Racial Bias in the Premier League

racial Bias in the postmortem examination unify racial favoritism in Footb each Will We Ever Kick It Out?An econometric Evaluation of Racial Biases in the prime(a) coalitionAbstract racial diversity and unlikeness vex unfortunately flirted a major(ip) progressncy in footb t pop out ensemble, essenti bothy since the creation of the sport due to amic able, political, and economic reasons. Although racialism is non as prevalent as it was before the twenty- sourcecentury, thither ar unchanging issues with the athletic field that exist to this very day. Various theatrical roles of favouritism occur deep down the sport and despite attempts from the FA alongside institutions such as FARE and Kick It Out, the issue and the import it has on m whatever biders, does non look like disappearing anytime soon. in that locationfrom, the aim of this essay entrust be to introduce and hit the books the different graphemes of difference that occur within British footbal l, with the assistance of literary productions fol wretchedups and critical evince, and delve deeper into the problem at good deal development a fixed way outs linear statistical turnabout precedent. This cast get out look into whether racial separatrix is at happen when it comes to corrective sanctions go outn out by referees. I testament be analysing the phenomenon of racial discrimination as an issue in football, with a specific focus on British football and its biggest competition, the post-mortem examination unify, with my assumption stating that vilenesser trimned tenderers ar to a greater extent believably to be schedule than lighter- fellned imposters, which was proved to be false. The results arrange target no considerable certainty that referees exhibit racial diagonal against any form of jumble t 1, with this conclusion trip upn as a c rose-cheekedit to cooking and anti-racism institutions.1. IntroductionThe primary purpose of the follo wingpaper is to introduce and analyse the theme of racial biases within a high-profile team up sport worldwide, and in thiscontext, test for the presence of racial discrimination in the application of corrective sanctions in British football and the postmortem examination unite. Regardingother pieces of hightail it, this specific type of issue has been the subject of exclusivelytwo other studies to date in the context of a maestro team sport, withPrice and Wolfers (2010) providing an application to nonrecreational basketball inthe US and Witt and Reilly (2011) providing question on the Premier League. With a focuson doer, referee and second-specific fixed launchs, the authors differ in their think get windings. Price and Wolfers find to a greater extent(prenominal) personal fouls atomic design 18 calledagainst imposters of a peculiar(prenominal) racial crowd when the games be officiated by resistance landing fieldd to own-race refereeing crews, oli banishum di s contend clear racialbiases. On the other hand, Reilly and Witt conclude that there is no demonstrateof unfair treatment of players from racial minority groups in the collectingof disciplinary card. With differing vector sums arguen, it impart be intriguing tosee my findings and how they compargon to the domesticate d ane with(p) prior to mine.In an attempt to esteem the descent, if one is entrap, between racial discrimination and disciplinary sanctions, we first subscribe to pay worst the topic through existing studies on racial discrimination within British football. This entrust be through throughout the portion books Review which has been split up into 3 sub- fr follow outs. The first section entails me analysing discrimination in fiscal name, with both wages and the valuation of players ground solely on their race to be discussed. Naturally following, the lack of compete opportunities or or else opportunities in their primary beat is researched in the 3rd section. Finally, we come to the fourth section in which we introduce the inspiration scarcet end 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 previousstudies reviewed, we near move on to introducing our seat. This influence willaim to question and analyse therelationship between racial bias and disciplinary sanctions in the sidePremier League and evaluate whether or not this coefficient of correlation truly exists. In browse to investigate this proposed relationship, the put consistsof a frame bring in that uses a rich entropyset on players for all games contend in the PremierLeague between the 3 chastens of 2014/2015 and 2016/17, the virtually recent fullseasons completed to date. The see emphasis in this model will be the correlationbetween the strip nip of a player and the human activity of d isciplinary sanctionsserved everyplace a contend season as measu scarlet by the accumulation of chicken and redcard game. Beckers (2010) economics of Discrimination was intriguing in terms of the analysis as he usesfor a similar model to mine and provided an resource theoretic frameworkin the sense that Premier League referees, who during this time were all white,could exhibit on sightly a taste for discrimination against verso race (ornon-white) players. However, I would prefer to interpret refereeing decisionsas subject to the potential influence of unintended or implicit discrimination,rather than a deeper issue, due to the training they ask in on a weekly stern andthe bene live of the doubt is given to them due to