Racism in Football – Will We Ever Kick It Out?

 An Econometric Evaluation of Racial Biases in the Premier League

Abstract

Racism and discrimination have unfortunately played a major role in football, essentially since the creation of the sport due to social, political, and economic reasons. Although racism is not as prevalent as it was before the 21^st century, there are still issues with the subject that exist to this very day. Various types of discrimination occur within the sport and despite attempts from the FA alongside institutions such as FARE and Kick It Out, the issue and the effect it has on many players, does not look like disappearing anytime soon. Therefore, the aim of this essay will be to introduce and analyse the different types of discrimination that occur within British football, with the assistance of literature reviews and critical evidence, and delve deeper into the problem at hand using a fixed effects linear regression model. This model will investigate whether racial bias is at play when it comes to disciplinary sanctions given out by referees.  I will be analysing the phenomenon of racism as an issue in football, with a specific focus on British football and its biggest competition, the Premier League, with my hypothesis stating that: Darker skinned players are more likely to be booked than lighter-skinned players, which was proved to be false. The results found show no considerable evidence that referees exhibit racial bias against any form of skin tone, with this conclusion seen as a credit to training and anti-racism institutions.

1. Introduction

The primary purpose of the following
paper is to introduce and analyse the
topic of racial biases within a high-profile team sport worldwide, and in this
context, test for the presence of racial discrimination in the application of
disciplinary sanctions in British football and the Premier League. Regarding
other pieces of work, this specific type of issue has been the subject of only
two other studies to date in the context of a professional team sport, with
Price and Wolfers (2010) providing an application to professional basketball in
the US and Witt and Reilly (2011) providing research on the Premier League.  With a focus
on player, referee and game-specific fixed effects, the authors differ in their
concluding findings. Price and Wolfers find “more personal fouls are called
against players of a particular racial group when the games are officiated by
opposite compared to own-race refereeing crews”, thus displaying clear racial
biases. On the other hand, Reilly and Witt conclude that there is “no evidence
of unfair treatment of players from racial minority groups in the accumulation
of disciplinary cards”. With differing outcomes shown, it will be intriguing to
see my findings and how they compare to the work done prior to mine.

In an attempt to evaluate the relationship, if one is found, between racial discrimination and disciplinary sanctions, we first have to break down the topic through existing studies on racial discrimination within British football. This will be done throughout the section “Literature Review” which has been split up into 3 sub-sections. The first section entails me analysing discrimination in monetary terms, with both wages and the valuation of players based solely on their race to be discussed. Naturally following, the lack of playing opportunities or rather opportunities in their primary position is researched in the third section. Finally, we come to the fourth section in which we introduce the inspiration behind the essay and the reasons why this was chosen. A proposition of racial bias in referees come to fruition and biases that referees may already exhibit outside racial terms are introduced and deliberated upon.

With previous
studies reviewed, we next move on to introducing our model. This model will
aim to question and analyse the
relationship between racial bias and disciplinary sanctions in the English
Premier League and evaluate whether or not this correlation truly exists. In order to investigate this proposed relationship, the model consists
of a framework that uses a rich dataset on players for all games played in the Premier
League between the 3 seasons of 2014/2015 and 2016/17, the most recent full
seasons completed to date. The key emphasis in this model will be the correlation
between the skin tone of a player and the number of disciplinary sanctions
served over a playing season as measured by the accumulation of yellow and red
cards. Becker’s (2010)
Economics of Discrimination was intriguing in terms of the analysis as he uses
for a similar model to mine and provided an alternative theoretical framework
in the sense that Premier League referees, who during this time were all white,
could exhibit on average a taste for discrimination against opposite race (or
non-white) players. However, I would prefer to interpret refereeing decisions
as subject to the potential influence of unintended or implicit discrimination,
rather than a deeper issue, due to the training they receive on a weekly basis and
the benefit of the doubt is given to them due to the pressure they are placed
under every game. Summary statistics collected from my preliminary model are
then analysed and initial findings are
discussed upon the subject.

