match official. Referee’s
display favourable behaviour towards one principal in a football match when there are certain
incentives in question.

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There has been an abundance of research conducted the investigation of favouritism is
sport. It has repeatedly been discovered that favouritism in sport does truly exist but the
complexity of the situation still baffles researches. Pollard (1986) discovered favouritism has
been part of professional sport in England and North America since the 18th century. Pollard
(2005) found that the magnitude of favouritism in association football was stronger in the
English Football League’s early years. But the reasons for the existence of favouritism in sport
is still an enigma to researchers in this area.
There has been a vast amount of research carried out investigating favouritism, but the
majority of the research has investigated home favouritism. There has been little research
investigating the presence of a bias towards the big teams. This paper classifies the status of
different principals by the teams financial and footballing performance which helps us identify
if agents display favourable behaviour towards certain principals. These officials are under
constant pressure and they are lambasted after every game. They are more often criticised for
their decisions rather than praised. These social pressures may play a part in the referee’s
decisions. This helps us get a better understanding to what effect a club’s reputation has on the
agent’s decision making, thus questioning the impartiality of referees in the EPL.

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2.LITERATURE REVIEW
There have been studies carried out examining team advantages in the top leagues in
Europe: Serie A (Italy), Spanish Primera Liga, German Bundesliga and the English Premier
League. These leagues are comprised of the teams that annually contest for Europe, footballs’s
most prestigious club competition the Uefa Champions League. The teams involved are
identified as the strongest teams in their domestic competitions. They generally have a larger
financial backing and larger fan suppor. Recent literature has looked at home advantade in
terms of disciniplary decisions (Boyko, et al., 2007) (Buraimo, et al., 2010). There is literature
that focuses on officials being biased in their allocation of injury time (Sutter and Kocher,
2004) (Garciano, et al., 2005) (Scoppa, 2008) (Rickman & Witt, 2008) (Riedl, et al., 2015).
Boyko et al (2007) examined 5244 English Premier League games over the seaons from
92/92 to 05/06 to test whether referees were swayed by crowd effects. They retrieved teams
involved, referee, score, attendence, yellow and red cards and penalty kicks converted. The
effect the crowd has on the referee is a common theme throughout these studies. They found
that referees were significanly affected by both the number of people in attendance and crowd
density as they peanalised the away team woth more yellow cards than the home team and
awarded the away teams more penalties. For every 10,000 person increase they found home
advanatge increased by approximately .086 goals. During this period they found a negative
relationship between refereee experience and home advanatge. With 50 referees involved
during this time, the refereees with greate expereince showcased less home advantage.
Buraimo et al. (2010) examined matches in the Bundesliga and English top flight from
2000 to 2006. They conducted a minute by minute bivariate probit analysis of bookes and
dismissals to detemine the probability of a caution at different times in a match. They also

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found that away teams are awarded with more bookings which is indicative of home team
favouritisim as a reuslt of crowd pressure. During derby matches (mathes between teams in the
same area) they discovered that there was an increased probablility of cautions. They also found
that referees show a home team bias caused by crowd pressure:
“That the net effect of a running track is to increase cards issued to home players
suggests that the result is being driven by the referee's response to the proximity of the
crowd and this is consistent with referees typically being biased towards the home team
because of the presence of partisan spectators.”
They found Similar to Boyko et al.’s study, away teams received more yellow and red
cards than home teams. They provided rationale for these findings. They considered that the
away team are more often on the back foot defending and as a result, they are involved in more
tackles and that if the goal margin is larger , the number of bookings declines as intensity
evidently drops.
These two studies show how referees can be influenced by the crowd nois when making
decisions on sanctioning the players. The crowd noise and size is out of the control of the
referee and it has showed evidence to contribute to home advantage. An experiment was
undertaken where referees watched recorded natches without the sound on. Ther results showed
that referees called less fouls for the away team when crowd noise was on compared to when
it was just the video. (Nevill, Balmer, & Williams, 1999, 2002).
Sutter and Kocher (2004) analysed the Bundesliga during the 01/02 season. They
investigated the hypotheses related to injury time allocation: 1. Extra time in the second half
depends on the margin, 2. Extra time will be longer if the home team are trailing by 1 goal than