the public press they are set(p) on a lower floor every game. Summary statistics collected from my preliminary model arethen analysed and initial findings arediscussed upon the subject.The following section details the econometric ruleology. Thestructured bod y of the empirical model has been inspired by work scripted by Reilly & Witt (2011) onthe very(prenominal) topic. dissimilitudes in our bodies of work harp with a richer entropysetfrom myself, tooth root with the 2014/15 season whereas Reilly and Witt tooktheir data from the Premier League season of 2003/04. I excessively opted to allow in the routine of red tease a player received toget a to a greater extent completed representation of disciplinary sanctions. Variables such asPosition and Games vie were include in my model whereas Witt and Reillychose to include Age and Native Language spoken. Finally, the intimately manifest changecomes from the dependent unsettled tested against. Both pieces of work testedfor the racial bias of course with ourhypotheses re master(prenominal) similar, however, mymain free-living variable was the shade of their skin whereas their variablewas the race of a player. Asian, Black, sportsmanlike and involved Race were thecategories c hosen for the authors mentioned and I elected for 5 lucidcategories of real uncontaminating, sluttish, Mixed, Dark and Very Dark.Concludingremarks will follow the econometric methodology and empirical results score beendiscussed. One of my expectations prior to the experiment victorious go into will put up to do with the disciplinary sanctions recorded. I will be expecting the bestow amount of separate to be high in semblance to a previous adopt done onthe same topic purely down to how the culture of the game has changed. Yellow separate are usually awarded to playerswho exhibit actions of foul play, whether that be a single violent challengeor an accumulation of softer tackles, but there can besides be acts by playersthat will warrant a back-to-back yellow/red card, regardless(prenominal) of their skin tone.Professional fouls e.g. intentionally stopping a fast break, taking off yourshirt during a celebration, time wasting, and dissent are all actions that aregive n bookings, per the rules of the law. With these occurring on a regular seat throughout agame and commit no bearing on the racialbias from the referee, there is no doubt these would influence my findings. rubicund tease on the other hand are less regularly given out, as they are awarded for more(prenominal)(prenominal) than proficient offences e.g. violent conduct, or if a playerreceives two yellow cards. These evaluations and more will be summarised in theconclusion and whether racial bias plays a part in determining disciplinarysanctions will indefinitely be base during this essay.2. Literature Review Now of course,extreme forms of racism swallow died down in recent times, broadly speaking due to theefforts and genuineattempts by authorities such as Kick It Out and FARE, and also with theabolition of the colour ban in worldwidesports, occurring in the mid-21st century. Despite this, there are button up extensiveamounts of evidence prevalent today that exhibit racial discriminativepractices in modern times. Becker (2010) found various forms of discrimination withinsports which include the following topics In make upity in compensation,inequality in hiring standards and inequality of positions (both vie andmanagerial). With this in mind, current literature on various types of discriminationthat have occurred in British football, and more notably the Premier League,will straightaway be reviewed.2.1 Discrimination in Monetary Terms The themeof racial discrimination in monetary terms in England has attracted limitedresearch, mainly due to the restrictions on access to salary data to the commonresearcher. However, an exclusion comes with the work of Szymanski (2000) whowas able to indirectly figure racial salary discrimination through the exploitation of wage bill information. The informationwas interpreted from a dining table of 39 clubs that had played in the side of meat 1st part between the 1978/79 season and the 1992/93 season (became Pr emierLeague in 1992/93 season). This test assumes all teams were ope military rank on orwithin their own production frontiers, with the stabmarket for players being exceedingly competitive. Szymanski found that clubs with anabove mean(a) proportion of shameful players tended to pull back, on medium andwith other things being equal, at a higher level in relation to their wages. Althoughat first, this does conjure that owners areallowing lighter-skinned players to underperform without any monetaryrepercussions in comparison to their darker peers, Szymanski also found noevidence of consumer or fan- arsediscrimination, which could also mean that this wage bill was just put down tosmart business from each of the clubs management. Continuing on from this and the theme ofdiscrimination in monetary terms, Reilly and Witt (1995) and Medcalfe (2008)provide studies in employ polish off fees that clubs pay for their players,concluding that there is no racialdiscrimination regarding the price of a player once their oerall talent andskillset has been taken into consideration. However, there is a widely held perceptionthat British players are all everywhere-valued compared to overseas players. flat though the Premier League isincreasingly global in its appeal to