The following section details the econometric methodology. The
structured body of the empirical model has been inspired by work written by Reilly & Witt (2011) on
the same topic. Differences in our bodies of work lie with a richer dataset
from myself, beginning with the 2014/15 season whereas Reilly and Witt took
their data from the Premier League season of 2003/04. I also opted to include the number of red cards a player received to
get a more accurate representation of disciplinary sanctions. Variables such as
Position and Games Played were included in my model whereas Witt and Reilly
chose to include Age and Native Language spoken. Finally, the most distinct change
comes from the dependent variable tested against. Both pieces of work tested
for the racial bias of course with our
hypotheses remaining similar, however, my
main independent variable was the shade of their skin whereas their variable
was the race of a player. Asian, Black, White and Mixed Race were the
categories chosen for the authors mentioned and I elected for 5 distinct
categories of Very Light, Light, Mixed, Dark and Very Dark.

Concluding
remarks will follow the econometric methodology and empirical results have been
discussed. One of my expectations prior to the experiment taking place will
have to do with the disciplinary sanctions recorded. I will be expecting the
total amount of cards to be higher in comparison to a previous study done on
the same topic purely down to how the culture of the game has changed.  Yellow cards are usually awarded to players
who exhibit actions of ‘foul play’, whether that be a single violent challenge
or an accumulation of softer tackles, but there can also be acts by players
that will warrant a straight yellow/red card, regardless of their skin tone.
Professional fouls e.g. intentionally stopping a fast break, taking off your
shirt during a celebration, time wasting, and dissent are all actions that are
given bookings, per the rules of the law.  With these occurring regularly throughout a
game and have no bearing on the racial
bias from the referee, there is no doubt these would influence my findings. Red
cards on the other hand are less
regularly given out, as they are awarded for more serious offences e.g. violent conduct, or if a player
receives two yellow cards. These evaluations and more will be summarised in the
conclusion and whether racial bias plays a part in determining disciplinary
sanctions will indefinitely be found during this essay.

2. Literature Review

Now of course,
extreme forms of racism have died down in recent times, mainly due to the
efforts and genuine
attempts by authorities such as Kick It Out and FARE, and also with the
abolition of the colour ban in worldwide
sports, occurring in the mid-21^st century.  Despite this, there are still extensive
amounts of evidence prevalent today that exhibit racial discriminatory
practices in modern times. Becker (2010) found various forms of discrimination within
sports which included the following topics: Inequality in compensation,
inequality in hiring standards and inequality of positions (both playing and
managerial). With this in mind, current literature on various types of discrimination
that have occurred in British football, and more notably the Premier League,
will now be reviewed.

2.1 – Discrimination in Monetary Terms

The theme
of racial discrimination in monetary terms in England has attracted limited
research, mainly due to the restrictions on access to salary data to the common
researcher. However, an exception comes with the work of Szymanski (2000) who
was able to indirectly examine racial salary discrimination through the exploitation of wage bill information. The information
was taken from a panel of 39 clubs that had played in the English 1^st
Division between the 1978/79 season and the 1992/93 season (became Premier
League in 1992/93 season). This test assumes all teams were operating on or
within their own production frontiers, with the labour
market for players being highly competitive. Szymanski found that clubs with an
above average proportion of black players tended to perform, on average and
with other things being equal, at a higher level in relation to their wages. Although
at first, this does suggest that owners are
allowing lighter-skinned players to underperform without any monetary
repercussions in comparison to their darker peers, Szymanski also found no
evidence of consumer or fan-based
discrimination, which could also mean that this wage bill was just put down to
smart business from each of the club’s management.

Continuing on from this and the theme of
discrimination in monetary terms, Reilly and Witt (1995) and Medcalfe (2008)
provide studies in using transfer fees that clubs pay for their players,
concluding that there is no racial
discrimination regarding the price of a player once their overall talent and
skillset has been taken into consideration. However, there is a widely held perception
that British players are over-valued compared to foreign players. Even though the Premier League is
increasingly global in its appeal to audiences and players worldwide, the
requirement that eight out of the club’s 25-man first-team squad must have
spent at least three years at an English or Welsh academy before their 21st
birthday adds an artificial hike to the cost of those players, with the demand
for ‘home-grown’ players at a continuous high (Foster, 2016). Players such as John
Stones, Jordan Pickford, and Michael
Keane all fit the criteria and have since been bought for a combined fee of
£107.5 million from clubs looking to meet the home-grown rule. Contrastingly, players
that were purchased from foreign clubs such as Riyad Mahrez, Ngolo Kante and
Eden Hazard (all of whom have gone on to win the PFA Player of the Year Award
for the last 3 years) combine for a transfer fee of only £38.4 million, with
each of their individual expected transfer fees increasing exponentially since
arriving in the Premier League (SkySports, 2018). Although clubs might just
have a British preference in order to match their required home-grown quota,
there is clear evidence that British players that are coming into the Premier League are regarded as more
valuable compared to their foreign peers, giving them a perceived advantage
based on race rather than footballing talent.