10. 10
if it’s a draw or they are ahead by a goal and 3. Refereees add more time as the number of
spectators increase. They found evidence that supports all these hypotheses. This presents
referees expressing home team favoritism:
Fig. 2.1-Extra timy by score margin (German Bundesliga 01/02)
Source: Sutter and Kocher (2004)
They found that when the score margin is a single goal more time is played but when
the final outcome of the game was clear, less time is allocated. The crowd size and denisty
also contributed to referees being home team biased as more penalties were awarded to home
teams than away teams. An intereising discovery was that there was only 4 occasions when
goals scored in injury time altered the outcome of the match. The home team benfited from
these goals on 3 occasions while Bayern Munich (the Bundesliga’s most successful team) were
the only away team beneficiary.
Garciano et al. (2005) tested a similar hypotheses about referees favouring te home
team to satisfy the crowd. They examined how crowd effects referee behaviour in the Spanish
Primera Liga. They found similar resutls to Mattias and Kocher 2004: when the home team is
trailing by 1 goal, injury time is on average 35% above the average injury time added (3
minutes) but when the away team are ahead by a goal it is 29% below average. They also found

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evidence that suggests referee bias is caused by crowd pressure. In games when the attendance
is larger the bias increases proving home favoritism as the home fan contingent is usually
larger. This was especially true in single goal margin games as the referees exhibted this bias
to a stronger magnitude.
Scoppa (2008) examined similar hypotheses to this dissertation in the Italian top tier, the
Serie A over the course of the two seaosns from 2003-2005. He investigated the existence of
home favouritism and a big club bias. He identified big teams by their economic, political and
media power. Scoppa examined injury time added on and also the poximity of the crowd as a
causal effect of referee favouritism when allocating additional time at the end of a game, similar
to Buraimo et al (2010). In the italian league abut 30 seconds extra was added on if the home
and/or big team were losing. Crowd proximity proved to be quite significant. Crowd effects
were stronger in stadiums where there was no running track separating the fans and the pitch,
thus the cue from the crowd shouting resulted in more fouls being called.
The studies done by Scoppa (2008), Mattias and Kocher (2004) and Garciano et al. (2005)
all found that crowd pressure plays a pivotol role in influecing referees, thus creating home
advantage. When the games are close coming towards the end the amount added on depends
on the current match result. When the home team were losing by one goal in all three leagues
more time was added on than if they were winning by a single goal, suggesting home
favouritism exists in the respective leagues. This gives the home team a greater chance of
improving their potential outcome and reduces the probability of the away team coming back
form a one goal deficit.

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Studies carried out by Neil and Witt (2008) and Riedl et al. (2015) showed different
results in their studes. Neil and Witt (2008) examined Premier League and first
division.referees in 2001/2002 when referees were employed as professionals. A natural
experiment occurred showeing how financial incentives changed referees’ decion making.
There were two groups: the Select Group, 57 professional match officials who would receive
an annual retainer fee of £33000 and £900 per game, and the national list who weren’t deemed
professional. “The introduction of professional referees created financial rewards for select
groups of refs and this resulted in them allocating injury time more independently than seen
before in Garciano et al. 2005”. They found similar results to other studies suggesting that
when the score margin is larger at 90 minutes that less time is added on.
Riedl et al. (2015) are the most recent to have carried out this type of investigation.
They have looked at the German Bundesliga fixtures from 2000/01 to 2010/11. They examined
the ±1 goal margin at 90 minutes’ bias, whether time is added on so games end as a draw rather
than a team to win (charity bias) and they then examined do these two hypotheses contribute
to home advantage. They confirmed that ±1 goal difference bias does exist but at a smaller
scale (only 19 seconds (± 4) to be the difference) and that when leads were more advantageous
(by 2 or more goals at 90 minutes) less injury time was allowed. They found evidence that
showed favouring for the home team also through the charity bias. 20 seconds ±7 was added
on when a potential goal in injury time would tie the game. In terms of the home teams lead,
as ΔG>0 is much more frequent than ΔG<0, this bias (charity bias) favours the away teams.
The effect of the biases was marginal and they were interpreted to work in opposite directions
in their favouring. They found no support that referee decision on the length of injury time
contributes to home advantage as the amount goals scored n added time was small. This
indicates there is no favouritism by referees in the Bundesliga which contradicts previous

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studies conducted. They conducted a smaller time scale study on the premier league from 2009-
2013 and found that these two biases were present but the effect was only marginal here too.
The ±1 goal at 90 minutes bias caused a 13 second (±7) difference in added time, while the
charity bias caused on average a 16 second (±5) to injury time. Only .03 additional goals for
home teams were scored in injury time suggesting no favouritism.
These studies by Riedl et al. (2015) and Neil and Witt (2008) show that referees may
neglect factors such as pressure from the crowd once financial incentives are involved. The
game of football has transformed as a whole. There is far more money involved in paying
players, managers, officials and far more revenue is generated for clubs meaning that there is
a greater loss/return from decisions going in/against a team’s favour. This suggests there is a
positive relationship between referees pay and their performance. Home advantage and
favouritism has reduced significantly in recent years according to Riedl et al. (2015) suggesting
the game has advanced and training for referees has improved.