audiences and players worldwide, therequirement that eight out of the clubs 25-man first-team team must havespent at least three days at an side of meat or Welsh academy before their 21stbirthday adds an artificial hike to the cost of those players, with the demandfor home-grown players at a continuous high (Foster, 2016). Players such as JohnStones, Jordan Pickford, and MichaelKeane all fit the criteria and have since been bought for a combined fee of107.5 million from clubs looking to fill the home-grown rule. Contrastingly, playersthat were purchased from foreign clubs such as Riyad Mahrez, Ngolo Kante andEden Hazard (all of whom have asleep(p) on to win the PFA Player of the Year Awardfor the last 3 years) combine for a transfer fee of precisely 38.4 million, witheach of their someone expected transfer fees increasing exponentially sincearriving in the Premier League (SkySports, 2018). Although clubs might justhave 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 morevaluable compared to their foreign peers, giving them a perceived advantage found on race rather than footballing talent.2.2 Discrimination in Playing Opportunities there has alsobeen a case made in various literature for how discrimination can play a role in the labourmarket that is football. Dr. John Mills wasthe close prominent researcher on thetopic, with his study in 2018 finding that skin tone in side footballcontinues to have a pregnant impact on which positions footballers play onthe pitch. This research was unique inthe sense that a20-point rating scale was apply opposed to the usual binary form of classifyingskin tone (generally either black or white) and was collated, reviewed andratified by astir(predicate) 1,300 researchers.His research found that footballers ofa darker skin tone are more seeming to occupy peripheral positions traditionallyassociated with athleticism and strength dapple teammatesof a lighter skin tone are more promising to fill central positions conventionallyconsidered to need organisational skillsand creativity (Mills et al., 2018). Is there a racial mark to this problem or is it simplylazy charabanc from above? Additionally,Goddard and Wilson (2008) conducted a study based on the potential mental picture thata players race can have on his labourmarket transition probabilities. These probabilities are calculated with thedependent of variables of divisional transition, initial status, and retention, development a three-equation model. They concluded with thefindings of hiring discrimination against black players, with these playershaving higher retention probabilities nonetheless off though they tend to be employed byteams of a higher status divisionally. This means black athletes need toperform at a higher than comely level in comparison to their white equals, enkindleingdiscrimination in the hiring labourmarket. The work of Goddard and Wilson (2008) also seems to suggestthat there are stereotypes within Western culture around black athletes beingmore by nature athletic, whilst white athletes tendingto be more creative and intelligent, which has also been reflected in certainmedia outlets and pundits within the sport when referring to the work of blackplayers. Another approach utilise to investigate this topic came in thework of Bachan, Reilly and Witt (2014),where they explored the correlation between racial composition and matchoutcomes for the cut and English national teams. This was done employmatch-specific variables which include the make-up of the first 11 of therespective teams. Although no solid evidence was found to suggest that racialbiases played a part in the teams transactions, there were still areas of concern. Reports stated that a formerEnglish coach was given instructions to make sure the national team waspreponderantly made up of white players, with the French following suit inopenly questioning the choice of black players in the national side, entirelydisregarding talent and choosing race instead(Bachan et al, 2014). Even national players hap victim to racial profiling,with lookries unwilling to go down the avenue of the emblematic black player flat if the talent is there, thereforely affecting their playing opportunitiesseverely.2.3 Referee BiasesWithstereotypical racism seen to be prevalent throughout the tarradiddle of Britishfootball, I would now like to introduce the inspiration so-and-so my proposedeconometric model, with alleged racial biases from referees to be analysed. Looking at the doings of referees in generally, Dawson an d other researchers (2007) foundthat crossways the period of 1996 to 2003 in the Premier League, referees wereinclined to award moredisciplinary points (yellow/red cards) to the away team rather than the hometeam (Dawson et al., 2007). Although this may bethe case, analysis done by Reilly and Witt (2011) found that, compared toreferees that officiate in the premier tiers of football in Italy, Germany& Spain, English referees are much more professional in terms of theirbias. They are continuously subject to ahigh degree of scrutiny, whether that comes from social media in todays dayand age, or from the Video confederate Referee (VAR) which has just recently comeinto fruition. In addition to this,Premier League referees are monitored by a match assessor who gives them gradeson their performances which is discussed during a compulsory meeting every 2weeks (PGMOL, 2018). Referees are generally required to