2.2 – Discrimination in Playing Opportunities

There has also
been a case made in various literature for how discrimination can play a role in the labour
market that is football. Dr. John Mills was
the most prominent researcher on the
topic, with his study in 2018 finding that skin tone in English football
continues to have a significant impact on which positions footballers play on
the pitch.  This research was unique in
the sense that a
20-point rating scale was used opposed to the usual binary form of classifying
skin tone (generally either black or white) and was collated, reviewed and
ratified by around 1,300 researchers.
His research found that footballers of
a darker skin tone are more likely to occupy peripheral positions traditionally
associated with athleticism and strength while teammates
of a lighter skin tone are more likely to fill central positions conventionally
considered to need organisational skills
and creativity (Mills et al., 2018). Is there a racial
dimension to this problem or is it simply
lazy coaching from above?

Additionally,
Goddard and Wilson (2008) conducted a study based on the potential effect that
a player’s race can have on his labour
market transition probabilities. These probabilities are calculated with the
dependent of variables of divisional transition, initial status, and retention, using a three-equation model. They concluded with the
findings of hiring discrimination against black players, with these players
having higher retention probabilities even though they tend to be employed by
teams of a higher status divisionally. This means black athletes need to
perform at a higher than average level in comparison to their white equals, suggesting
discrimination in the hiring labour
market. The work of Goddard and Wilson (2008) also seems to suggest
that there are stereotypes within Western culture around black athletes being
more naturally athletic, whilst white athletes tending
to be more creative and intelligent, which has also been reflected in certain
media outlets and pundits within the sport when referring to the work of black
players.

Another approach used to investigate this topic came in the
work of Bachan, Reilly and Witt (2014),
where they explored the correlation between racial composition and match
outcomes for the French and English national teams. This was done using
match-specific variables which included the make-up of the first 11 of the
respective teams. Although no solid evidence was found to suggest that racial
biases played a part in the team’s performances, there were still areas of concern. Reports stated that a former
English coach was given instructions to make sure the national team was
predominantly made up of white players, with the French following suit in
openly questioning the choice of black players in the national side, totally
disregarding talent and choosing race instead
(Bachan et al, 2014). Even national players fall victim to racial profiling,
with countries unwilling to go down the avenue of the ‘typical black player’
even if the talent is there, thus affecting their playing opportunities
severely.

2.3 – Referee Biases

With
stereotypical racism seen to be prevalent throughout the history of British
football, I would now like to introduce the inspiration behind my proposed
econometric model, with alleged racial biases from referees to be analysed.

Looking at the
behaviour of referees in generally, Dawson and other researchers (2007) found
that across the period of 1996 to 2003 in the Premier League, referees were
inclined to award more
disciplinary points (yellow/red cards) to the away team rather than the home
team (Dawson et al., 2007). Although this may be
the case, analysis done by Reilly and Witt (2011) found that, compared to
referees that officiate in the premier tiers of football in Italy, Germany
& Spain, English referees are much more professional in terms of their
bias.  They are continuously subject to a
high degree of scrutiny, whether that comes from social media in today’s day
and age, or from the Video Assistant Referee (VAR) which has just recently come
into fruition.  In addition to this,
Premier League referees are monitored by a match assessor who gives them grades
on their performances which is discussed during a compulsory meeting every 2
weeks (PGMOL, 2018). Referees are generally required to make decisions within
the second, so there can be some form of tolerance when permitting bias upon
them. However, this leniency would not
excuse a more sinister form of bias-motivated
by race. Although this is a serious accusation, all Premier League referees
working today are drawn from the white ethnic group, making the proposition
more likely to occur, even unintentionally. Payne (2006) took this study on using
laboratory evidence and concluded on the theme of ‘weapon bias’ – the idea that
an individual’s tendency to unknowingly make stereotypical decisions will
increase with the need to make decisions rapidly, which is the case 99% of the
time for referees, especially in high leverage moments (Payne, 2006).