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3. DATA COLLECTION
In order to investigate the existence of referee biases in relation to the allocation of added
time in the English Premier League, there was data collected on every fixture during the
2013/2014 and 2014/2015 seasons. This dissertation is testing whether favouritism is displayed
towards two classifications of teams; home team favouritism and ‘big’ club favouritism
(‘Fergie Time’). In order to differentiate a ‘big’ club from the rest of the teams in the league
they must comply with a classification system. This paper defines a big club by their financial
and footballing performance. Thus, ‘big clubs’ must comply with the following standards:
1. The club must be inside the top twenty worldwide clubs by revenue generation in the
Deloitte Football Money League Report for the two seasons being examined;
2013/2014 and 2014/2015.
2. The club must have participated in the Group Stages of the UEFA Champions League
and won a major domestic competition (the English Premier League, the FA cup and/or
the League Cup) in the past decade.
As the commercialization of football is ever increasing, it is important to judge a club on
their sporting exploits as well as their financial position. Any club which doesn’t meet the
criterion for a ‘big club’ will be known as a ‘small club’ hereafter. Only six EPL clubs met the
standards to be classified as a big club: Manchester United, Arsenal, Liverpool, Chelsea,
Manchester City and Tottenham Hotspur.
The dataset includes statistics from 760 EPL games which took place over the course of
two full seasons from August 2013 to May 2015. Data was collected for the matches using the
British Broadcasting Corporation (BBC) website. Fortunately, the data was obtained before the

15. 15
BBC changed the format of their website. The changes they implemented resulted in match
reports not displaying how many seconds of additional time were played at the end of the
second half. Data was collected for each fixture on the teams involved, the amount of added
time allocated at the end of ninety minutes, the goal margin between the teams at the end of
ninety minutes of play, the total number of goals in each game, the total number of yellow and
red cards distributed in each match, the attendance, the referee officiating each game and his
age and experience and whether or not a serious injury occurred during the game (a serious
injury is said to have occurred if over six and a half minutes of added time occurred). The other
stoppages that occur throughout a game include the number of fouls, corner kicks, throw ins
and offside decisions. FIFA’s Law 7 states that these are natural stoppages and that officials
aren’t required to keep record of time elapsed during these stoppages unless when the time
elapsed is excessive. Table 2.1 displays descriptive statistics for the two seasons in question.
Table 3.1 EPL 2013-2015 Descriptive Statistics
Variable Mean St. Dev. Min Max
Additional Time (seconds) 262 80 6 1035
Second half goals 1.33 1.18 0 6
Margin after 90 minutes 1.36 1.16 0 6
Substitutions 5.5 0.8 0 6
Yellow Cards 3.52 2.00 0 10
Red cards1 0.17 0.47 0 6
Referee Experience (Years) 7.64 4.34 0 15
Attendance 36,427 13,985 9100 75,454

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4. METHODOLOGY
To investigate the presence and magnitude of favouritism in question in the 760 EPL
games in the sample, 14 regressions were calculated. Each regression was a simple linear
regression (OLS), corrected for heteroscedasticity. The dependent variable in each regression
was the amount of additional time in seconds. The independent variables include match
statistics mentioned earlier such as: number of second half goals, the goal margin at ninety
minutes, number of substitutions, yellow cards, red cards, the referee’s age and experience, the
log attendance and whether or not a serious injury occurred. The other dependent variables
were used to identify if referees behaved favourably towards the home teams and/or big teams
or if they were behaving adversely towards the away and/or small teams. The match results in
question refer to the outcome at the end of ninety minutes. It does not mean the final result of
the game as a decisive goal may have been scored during the injury time added by the referee
at the end of the second half.
Regressions (1) – (7) include all 760 EPL games from August 2013-May 2015. Regressions
(8) - (14) calculate the existence of favouritism in ‘close’ games. These games are classified
by the goal margin at ninety minutes. If the goal margin is 0 or 1 at the end of normal time then
it is classified as a close game, if the margin is greater than 1 than it isn’t included. By
comparing the magnitude of favourable behaviour in every game versus favouritism in the
close games we can test for the existence of some aspects of Fergie Time. Regressions (1) –
(3) and (8) – (10) are both testing for home advantage using the same regression models.
Regressions (4) – (7) and regressions (11) – (14) are both testing for ‘big’ club favouritism.
Sutter and Kocher (2004), Garciano et al. (2005) Scoppa (2008) and Riedl et al. (2015)