make decisions withinthe second, so there can be some form of tolerance when per mitting bias uponthem. However, this leniency would not palliate a more sinister form of bias-motivatedby race. Although this is a serious accusation, all Premier League refereesworking today are pull from the white ethnic group, making the propositionmore believably to occur, even unintentionally. Payne (2006) took this study on usinglaboratory evidence and concluded on the theme of weapon bias the idea thatan somebodys drift to unknowingly make stereotypical decisions willincrease with the need to make decisions rapidly, which is the case 99% of thetime for referees, especially in high supplement moments (Payne, 2006). Another form of potentialreferee bias was conducted in the study done by Reilly and Witt (2013). Testsfor home biases were undertaken using player/match level data, with the measured bias coming in the form of thestrictest sanction, the red card. Although evidence was found for home biasesin the Premier League, this did not occur through this form of disciplina ryactions, but rather dinkyer itemors that would not have a major issuing on thegame e.g. fouls given. Further studiestook place by Reilly and Witt in 2016, with the use of both random effect andplayer-specific models and a non-panel pooled logit model, to test forpotential biases in the result of bookings given to the away team. character torefereeing training and the employees themselves, as succeeding(prenominal) to no evidence wasfound to suggest referees were succumbing to hale from external positionors(Reilly & Witt, 2016). As themeasure of social pressure in this experiment was the fans in attendance,especially in the Premier League, the fact that referees are not swayed to makedecisions that would have a major effect on the game to favour the home team, should be recognisedand praised. The opinion ofracial bias in regards to referees in the Premier League intrigued me the nearduring my research, with the effect that a yellow card can have on the overallresult of th e game ofttimes underrated. A booking, especially one given to anintegral member of the team, could change the game plan of verbalize player, mayberendering them unable to make a tackle with the association that he may becautioned for the second time. Sanctionscould also have implications for a players wage rate if a clubs pay structure is related to disciplinary actions.Both of these factors and many more would put clubs with a large add up ofdarker skinned players at a distinct disadvantage and if there is a racial biasshown in referees, especially in a high-profile confederation like the Premier League,this type of behaviour could result in extreme backlash from fans, players andorganisations alike worldwide. entirely in all, this research prompted me to delvedeeper into this proposed form of discrimination through a unique and detailed dataset and interrogate whether or not there isa relationship between race and disciplinary sanctions, which will now bediscussed in the fo llowing section.3. entropyAfterintroducing, discussing and analysingvarious forms of racial discrimination in British football, I would like toresearch whether or not these cause could trickle down to the refereesinvolved in the game, as they play a vital role in the game of football whichoften gets overlooked. With momentous evidence pointing to discrimination,stereotypes and racial bias in other forms of the game, could it also be foundin refereeing decisions concerning darker players? In this model, I will beinvestigating whether or not darker players or more likely to get booked/penalised for fouls, with aggressivestereotypes playing a part.3.1 Collection of Data each the data that has been utilize to create this econometric model wasprovided by the Premier Leagues official website and the following statisticswere taken for each player found in this database Number of yellow cards,Number of red cards, Age, Fouls committed, Games played and their playingposition. I was also able to extract their skin tone through this website, andthe racial classification of these players wasbased on the review of colour photographsfound both on the official Premier League website and the players respectiveclub website (Premier League, 2018). Players were classified solely on theshade of their skin rather than their background as this an experiment that ispurely trying to investigate whether or not there is racial bias, whereforemaking the data used validly. Theseplayers were divided into 5 distinct groups based on their skin tone, which areas follows Very Light, Light, Mixed, Dark and Very Dark.I have also chosen thisclassification as skin colour/tone wouldbe the first thing that a referee would see when dealing with a player and ifracial bias were to be found, referees would stereotype based on their firstimpression, which in this case would be their skin colour alone. Even though the Premier League is a league based inBritain, I still opted to classify players by skin tone specifically, thereforeseparating Black British and White British players, however this may be a routeto go for in a further study of the topic as there may be a bias towards home-grownplayers regardless of their complexion. White British players often fell intothe household of Very Light whereas White European players made up themajority of the Light group but also featured in the former group mentioned