Another form of potential
referee bias was conducted in the study done by Reilly and Witt (2013). Tests
for home biases were undertaken using player/match level data, with the measured bias coming in the form of the
strictest sanction, the red card. Although evidence was found for home biases
in the Premier League, this did not occur through this form of disciplinary
actions, but rather smaller factors that would not have a major effect on the
game e.g. fouls given.  Further studies
took place by Reilly and Witt in 2016, with the use of both random effect and
player-specific models and a non-panel pooled logit model, to test for
potential biases in the number of bookings given to the away team. Credit to
refereeing training and the employees themselves, as next to no evidence was
found to suggest referees were succumbing to pressure from external factors
(Reilly & Witt, 2016).  As the
measure of social pressure in this experiment was the fans in attendance,
especially in the Premier League, the fact that referees are not swayed to make
decisions that would have a major effect on the game to favour the home team, should be recognised
and praised.   

The prospect of
racial bias in regards to referees in the Premier League intrigued me the most
during my research, with the effect that a yellow card can have on the overall
result of the game often underrated. A booking, especially one given to an
integral member of the team, could change the game plan of said player, perhaps
rendering them unable to make a tackle with the knowledge that he may be
cautioned for the second time. Sanctions
could also have implications for a player’s wage rate if a club’s pay structure is related to disciplinary actions.
Both of these factors and many more would put clubs with a large number of
darker skinned players at a distinct disadvantage and if there is a racial bias
shown in referees, especially in a high-profile league like the Premier League,
this type of behaviour could result in extreme backlash from fans, players and
organisations alike worldwide. All in all, this research prompted me to delve
deeper into this proposed form of discrimination through a unique and detailed dataset and interrogate whether or not there is
a relationship between race and disciplinary sanctions, which will now be
discussed in the following section.

3. Data

After
introducing, discussing and analysing
various forms of racial discrimination in British football, I would like to
research whether or not these effects could ‘trickle down’ to the referees
involved in the game, as they play a vital role in the game of football which
often gets overlooked. With significant evidence pointing to discrimination,
stereotypes and racial bias in other forms of the game, could it also be found
in refereeing decisions concerning darker players? In this model, I will be
investigating whether or not darker players or more likely to get booked/penalised for fouls, with aggressive
stereotypes playing a part.

3.1 – Collection of Data

All the data that has been used to create this econometric model was
provided by the Premier League’s official website and the following statistics
were taken for each player found in this database: Number of yellow cards,
Number of red cards, Age, Fouls committed, Games played and their playing
position. I was also able to extract their skin tone through this website, and
the racial classification of these players was
based on the review of colour photographs
found both on the official Premier League website and the player’s respective
club website (Premier League, 2018). Players were classified solely on the
shade of their skin rather than their background as this an experiment that is
purely trying to investigate whether or not there is racial bias, therefore
making the data used validly. These
players were divided into 5 distinct groups based on their skin tone, which are
as follows: ‘Very Light’, ‘Light’, ‘Mixed’, ‘Dark’ and ‘Very Dark’.

 I have also chosen this
classification as skin colour/tone would
be the first thing that a referee would see when dealing with a player and if
racial bias were to be found, referees would stereotype based on their first
impression, which in this case would be their skin colour alone. Even though the Premier League is a league based in
Britain, I still opted to classify players by skin tone specifically, therefore
separating Black British and White British players, however this may be a route
to go for in a further study of the topic as there may be a bias towards home-grown
players regardless of their complexion. White British players often fell into
the category of ‘Very Light’ whereas White European players made up the
majority of the ‘Light’ group but also featured in the former group mentioned
significantly. Black British, Black European, and Black African players
featured across the categories of ‘Mixed’, ‘Dark’ and Very Dark’, with those of
Asian descent all featuring in the ‘Very Light’ category.

A time-varying covariate has also
been constructed in the form of the age variable. Players at or over the age of
33 at the beginning of the respective season have been defined as ‘veterans’ as
seen in Table 1. This variable gives us an idea of how age affects your overall
play when it comes to receiving sanctions. At their ages, their footballing
experience gained could give them the edge when it comes to avoiding a sanction
as they know how the referee tends to act during particular situations.
However, these ‘veterans’ could also see their performances declining,
resulting in steps missed and late tackles, often resulting in an influx of
yellow cards.