17. 17
investigated the effect the goal margin has on the referee’s decision to allocate added time.
Riedl et al (2015) labelled this type of favouritism as a charity bias. They found similar results
which suggested there was a bias towards the home team in three of Europe’s top league’s:
German Bundesliga (Sutter and Kocher 2004, Riedl et al. 2015) , Italian Serie A (Scoppa,2008)
and Spanish La Liga (Garciano et al. 2005). They each found that when the goal difference was
greater than one at ninety minutes that less time would be added on as opposed to when the
margin is one or zero. Sutter and Kocher (2004), Garciano et al. (2005) and Scoppa (2008)
discovered more added time was allocated when the home team is behind by one goal versus
when they are ahead by one goal, thus providing evidence for Fergie Time in their respective
leagues.
4.1 Home Favouritism
  998765433210 )log( HLHWAEIrcycSMGYt
(1),(8)
tY represents the additional time, in seconds, added by referees at the end of the second half in
each game. G is the amount of goals scored in the second half, M is the goal margin between
the two teams at ninety minutes, S is the number of substitutions made in the game, yc is the
number of cautions distributed by the referee in the game, rc represents the number of red cards
given in the match, I represents whether or not a serious injury occurred during the second half,
E is the referee’s experience officiating in the EPL in years and A represents the attendance.
The figure for attendance had to be given in a log form to erase problems with
heteroscedasticity. As the disparity between the stadium capacities in the EPL, it is better for
the OLS model to bring these values to scale rather than mix the high figures (e.g. Manchester
United vs. Chelsea, August 2013, Attendance: 75,032) with low figures (e.g. QPR vs Hull,
August 2014, Attendance:17603). The dependent variables mentioned already in regression (1)

18. 18
are included in each regression. The dummy variable for regressions (1) - (3), (8) – (10) is a
‘home draw’.
HW in regression (1) represents and home win and HL represents a home loss. These
dependent variables are used to identify whether the referees add different amounts of time
depending on the home team’s result at ninety minutes. The status of the club (big or small)
doesn’t matter here as we are only testing for home favouritism.
  BHLBHDBHWAEIrcycSMGYt 998765433210 )log( (2),
(9)
  SHLSHDSHWAEIrcycSMGYt 998765433210 )log( (3), (10)
Regressions (2) and (3) include the club’s status classified by their financial and
footballing performance as mentioned earlier. In regression (2) BHW represents a big team
winning at home, BHD represents a big club drawing at home and BHL represents a big club
losing at home. There are only six clubs who qualify as a ‘big’ club. Regression (2) compares
their home matches to the rest of the games in the sample. In regression (3) SHW represents a
small club winning, SHD represents a home club drawing at home and SHL represents a home
club losing at home. Regression (3) is similar to regression (2) but considers the opposite
relationship i.e. compares small clubs home games versus the rest of the fixtures in the sample.
By comparing the amount of added time allotted when big teams are winning/losing at home
against when small teams are winning/losing at home it can help us identify the existence of
Fergie Time.

19. 19
4.2 Big club favouritism
  BBLBBWAEIrcycSMGYt 98765433210 )log( (4)(11)
Regression (4) represents games when the six big teams (Manchester United,
Manchester City, Tottenham, Liverpool, Arsenal and Chelsea) play each other. The
independent variables here represent Big vs. Big win (BBW) and Big vs. Big loss (BBL). The
dummy variable for this regression is when the result is a draw at ninety minutes between two
big clubs
  BSLBSWAEIrcycSMGYt 98765433210 )log( (5)(12)
Regression (5) considers when a big team played against a small team at home. BSW
considers a big club winning at home against a big team and BSL represents when a big club is
losing at home against a small team. The dummy variable foe regression (5) is when a big club
and small club are level at ninety minutes. This regression will should provide us with more
evidence on whether Fergie Time exists or not as the two principals involved represent what
Fergie Time refers to: a bias towards the big club.
  SBLSBWAEIrcycSMGYt 98765433210 )log( (6)(13)
Regression (6) examines the opposite to regression (5). For this regression the Small
team are at home against a big team; SBW representing a win for the home side at the end of
ninety minutes while SBL represents the small cub losing to a big team at ninety minutes. The
dummy variable for this regression is SBD, when the small club is drawing to a big side at
home. Similar to regression (5) , this will provide us with evidence supporting or negating the
Fergie Time hypotheses.
  SSLSSWAEIrcycSMGYt 98765433210 )log( (7)(14)

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The final regression testing for big club favouritism measures games involving only
small sides. The dummy variable in this case is the time in seconds added on when the two
sides are level at ninety minutes. Similar to this paper, Scoppa (2008) investigated for a big
team bias in Serie A. He identified big teams by their economic, political and media power off
the field in relation to the match fixing scandal. Serie A referee’s were favourable towards the
big teams in the Serie A when allocating added time. When the suspected teams were losing,
the referee’s added more time, which questions how impartial the Italian league officials
actually are. This gives more evidence that concept of Fergie Time exists not only in the EPL
but in other top League’s in Europe.