probatoryly. Black British, Black European, and Black African playersfeatured crosswise the categories of Mixed, Dark and Very Dark, with those ofAsian descent all featuring in the Very Light category.A time-varying covariate has alsobeen constructed in the form of the age variable. Players at or over the age of33 at the beginning of the respective season have been defined as veterans asseen in submit 1. This variable gives us an idea of how age affects your overallplay when it comes to receiving sanctions. At their ages, their footballingexperience gained could give the m the edge when it comes to avoiding a sanctionas 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 ofyellow cards.Players represent the unit of observation in this experiment and aretaken 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, SwanseaCity, Tottenham, Watford & West Ham. All first-teamplayers that had made at least one appearance for a Premier League club listedabove between the seasons of 2014/2015 to 2016/2017 were eligible for thisexperiment. meliorate effect dummy variables for the 22 clubs als o feature in thisanalysis, with the inclusion of these variables ensuring instruction for thediffering club cultures, as clubs with a more aggressive style of play are morelikely to be given a greater number of bookings. This panel is comprised of1,605 observations carried out on 1,012 players, with close to 37% of playersremaining in the Premier League for multiple seasons during this 3-year period.3.2 Summary StatisticsTable 1 (can be seen on Page 18) provides a description of both thevariables used in the model and some specific summary statistics found usingthe data taken from the players. Standard deviation is represented by the metrical composition in parentheses found underneaththeir respective values. In order to combine both forms of disciplinarysanctions into this model, I have taken yellow cards to the equal one card and red cards to equal two. Forexample, if a player has received 5 yellow cards and 1 red card over the courseof a season, his summarize card count will be set at 7. I have done this asalthough red cards are rare in comparison to yellow cards, I wanted to take intoaccount all forms of penalisation received by players and consequently given outby referees.The average number of cards received per player crossways all seasons was 2.64,with the seasons retentiveness the largest and fewest number of issued cards being2014/15 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 seasonagain showing signs of leniency fromreferees throughout this season, with datasetlow average of 14.7 fouls committed per player. The average player in thesample also played around 19 games per season across the dataset used. Veteranplayers only accounted for 8% of the data, a testament to how tough thedemands of the Premier League are, with most of these players operating asgoalkeepers and defenders. As expected, thedistribution of players in terms of their playing pos ition is concentratedbetween midfielders and defenders, with these 2 positions combining to accountfor over 72% of the sample size. Additionally, the skin tone Very Light wasthe largest player skin tone throughout the dataset, with just over fractional of theplayers go under this category. This again was expected as the majority ofplayers in the English Premier League are home-grown British players. Out ofthe 1605 observations collected in the database, almost 29% did not receive ayellow or red card throughout the 3 seasons. This statistic will have major implicationsfor the model, which will be discussed in the section Econometric Methodology. applicable notesfor the table are as follows Summary Statistics represent the means of the applicable variables, numbers found in parentheses represent the standarddeviation.This data will now be for a preliminary arrange, with the differences inboth fouls committed and cards received in comparison to their skin tone to be try ond. Due to the nature of thismodel, we will first allocate players to either a white or a non-whitecategory, for the purpose of this initial experiment. As we are researchingpotential racial bias in a predominately white league, I felt that placingplayers into these two distinct groups, tobegin with, would be interesting.Essentially, the two skin tones of Very Light and Light will fall into the former category and Mixed, Darkand Very Dark will fall under the latter. This will be done with a set ofparametric (T-Test) and non-parametric tests (Mann-Whitney U-Test) in order to go under if there are any significant differences statistically across these 2groups. Both types of tests were used to assess any statistical differencesbetween the population means for the t-test and the population median for theMann-Whitney U-Test. Where the standard t-test results may lack inapplying for attributes, the non-parametric Mann-Whitney is able to apply forboth variables and attributes, giving us a more reliab le set of results. Havingboth types of statistical tests obtainable also allows us to account for ifthere was no information about the population with regards to thenon-parametric test, although this would not be a problem in our framework,with the number of observations andplayers known. Our parametric test also assumes that variables are measured oneither a ratio or interval level, withboth fouls committed and the total number of cards, falling under the