Players represent the unit of observation in this experiment and are
taken from the 22 clubs that featured over the 3-season period of 2014/15,
2015/16 and 2016/17 that this dataset covers. These clubs include: Arsenal,
Aston Villa, Bournemouth, Burnley, Chelsea, Crystal Palace, Everton, Hull City,
Leicester City, Liverpool, Manchester City, Manchester United, Newcastle United,
Norwich City, Queens Park Rangers, Southampton, Stoke City, Sunderland, Swansea
City, Tottenham, Watford & West Ham. All first-team
players that had made at least one appearance for a Premier League club listed
above between the seasons of 2014/2015 to 2016/2017 were eligible for this
experiment. Fixed effect dummy variables for the 22 clubs also feature in this
analysis, with the inclusion of these variables ensuring control for the
differing club cultures, as clubs with a more aggressive style of play are more
likely to be given a greater number of bookings. This panel is comprised of
1,605 observations carried out on 1,012 players, with close to 37% of players
remaining in the Premier League for multiple seasons during this 3-year period.

3.2 – Summary Statistics

Table 1 (can be seen on Page 18) provides a description of both the
variables used in the model and some specific summary statistics found using
the data taken from the players. Standard deviation is represented by the numbers in parentheses found underneath
their respective values. In order to incorporate both forms of disciplinary
sanctions into this model, I have taken yellow cards to the equal one card and red cards to equal two. For
example, if a player has received 5 yellow cards and 1 red card over the course
of a season, his total card count will be set at 7. I have done this as
although red cards are rare in comparison to yellow cards, I wanted to take into
account all forms of punishment received by players and consequently given out
by referees.

The average number of cards received per player across all seasons was 2.64,
with the seasons holding the largest and fewest number of issued cards being
2014/15 and 2015/16, with an average of 2.82 cards and 2.32 cards respectively.
The average foul count was just under 16 committed, with the 2015/16 season
again showing signs of leniency from
referees throughout this season, with dataset
low average of 14.7 fouls committed per player. The average player in the
sample also played around 19 games per season across the dataset used. ‘Veteran’
players only accounted for 8% of the data, a testament to how tough the
demands of the Premier League are, with most of these players operating as
goalkeepers and defenders. As expected, the
distribution of players in terms of their playing position is concentrated
between midfielders and defenders, with these 2 positions combining to account
for over 72% of the sample size. Additionally, the skin tone “Very Light” was
the largest player skin tone throughout the dataset, with just over half of the
players falling under this category. This again was expected as the majority of
players in the English Premier League are home-grown British players. Out of
the 1605 observations collected in the database, almost 29% did not receive a
yellow or red card throughout the 3 seasons.  This statistic will have major implications
for the model, which will be discussed in the section “Econometric Methodology”.
Relevant notes
for the table are as follows: Summary Statistics represent the means of the
relevant variables, numbers found in parentheses represent the standard
deviation.

This data will now be for a preliminary exercise, with the differences in
both fouls committed and cards received in comparison to their skin tone to be
examined.  Due to the nature of this
model, we will first allocate players to either a “white” or a “non-white”
category, for the purpose of this initial experiment. As we are researching
potential racial bias in a predominately white league, I felt that placing
players into these two distinct groups, to
begin with, would be interesting.
Essentially, the two skin tones of “Very Light” and “Light” will fall into the former category and “Mixed”, “Dark”
and Very Dark” will fall under the latter. This will be done with a set of
parametric (T-Test) and non-parametric tests (Mann-Whitney U-Test) in order to
determine if there are any significant differences statistically across these 2
groups. Both types of tests were used to assess any statistical differences
between the population means for the t-test and the population median for the
Mann-Whitney U-Test.  Where the standard t-test results may lack in
applying for attributes, the non-parametric Mann-Whitney is able to apply for
both variables and attributes, giving us a more reliable set of results. Having
both types of statistical tests available also allows us to account for if
there was no information about the population with regards to the
non-parametric test, although this would not be a problem in our framework,
with the number of observations and
players known. Our parametric test also assumes that variables are measured on
either a ratio or interval level, with
both fouls committed and the total number of cards, falling under the latter
category (Surbhi, 2016.) The results found for the aforementioned categories are
reported in Tables 2 and 3 respectively.