21. 21
5. RESULTS
The F test (P>F-Value) for regressions (1) – (14) is significant to the 1% level. The F test was
0.000 for regressions (1) – (14). Table 5.1 shows the OLS results for the 780 EPL over the two
seasons. The R² value for regressions (1) – (3) suggests the model explains 44%-45% of the
variance in the amount of seconds added on by referees. As we can see many of the independent
variables are significant in explaining the reasons for the amount of added time allocated at the
end of the second half. The number of second half goals, the goal margin at full time, yellow
cards and serious injury all contribute to the amount of added time awarded across the three
regressions. As we can see, the goal margin is statistically significant in negatively impacting
the amount of time added on. This suggests that the greater the margin is at the end of the
second half, the referee reduces the amount of time added. Regression (1) provides the first test
for home favouritism. There is a greater amount of time added on whether a home team is
winning or losing at the end of the second half. Regression (1) found that there is 34 seconds
more added on when a home side is winning and 29 seconds extra added on when they are
winning. This provides evidence are impartial between home and away teams as there is
significantly more time added on whether a home team is winning or losing.
Regression (2) considers matches when the big clubs are playing at home only and
compares them to the other matches in the sample. The results here are interesting. A significant
result was found that when a big club is winning (-11.46 seconds) or drawing (-28.67 seconds)
at home, that less time is allocated. Regression (3) examines the opposite relationship to
regression (2). A significant result found that when a small side is winning or losing at home
that more time is added on. This provides evidence that suggests referees are impartial in their
allocation of added time when small teams are at home.

22. 22
If we compare these results from regression (2) and (3), there is evidence of Fergie Time
found in both set of results. The amount of time added on when a big team is winning at home
is significantly less than when a small team is winning at home.
5.1 The Determinants of Additional Time in the EPL 2013-2015
Regression (1) (2) (3)
Constant 283.02*** 223.46** 227.58**
(63.45) (79.64) (82.57)
Goals 7.31*** 7.32*** 7.43***
(2.16) (2.17) (2.16)
Margin -25.97*** -19.85*** -23.81***
(2.75) (2.17) (2.41)
Substitutions 6.29** 7.51** 6.40**
(2.77) (2.82) (1.06)
Yellow Cards 6.46*** 6.53*** 6.49***
(1.06) (1.07) (1.06)
Red Cards 3.39 3.17 3.67
(4.79) (4.53) (4.67)
Serious Injury 183.29*** 182.68*** 216.71***
(12.55) (19.77) (12.51)
Referee Experience 0.59 0.53 0.64
(0.59) (0.59) (0.58)
Log Attendance -19.97 -4.21 -4.66
(13.98) (17.72) (17.70)
Home Win 28.68***
(7.96)
Draw -
Home Loss 34.02***
(8.44)
Big Club Home Winning -11.46*
(6.4)
Big Club Home Drawing -28.67**
(10.90)
Big Club Home Losing 14.57
(9.23)
Small Club Home Winning 16.10*
(4.57)
Small Club Home Drawing -14.08
(8.91)
Small Club Home Losing 14.82*
(6.80)
N 759 759 759
Prob > F 0.000 0.000 0.000
R² 0.4485 0.4403 0.4462
VIF 1.45 1.26 1.47
Statistically significant: ***at 0.1% level; **at 1% level; *at 5% level.
† Results include referee fixed effects.
†† The logarithm of the dependent variable (second half additional time in seconds)produces
results that do not differ statistically from those presented and demonstrate robustness in the
dependent variable.

23. 23
In table 5.2 we see the results from regressions (4) – (7). These regressions investigate
the existence of a bias towards one of the principals in football matches for all games over the
two seasons, based on their status. Regression (4) considers the matches when the six big clubs
play each other only. The R2 value for this regression is strong at 70.32%. AS we can see, five
independent variables are statistically significant in explaining a change in the additional time
added on: the number of second half goals, the occurrence of a serious injury, big home team
winning or losing all contribute positively to the additional time, whereas seen in the previous
set of regressions, the goal margin negatively effects the amount of time allotted. Regression
(4) provides evidence which suggests referees are impartial in their allocation of added time
when two big teams are playing. There is a case which argues that the referee is slightly more
favourable to the big team playing at home because there is 15 seconds more time added on
when the home side is losing against another big team compared to when they are winning.
Regression (5) investigates an aspect of Fergie Time. Regression (5) solely deals with
games when a big club is at home to a small team. This subset amounts to 160 games over the
course of two seasons. The R2 value is 57.93%. Many of the recurring independent variables
are statistically significant in contributing to the increasing/decreasing the amount of seconds
added on: second half goals, the margin, the number of yellow cards and a serious injury. The
most interesting significant independent variable is the value for when a big team is losing at
home to a small team (p<0.1) which presents us with evidence which suggests the existence of
Fergie Time. When a big team is trailing a small team at home, an extra 30 seconds is awarded.
Big clubs do not play significantly more time when they are ahead or level at the end of the
second half. This finding suggests the referees are influenced by the characteristics of the
principals in a football match. As we can see from the results, the suggestion that crowd effects
impact referee decisions can be refuted. By profession, referees are meant to be totally impartial