lattercategory (Surbhi, 2016.) The results found for the aforementioned(prenominal) categories arereported in Tables 2 and 3 respectively.Table 2 Fouls Committed Table 3 Cards Received 3.3 Initial FindingsThe data shown above will now be used to analyseand examine any differences that may have been found, concerning both fouls committed,and cards received across the two distinct skin groups. During thisintroductory exercise, I have used a set of both parametric and non-parametric testsin order to determine if any statistical d ifferences lie at a 5% level. dealingswith fouls committed first, the point estimate for the foul count was greaterwith darker players across all seasons on average, with the data also beingstatistically significant at the 5% level. On the other hand, the pointestimate for the total cards received was higher for the lighter group, wassignificant across all seasons on average, and was only not significant duringthe 2015/16 season at the 5% level. Therefore, the results from this preliminaryexercise show that lighter skinned players are penalisedless than darker skinned players are, however,darker players do receive fewer cards onaverage. These initial results are interesting, tobegin with however, the differences in card count especially might not beaccurate with these simple tests. A key characteristic that would affect theoutcome of the total amount of sanctions that a player might receive would behis position. Naturally, goalkeepers are less likely to find themselves in aposition to commit a foul and hence receive a booking, in comparison to amidfielder or a defender. Additional factors such as the club the player playsfor, the age of players and even the number of derbies a player participatesin, have not yet been considered. Because of this, a more thorough analysis ofthis topic requires the use of a moreadvanced econometric test, which will be done in the next section.4. Econometric MethodologyAssuming omittedfactors from the previous experiment such as Positionand Games Played vary across participants, we will be able to account for thesefactors using a standard linear regression. This allows for a relationshipbetween covariates and fixed effects to be seen, but there is no necessity fora parametric distribution to be specified. All observations will be used inthis model but players who do not feature for more than one season make no unequivocal contribution to the within-group rendering, therefore making nodifference to the estimates of the included covariates. dictated effects for allplayers are attainable in this framework, however,which is very multipurpose given the role of the time-invariantfactor of race (any given season) (Reilly & Witt, 2011). 4.1 Regression caseA total of 5variables were used in the linear regression ran through the STATA package package inorder to establish any correlation with the following variables and the totalnumber of cards received Skin tone, Games Played, Position, Seasons and Foulscommitted. The self-reliant variables of Skin tone and Position variables areexpected to have the biggest effect on the number of total cards received, withSkin tone forming the al-Qaida of my hypothesis and Position internallyaffecting my results. This leaves me with the formula for my linear panelregression as followsCARDSi = 0 + 1SKINTONEi +2FOULSi + 3GAMESi + 4POSITIONi+ 5VETERANi +iwhere CARDS is the total number of cards received (yellow and red), FOULS are the number of fouls committedby a player and GAMES are the totalamount 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 theinability to quantify them in a regression, although they might have a minoreffect on the number of cards a player receives e.g. club culture, nature ofthe player. These are incorporated into the error term, i. The variables SKINTONE and POSITION represent ordinal and nominal data respectively and are eachrepresented by their own dummy variablesas seen in Table 1. Likewise, VETERAN isa dummy variable equal to 1 if the player is over 33 years old, and 0 ifotherwise, which again can be seen in Table 1.With the nature of the linearregression, I could encounter some drawbacks using a linear panel method as thedependent variable is assumed to be continuous rather than ordinally discrete. AsI am dealing with count data throughout this model, a Poisson model will also be run in order to offs et this problem. Unlikethe linear panel model, this model will not include players that have received cryptograph yellow cards in their appearances, as they would make no contribution tothe conditional supreme likelihood function. The estimation of these modelswith fixed effects can occur using either a conditional maximum likelihoodestimator or an unconditional estimator. The conditional procedure is conditionedon the sum of the counts for the individual over time, giving us an easierestimation process (Reilly & Witt, 2011). Also, with the econometricsoftware of STATA that I have used, there will be no biases included due to theproblem of incidental parameters. This allows my estimation of the method and use of software to leave me withboth valid and reliable results.4.2 HypothesesWith the main question ofthis model being whether or not darker skinned players are more likely to bebooked than lighter skinned players, we now also have to introduce ourhypothesis in formula form, whi ch can be written asH01 0H11 0This shows both our aughthypothesis in