3.3 – Initial Findings

The data shown above will now be used to analyse
and examine any differences that may have been found, concerning both fouls committed,
and cards received across the two distinct skin groups. During this
introductory exercise, I have used a set of both parametric and non-parametric tests
in order to determine if any statistical differences lie at a 5% level. Dealing
with fouls committed first, the point estimate for the foul count was greater
with darker players across all seasons on average, with the data also being
statistically significant at the 5% level. On the other hand, the point
estimate for the total cards received was higher for the lighter group, was
significant across all seasons on average, and was only not significant during
the 2015/16 season at the 5% level.  Therefore, the results from this preliminary
exercise show that lighter skinned players are penalised
less than darker skinned players are, however,
darker players do receive fewer cards on
average. These initial results are interesting, to
begin with; however, the differences in card count especially might not be
accurate with these simple tests. A key characteristic that would affect the
outcome of the total amount of sanctions that a player might receive would be
his position. Naturally, goalkeepers are less likely to find themselves in a
position to commit a foul and thus receive a booking, in comparison to a
midfielder or a defender. Additional factors such as the club the player plays
for, the age of players and even the number of derbies a player participates
in, have not yet been considered. Because of this, a more thorough analysis of
this topic requires the use of a more
advanced econometric test, which will be done in the next section.

4. Econometric Methodology

Assuming omitted
factors from the previous experiment such as Position
and Games Played vary across participants, we will be able to account for these
factors using a standard linear regression. This allows for a relationship
between covariates and fixed effects to be seen, but there is no necessity for
a parametric distribution to be specified. All observations will be used in
this model but players who do not feature for more than one season make no
obvious contribution to the within-group variation, therefore making no
difference to the estimates of the included covariates. Fixed effects for all
players are attainable in this framework, however,
which is very useful given the role of the time-invariant
factor of race (any given season) (Reilly & Witt, 2011).

4.1 – Regression Model

A total of 5
variables were used in the linear regression ran through the STATA software in
order to establish any correlation with the following variables and the total
number of cards received: Skin tone, Games Played, Position, Seasons and Fouls
committed. The independent variables of Skin tone and Position variables are
expected to have the biggest effect on the number of total cards received, with
Skin tone forming the foundation of my hypothesis and Position naturally
affecting my results. This leaves me with the formula for my linear panel
regression as follows:

CARDS_i = β₀ + β₁SKINTONE_i +
β₂FOULS_i + β₃GAMES_i + β₄POSITION_i
+ β₅VETERAN_i +€_i   

where CARDS is the total number of cards received (yellow and red), FOULS are the number of fouls committed
by a player and GAMES are the total
amount of games a player has appeared in, in a given season i which is also present for all other variables.
There are a few variables that have not been taken into account due to the
inability to quantify them in a regression, although they might have a minor
effect on the number of cards a player receives e.g. club culture, nature of
the player. These are incorporated into the error term, €_i. The variables SKINTONE and POSITION represent ordinal and nominal data respectively and are each
represented by their own dummy variables
as seen in Table 1. Likewise, VETERAN is
a dummy variable equal to 1 if the player is over 33 years old, and 0 if
otherwise, which again can be seen in Table 1.

With the nature of the linear
regression, I could encounter some drawbacks using a linear panel method as the
dependent variable is assumed to be continuous rather than ordinally discrete. As
I am dealing with count data throughout this model, a Poisson model will also be run in order to offset this problem. Unlike
the linear panel model, this model will not include players that have received
zero yellow cards in their appearances, as they would make no contribution to
the conditional maximum likelihood function. The estimation of these models
with fixed effects can occur using either a conditional maximum likelihood
estimator or an unconditional estimator. The conditional procedure is conditioned
on the sum of the counts for the individual over time, giving us an easier
estimation process (Reilly & Witt, 2011). Also, with the econometric
software of STATA that I have used, there will be no biases included due to the
problem of ‘incidental parameters’. This allows my estimation of the method and use of software to leave me with
both valid and reliable results.