24. 24
between teams in a game but this paper suggests otherwise. There is no reason big teams should
be experiencing exclusive advantages.
Regression (6) looks at games where a small team is at home versus a big team. This
examines the opposite to regression (5). Only 38.12% of the variance in added time is explained
by regression (6). The same recurring independent variables as regression (5) are statistically
significant. Regression (6) actually provides evidence that referees are impartial in their
allocation of injury time during these games. The difference in time added on when a small
side is winning at home and when a small side is losing at home against a big team is only 1
second. One conclusion can be drawn from the model is that when a small team plays a big
team at home that an extra half a minute will be played if either side are ahead.
Regression (7) is the final regression where all games over the two seasons are included.
As in regression (5) and (6) the same recurring independent variables are statistically
significant with the omission of second half goals. 46% of the OLS models explains variance
in the amount of time added on. Regression (7) examines games only involving small clubs
and it has the largest number of observations. Similar to regression (6) the referees are more or
less completely impartial. Significantly more added time (30 seconds) will be played whether
the home team is losing or winning.

25. 25
5.2 The Determinants of Additional Time – Club Size 2013-2015
Regression (4) (5) (6) (7)
Constant 193.15 50.40 179.08 286.02**
(336.22) (150.74) (183.242) (127.73)
Goals 18.44** 7.49* 13.84** 0.42
(8.16) (3.98) (4.29) (3.65)
Margin -42.76*** -24.67*** -31.52*** -21.14***
(6.44) (4.44) (5.42) (4.86)
Substitutions 9.56 10.29 -0.04 9.47**
(8.02) (6.72) (5.49) (3.45)
Yellow Cards 2.12 8.46*** 5.54** 6.90***
(4.02) (2.15) (2.50) (1.67)
Red Cards 7.81 -7.50* 7.57 3.17
(10.58) (4.02) (11.21) (8.77)
Serious Injury 268.48*** 193.24*** 119.98*** 210.72***
(15.93) (26.75) (15.54) (38.38)
Referee Experience -0.79 0.51 -0.93 0.96
(1.57) (0.88) (1.08) (0.87)
Log Attendance -5.11 24.61 13.74 -24.84
(68.10) (32.36) (41.00) (28.60)
Big Vs. Big Win 87.21***
(23.24)
Big Vs. Big Draw -
Big Vs. Big Loss 101.99***
(21.69)
Big Vs. Small Win 12.69
(15.72)
Big Vs. Small Draw -
Big Vs. Small Loss 30.25*
(16.47)
Small Vs. Big Win 37.00*
(22.41)
Small Vs. Big Draw -
Small Vs. Big Loss 35.50**
(17.64)
Small Vs. Small Win 30.69**
(10.71)
Small Vs. Small Draw -
Small Vs. Small Loss 30.38*
(11.78)
N 60 160 175 363
Prob > F 0.000 0.000 0.000 0.000
R² 0.7032 0.5793 0.3812 0.46
VIF 1.65 1.45 1.49 1.43
Statistically significant: ***at 0.1% level; **at 1% level; *at 5% level.
† Results include referee fixed effects
†† The log of the dependent variable produces results that do not differ statistically from those presented and
demonstrate robustness in the dependent variable.

26. 26
In all regressions, the experience of the referee and the number in attendance didn’t have a
statistically significant impact on the amount of added time. Regressions (2) and (5) can be
interpreted as evidence for Fergie Time. When we compare the results for regression (2) and
regression (3) we can see there is a bias in favour of the big teams when they are losing at home
as regression (3) negates the presence of home favouritism when the home team is a small club.
There isn’t enough proof to criticise referees for behaving favourable towards the big clubs.
Regressions (1), (4) and (6) and (7) actually provide evidence supporting EPL officials’
impartiality. The time added on isn’t advantageous to either principal in question, whether they
are home/away and/or big/small. Regression (4) results can be argued that referees behave
favourably towards the home side.
Regressions (8) – (14) run the same tests but only on close games. The close game
factor (goal margin of ≤1) is something which may play a part on referees behaviour because
they are under more pressure. The margin factor is a key aspect of Fergie Time. The outcome
altering goals scored in additional time are quite low. Alex Ferguson often sought for more
time when his team could score a goal which would change the final outcome of a game in his
teams favour.
The independent variables substitutions, yellow cards and serious injury are
statistically significant in each regression (8) – (10). These set of regressions explain 38% -
39% of the added time allocated by referees at the end of the second half in close games.
Regression (8) suggests referees are favourable to the home team in close matches as 13
seconds extra time is played when they are behind. There is no statistically significant evidence
that suggests referees play more/less time is played when the home team is winning. Regression