H0, stating the slope of the regression line is lessthan or equal to zero and our alternative hypothesis in H1, statingthe slope of the regression line is greater than zero. The alternativehypothesis represents our initial question, if there is a positive correlationbetween a darker skin tone and the likelihood of a player receiving adisciplinary sanction, with the null hypothesis naturally stating the opposite.In our results, if our coefficients for the categories Mixed, Dark and VeryDark are greater than zero (assuming results are also found to be significantat the 5% level), we can conclude there is a relationship between skin tone andbookings within this model, by rejecting the null hypothesis and accepting thealternative hypothesis. The conclusion of our overall hypothesis should reallyonly hold if the opposite instance is present for the Light and Very Lightcategories. Essentially, if the coefficients for the two categories are alsopositive, we cannot differentiate between the two race categories, as they havethe same correlation in terms of bookings. Further evaluations of our resultswill be discussed in the following section, semiempirical Results.5. Empirical Results With the foundation of the regressionintroduced and explained, we are now able to use the above mentioned to findour empirical results. The estimated model provides a deep exploration into ourhypothesis, with variables such as Positionplayed, and Games Played used in this experiment that would have a directeffect on the hypothesis of whether skin tone affects the referees decisionswhen it comes to disciplinary sanctions. Time dummies are included in theframework (relevant seasons) in order to account for any potential altercationsin refereeing policy over time in the Premier League. For example, the rulethat players will receive bookings for exemplar/diving was only use in 2017 which would affectour dataset and the potential outcome of the results in comparison to seasonsprior. The main catalyst for disciplinary sanctions is expected to be thenumber of fouls committed due to obvious reasons, and this variable will alsofeature in the empirical specification, with the linear and poisson model exhibiting 1605 and 1142 fixedeffects respectively, specific to each observation found across all 3 seasons.Further analysis of the empirical results calculated using the regression foundin Table 4 will be discussed in the next section. Relevant notes for the tableare as follows ***, **, * represent statistical importation at the 1%, 5% and10% level respectively and represents the base group ofestimation and these variableshave been omitted in the regression. The number of club controls within thedatabase is set at 21, with one club omitted as the base club.Table 4 Fixed personal effects Model for Cards Received 5.1 worldwide Analysis With the resultsshown above, we can deduce various findings. When lo oking at the number ofcards given out by referees as the seasons go, there is evidence of leniencywithin the Premier League. On average, the total amount of cards received byplayers has decreased by around 0.19 cards, with the largest decrease coming inthe 2015/16 season at 0.3 cards per game, which was also found to besignificant at the 1% level. However, leniency in cards received does notcorrelate with leniency in fouls given as evidenced with an increase in fouls,although relatively small, at roughly 0.1, with the representation of an extra foulincreasing the card count on average (and ceteris paribus) by the same value.This was anticipated prior to the regression and unsurprisingly, this variableaccounted for over 50% of the variation in total cards received also. cosmos aveteran player was deemed to decrease the total amount of cards, althoughonly minimally at the 5% level, suggesting experience does outweigh a naturaldecline in overall athleticism, but only marginally.Analy sing 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 seenin Figures 1 and 2 below. This number is much higher compared to the studyundertook with data in 2003/04 to 2007/2008 in which only 1.82 cards were givenout on average (Reilly & Witt, 2011). This is most likely due to therebeing stricter rules implemented in order to protect players rather thanunderlying racial factors. There have also been bookings given out to playersdue to simulation (diving) or professional fouls (intentional fouls done tostop a fast break). Both actions arestraight yellow cards which would obviously affect the data and would havenothing to do with stereotypes or racial biases.Figure 1 sum Density Plot for Linear Panel Model Fixed Effects Figure 2 Kernel Density Plot for Poisson Model Fixed Effects 5.2 Position AnalysisPosition wise,the results show that the field position of a player has a s tatistical influence on the variable of cards received. Onaverage (and ceteris paribus), goalkeepers receive around one less booking incomparison to forwards, with this data found to be significant at the 1% level.This result makes sense considering goalkeepers are rarely called into actionin which they must commit a foul compared to forwards who are usually taskedwith pressurising opposing defenders andcommitting professional fouls, to slow down play, which warrants a straight yellow card per therulebook. However, when goalkeepers are committing fouls they are usually thelast 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 professionalbookings for