4.2 – Hypotheses

With the main question of
this model being whether or not darker skinned players are more likely to be
booked than lighter skinned players, we now also have to introduce our
hypothesis in formula form, which can be written as:

H₀:β₁ ≤ 0

H₁:β₁ > 0

This shows both our null
hypothesis in H₀, stating the slope of the regression line is less
than or equal to zero and our alternative hypothesis in H₁, stating
the slope of the regression line is greater than zero. The alternative
hypothesis represents our initial question, if there is a positive correlation
between a darker skin tone and the likelihood of a player receiving a
disciplinary sanction, with the null hypothesis naturally stating the opposite.
In our results, if our coefficients for the categories Mixed, Dark and Very
Dark are greater than zero (assuming results are also found to be significant
at the 5% level), we can conclude there is a relationship between skin tone and
bookings within this model, by rejecting the null hypothesis and accepting the
alternative hypothesis. The conclusion of our overall hypothesis should really
only hold if the opposite instance is present for the Light and Very Light
categories. Essentially, if the coefficients for the two categories are also
positive, we cannot differentiate between the two race categories, as they have
the same correlation in terms of bookings. Further evaluations of our results
will be discussed in the following section, “Empirical Results”.

5. Empirical Results

With the foundation of the regression
introduced and explained, we are now able to use the above mentioned to find
our empirical results. The estimated model provides a deep exploration into our
hypothesis, with variables such as Position
played, and Games Played used in this experiment that would have a direct
effect on the hypothesis of whether skin tone affects the referee’s decisions
when it comes to disciplinary sanctions. Time dummies are included in the
framework (relevant seasons) in order to account for any potential altercations
in refereeing policy over time in the Premier League. For example, the rule
that players will receive bookings for simulation/diving was only implemented in 2017 which would affect
our dataset and the potential outcome of the results in comparison to seasons
prior. The main catalyst for disciplinary sanctions is expected to be the
number of fouls committed due to obvious reasons, and this variable will also
feature in the empirical specification, with the linear and poisson model exhibiting 1605 and 1142 fixed
effects respectively, specific to each observation found across all 3 seasons.
Further analysis of the empirical results calculated using the regression found
in Table 4 will be discussed in the next section. Relevant notes for the table
are as follows: ***, **, * represent statistical significance at the 1%, 5% and
10% level respectively and – represents the base group of
estimation and these variables
have been omitted in the regression. The number of club controls within the
database is set at 21, with one club omitted as the base club.

5.1 – General Analysis

With the results
shown above, we can deduce various findings. When looking at the number of
cards given out by referees as the seasons go, there is evidence of leniency
within the Premier League. On average, the total amount of cards received by
players has decreased by around 0.19 cards, with the largest decrease coming in
the 2015/16 season at 0.3 cards per game, which was also found to be
significant at the 1% level. However, leniency in cards received does not
correlate with leniency in fouls given as evidenced with an increase in fouls,
although relatively small, at roughly 0.1, with the commission of an extra foul
increasing the card count on average (and ceteris paribus) by the same value.
This was anticipated prior to the regression and unsurprisingly, this variable
accounted for over 50% of the variation in total cards received also. Being a
‘veteran’ player was deemed to decrease the total amount of cards, although
only minimally at the 5% level, suggesting experience does outweigh a natural
decline in overall athleticism, but only marginally.

Analysing the data,
the average number of cards received per player was at 2.64 across all seasons,
with both models revealing evidence of a positive skewness, which can be seen
in Figures 1 and 2 below. This number is much higher compared to the study
undertook with data in 2003/04 to 2007/2008 in which only 1.82 cards were given
out on average (Reilly & Witt, 2011). This is most likely due to there
being stricter rules implemented in order to protect players rather than
underlying racial factors. There have also been bookings given out to players
due to simulation (diving) or professional fouls (intentional fouls done to
stop a fast break). Both actions are
straight yellow cards which would obviously affect the data and would have
nothing to do with stereotypes or racial biases.

5.2 – Position Analysis

Position wise,
the results show that the field position of a player has a statistical influence on the variable of cards received. On
average (and ceteris paribus), goalkeepers receive around one less booking in
comparison to forwards, with this data found to be significant at the 1% level.
This result makes sense considering goalkeepers are rarely called into action
in which they must commit a foul compared to forwards who are usually tasked
with pressurising opposing defenders and
committing ‘professional’ fouls, to slow down play, which warrants a straight yellow card per the
rulebook. However, when goalkeepers are committing fouls they are usually the
last man, meaning these fouls are more likely to lead to straight red cards,
thus affecting the card count substantially for the goalkeeper position.
Additionally, goalkeepers are the main culprits when it comes to receiving ‘professional
bookings’ for time wasting. Goalkeepers tasked with taking goal kicks use this
as the perfect opportunity to time waste unfairly to gain the desired result.
As a result of this, referees often give out straight bookings as a signal to
the keeper to hurry up, on top of adding on additional time.