27. 27
(9) examines close matches when the six big clubs are at home. There is evidence for Fergie
time here because there is 17 seconds less played when they are winning at home. Regression
(10) suggests that when the small teams are playing, referees are impartial. In these fixtures,
there is significantly more time added on regardless of the outcome at ninety minutes. If we
compare the results for regression (9) and (10), we see that there is significantly less time
played when the big side is leading at home versus when the small teams are leading at home
at the end of the second half in close games.
Regressions (11) – (14) investigate the existence of Fergie Time in close matches where
the status of the principal is identified i.e. big or small. Regression (11) examines games where
the Manchester United, Arsenal, Chelsea, Liverpool, Manchester City, and Tottenham play
each other. This model is strong in explaining the causes of added time as the R2 value is
68.60%. Regression (11) presents findings which show referees giving an advantage to the
home side in close games involving only big clubs. When the home team is winning only 35
seconds extra will be played compared to when the home side is losing where 83 seconds are
played.
Regressions (12) examines the presence of Fergie Time when a big club is at home to
a small side. The model explains 52.61% of additional time awarded. There is no statistically
significant evidence that suggests referees behave favourably towards the big side in close
games. Regression (13) also does not find any evidence of a bias towards the big team or home
side when the small club is at home versus a big team when the margin is ≤1 at ninety minutes.
And finally, regression (14) does not suggest referees behave favourable towards either side
when there just small teams are involved

28. 28
5.3 Determinants of Additional Time - Close Matches 2013-2015
Regression (8) (9) (10)
Constant 231.33* 142.63 146.18
(83.92) (103.69) (109.97)
Goals 4.08 4.62 4.50
(2.92) (3.45) (2.89)
Substitutions 10.63** 11.24*** 10.37**
(3.39) (3.45) (3.31)
Yellow Cards 7.29*** 6.98*** 7.50***
(1.35) (1.36) (1.35)
Red Cards -0.21 -0.80 -0.30
(6.86) (6.43) (6.60)
Serious Injury 166.87*** 165.00*** 167.61***
(22.00) (21.94) (22.67)
Log Attendance -12.78 8.24 5.43
(18.34) (22.94) (23.43)
Home Win 6.54
(7.19)
Draw -
Home Loss 13.45*
(7.55)
Big Club Home Winning -17.10*
(8.79)
Big Club Home Drawing -20.64*
(11.31)
Big Club Home Losing 9.69
(11.10)
Small Club Home Winning 17.45*
(9.17)
Small Club Home Drawing 4.96
(9.13)
Small Club Home Losing 15.13*
(9.05)
N 473 475 470
Prob > F 0.000 0.000 0.000
R² 0.3863 0.3892 0.3863
VIF 1.13 1.19 1.45
Statistically significant: ***at 0.1% level; **at 1% level; *at 5% level
† Results include referee fixed effects.
†† The log of the dependent variable produces results that do not differ statistically from those presented and
demonstrate robustness in the dependent variable.

29. 29
5.4 Determinants of Additional Time - Close Matches & Club Size 2013-2015
Regression (11) (12) (13) (14)
Constant -444.98 -103.57 245.11 167.79
(609.49) (207.65) (230.04) (158.52)
Goals 10.24 5.37 11.34 -3.79
(12.40) (5.01) (7.07) (5.05)
Substitutions 25.88* 14.52* 10.65 10.45*
(13.23) (8.27) (9.14) (4.01)
Yellow Cards 2.26 11.92*** 4.98 7.61***
(5.04) (3.12) (3.40) (2.03)
Red Cards 28.89 -9.42* 3.02 -3.20
(21.37) (4.26) (16.61) (12.63)
Serious Injury 248.95*** 154.40*** 119.94*** 194.37***
(26.67) (13.47) (17.78) (40.95)
Log Attendance 110.69 51.56 -14.66 2.63
(120.37) (45.73) (54.00) (35.02)
Big Vs. Big Win 35.37*
(25.39)
Big Vs. Big Draw -
Big Vs. Big Loss 83.52***
(20.90)
Big Vs. Small Win -8.64
(15.88)
Big Vs. Small Draw -
Big Vs. Small Loss 3.51
(17.12)
Small Vs. Big Win 12.25
(24.18)
Small Vs. Big Draw -
Small Vs. Big Loss 4.28
(17.17)
Small Vs. Small Win 13.52
(8.96)
Small Vs. Small Draw -
Small Vs. Small Loss 15.90
(10.39)
N 35 80 108 250
Prob > F 0.000 0.000 0.000 0.000
R² 0.6860 0.5261 0.2762 0.4443
VIF 1.32 1.29 1.18 1.14
Statistically significant: ***at 0.1% level; **at 1% level; *at 5% level.
† Results include referee fixed effects.
†† The log of the dependent variable produces results that do not differ statistically from those presented and
demonstrate robustness in the dependent variable.