time wasting. Goalkeepers tasked with taking goal kicks use thisas the ameliorate opportunity to time waste unfairly to gain the desired result.As a result of this, refe rees often give out straight bookings as a signal tothe keeper to hurry up, on top of adding on additional time. One result thatstood out to me was the reversal of the coefficient for goalkeepers, with a minus correlation between cards and goalkeepers found with the linear modelbut a positive correlation found with the linear data. With goalkeepers rarelypulled up for bookings to begin with,eliminating goalkeepers with no bookings would have given us a small samplesize with a high tendency to receive bookings, thus skewing the data. In termsof midfielders and defenders, these two positions are statistically more likelyto receive one more card compared to forwards, which was expected. Defendersjust edge out midfielders when it comes to receiving sanctions, which are againfound to be significant at the 1% level and was also expected prior to theexperiment taking place. Overall, there is shown to be a clear variation intotal cards when it comes to a players primary position, with appro ximately90% of the variation in the fixed effects model down to a players differentposition.5.3 Skin tone AnalysisFrom thepreliminary exercise that took place initially, there did appear to be a racial speck to the decisions of the referees in terms of disciplinary sanctions.Players with a darker skin tone were penalisedmore often than their lighter-skinnedpeers, although they were also booked less often as rise in comparison. In thismodel however, there is no evidence ofracial bias towards darker skinned players in this panel when controlling formatch performance affecting variables and a variety of other club controls. With negative coefficients for Mixed, Dark andVery Dark players ranging from around -0.5 to -0.75 for both linear and poisson data, we can see that the slope of theregression line does satisfy the condition for the null hypothesis at the 5%level. This means we cannot reject the null hypothesis and pop off to accept thealternative hypothesis, giving us a conclusio n of no racial bias beingexhibited towards darker players in terms of disciplinary sanctions. In fact, theevidence claims at a 5% significance level that compound race, dark and very dark playersare receiving around a half fewer bookings compared to very light players, andlight players are getting booked at a rate of 50% more often, on average andceteris paribus, with the trend continuing on through our poisson model. Our regression coefficients alsoshow a correlation to where you are more likely to receive a booking if you area lighter player, with numbers decreasing across the range of skin tones, forboth the linear and poisson data. Although there could be a case made that usingthe significant evidence found (other factors still have to be taken intoaccount), referees are carrying themselves in a more lenient manner withplayers outside their skin tone (as all referees in this database would beclassified as very light), we can conclude with our lord hypothesis beingfalse. There is no evidence on the basis of both the linear and poisson model, that darker skinned players area victim of racial bias and therefore are not more likely to receivedisciplinary sanctions compared to their lighter-skinned counterparts.ConclusionThis paper has introducedand analysed various forms of racialdiscrimination that have been displayed throughout British football and mainlythe Premier League. With studies on the topic done prior to mine, a hypothesiswas formed and tested to examine whether or not racial biases play a factorwhen referees give out sanctions to players, namely darker skinned players. Thekey research question was answered using an econometric model analysing a fixed effects panel model. Theevidence gained from this model gave a strong indication that there is nodistinct correlation between darker skinned players and an unfair treatmentwhen it comes to bookings, with there even being evidence of a greater leniencywhen it comes to referees with darker skinned play ers. With referees (whoin this sample were all white) displaying no evidence of a racial bias towardsnon-white players and thus their own race, this could be taken extremelypositively on both anti-racism institutions and training that these referees receive. In his own study, Dr. Witt took from referees being cleared ofany form of racial bias that This may also reflect the fact that referee behaviour is heavily informed by theanti-racist initiatives that have characterisedthe professional game in England over the last decade or more (GetSurrey,2013). Anti-racism institutions such as Kick It Out and FARE could have playeda part in referee behaviour when it comesto this issue, as these movements would be responsible for referees becomingmore racially sensitive and aware over time, thus explaining the outcomeobserved from our model. In terms offuture research that may be done on this agenda, additional variables that werenot used in this framework, or are hard to quantify in a sense, h ave to be heldaccountable for. Variables such as the effects of league position, the cultureof a club, fixtures played home or away, the numberof derby hat games and crowd attendance could all potentially have asignificant effect on sanction outcomes. 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