One result that
stood out to me was the reversal of the coefficient for goalkeepers, with a
negative correlation between cards and goalkeepers found with the linear model
but a positive correlation found with the linear data. With goalkeepers rarely
pulled up for bookings to begin with,
eliminating goalkeepers with no bookings would have given us a small sample
size with a high tendency to receive bookings, thus skewing the data. In terms
of midfielders and defenders, these two positions are statistically more likely
to receive one more card compared to forwards, which was expected. Defenders
just edge out midfielders when it comes to receiving sanctions, which are again
found to be significant at the 1% level and was also expected prior to the
experiment taking place. Overall, there is shown to be a clear variation in
total cards when it comes to a player’s primary position, with approximately
90% of the variation in the fixed effects model down to a player’s different
position.

5.3 – Skin tone Analysis

From the
preliminary exercise that took place initially, there did appear to be a racial
undertone to the decisions of the referees in terms of disciplinary sanctions.
Players with a darker skin tone were penalised
more often than their lighter-skinned
peers, although they were also booked less often as well in comparison. In this
model however, there is no evidence of
racial bias towards darker skinned players in this panel when controlling for
match performance affecting variables and a variety of other club controls.  With negative coefficients for Mixed, Dark and
Very Dark players ranging from around -0.5 to -0.75 for both linear and poisson data, we can see that the slope of the
regression line does satisfy the condition for the null hypothesis at the 5%
level. This means we cannot reject the null hypothesis and fail to accept the
alternative hypothesis, giving us a conclusion of no racial bias being
exhibited towards darker players in terms of disciplinary sanctions.

In fact, the
evidence claims at a 5% significance level that mixed race, dark and very dark players
are receiving around a half fewer bookings compared to very light players, and
light players are getting booked at a rate of 50% more often, on average and
ceteris paribus, with the trend continuing on through our poisson model. Our regression coefficients also
show a correlation to where you are more likely to receive a booking if you are
a lighter player, with numbers decreasing across the range of skin tones, for
both the linear and poisson data.  Although there could be a case made that using
the significant evidence found (other factors still have to be taken into
account), referees are carrying themselves in a more lenient manner with
players outside their skin tone (as all referees in this database would be
classified as very light), we can conclude with our original hypothesis being
false. There is no evidence on the basis of both the linear and poisson model, that darker skinned players are
a victim of racial bias and therefore are not more likely to receive
disciplinary sanctions compared to their lighter-skinned counterparts.

Conclusion

This paper has introduced
and analysed various forms of racial
discrimination that have been displayed throughout British football and mainly
the Premier League. With studies on the topic done prior to mine, a hypothesis
was formed and tested to examine whether or not racial biases play a factor
when referees give out sanctions to players, namely darker skinned players. The
key research question was answered using an econometric model analysing a fixed effects panel model. The
evidence gained from this model gave a strong indication that there is no
distinct correlation between darker skinned players and an unfair treatment
when it comes to bookings, with there even being evidence of a greater leniency
when it comes to referees with darker skinned players.

With referees (who
in this sample were all white) displaying no evidence of a racial bias towards
non-white players and thus their own race, this could be taken extremely
positively on both anti-racism institutions and training that these referees receive. In his own study, Dr. Witt took from referees being cleared of
any form of racial bias that “This may also reflect the fact that referee behaviour is heavily informed by the
anti-racist initiatives that have characterised
the professional game in England over the last decade or more” (GetSurrey,
2013). Anti-racism institutions such as Kick It Out and FARE could have played
a part in referee behaviour when it comes
to this issue, as these movements would be responsible for referees becoming
more racially sensitive and aware over time, thus explaining the outcome
observed from our model.  

In terms of
future research that may be done on this agenda, additional variables that were
not used in this framework, or are hard to quantify in a sense, have to be held
accountable for. Variables such as the effects of league position, the culture
of a club, fixtures played home or away, the number
of derby games and crowd attendance could all potentially have a
significant effect on sanction outcomes. Factors like this would allow for the club, referee and game effects to be controlled
which would provide us with more accurate and perhaps more insightful findings
into disciplinary outcomes and whether or not these would have a significant
effect on the racial bias as well,
remains to be seen.

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