30. 30
6. DISCUSSION & CONCLUSIONS
This paper examines various factors which contribute to additional time in all EPL games over
the course of the 2013/2014 and 2014/2015 season. The impartiality of the EPL is questioned
through testing two hypotheses which suggest; (a) referees behave favourably towards the
home team and (b) referees behave favourably towards the big teams (Fergie Time) when they
decide on how much added time is appropriate to add on in the second half. In the investigation,
we can take away that certain recurring factors contribute to the explanation of how much time
is added on. These include the number of second half goals, the goal margin between the teams
at ninety minutes, the number of cautions and dismissals awarded during a game, the number
of substitutes made and second half serious injuries.
Evidence from the paper provides strong evidence supporting the Fergie Time hypotheses,
although there was weak evidence supporting the existence of home favouritism. The results
of regression (2) suggest that when a big club is winning at home, a significantly less amount
of time is played, this supports the existence of Fergie Time. The evidence suggesting there is
a home bias is relatively weak. Results from regressions (1), (3) and (10) provide evidence
which suggests that referees display no advantage to the home side.
This investigation provides evidence that there is a bias towards big clubs over small clubs in
relation to second half injury time. This concept is commonly referred to as Fergie Time in the
English media. It was discovered that big clubs play over a half a minute more when they are
losing home or away to smaller clubs.

31. 31
Examining the impact that the goal margin has on referees’ decision making brought
about some interesting results. Regressions (8) – (14) considered games where the margin was
≤1 at ninety minutes. Regressions (8) and (11) provide significant evidence which suggests
referees add more time when the home side are down by a goal. Regression (11) considers
games where only the six big teams are playing. It was discovered in this regression, that 84
seconds more are played when the home team is down compared to when they are level. There
was no significant evidence which supported the existence of Fergie Time in the close matches
over the course of the two seasons. In games where the principals were the same standard,
regressions (4) and (7), referees were impartial when adding on time.
As referee experience and the number of people in attendance didn’t have an impact on
the amount of additional time played, we see different results to that found in the Serie A
(Scoppa, 2008). It was discovered that crowd noise and their proximity from the field of play
are the main cause of biased referee decisions. Similar to Scoppa’s (2008) paper though, we
see that there is evidence of favouritism towards the big teams. This paper found evidence that
supports Garciano et al (2005) paper on La Liga. This research found evidence that suggests
there may be a slight charity bias towards the home side when they are behind by one goal in
the EPL. Riedly et al. (2015) discovered this charity bias existed in the German Bundesliga as
well. He found that an extra 19 seconds is played when the margin is only a single goal, whereas
this paper found that the charity bias was towards the home team only in close games.
There are some limitations to this paper. People with a keen interest in football may be
speculative of the six teams classified as big in this paper. There are arguments that other teams
included should be omitted and replaced by others. The method used to establish big teams is

32. 32
appropriate in today’s football climate and can be replicated if investigating other top tier
leagues around the world to identify big teams. This paper only looks at two seasons of the
EPL which has been running since 1992. If it were possible to go back to the first full EPL
season in 1992/1993 and gather similar datasets, it would provide a greater amount of evidence
supporting or refuting the hypotheses questioned here. Future papers may include international
club competitions involving referees from England to test EPL referees behaviour when teams
from outside the United Kingdom are involved.
Solving the issue regarding added time is complex. There is no one right answer, but if
there were clearer directives to referees on how much they should allow to be added for each
stoppage, it would help make the game fairer and protect referees from criticism. If all parties
involved in football were provided with guidelines for how much added time should be allotted
for yellow cards, red cards, substitutions, goals etc. it would reduce the uncertainty. It would
be easier for the officials to appropriate added time and managers and teams could then
comprehend where the time is coming from. One solution, which is hasn’t been mentioned is
removing timekeeping duties from the referee completely. If there were a third party, for
example a television match official or a group of match officials away from the field of play,
put in charge of the allocation of added time. They would be away from the field of play,
therefore, they would be under less pressure from fans, players and managers. The introduction
of additional time at the end of each half has contributed to the excitement and fairness in the
game of football.
The officials are meant to be impartial and recent studies have proved evidence that the FA
may need to intervene to increase the transparency relating to how much time is the right

33. 33
amount of time to allocate. If there were clearer directions given to match officials and if
referees followed them stringently, their impartiality could not be questioned.

34. 34
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