Home Team Advantage in English Premier League

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1 Patrice Marek* and František Vávra** *European Centre of Excellence NTIS New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Czech Republic: ** Department of Mathematics, Faculty of Applied Sciences, University of West Bohemia, Czech Republic: Abstract The home team advantage in association football is a well known phenomenon. The aim of this study is to offer a different view on the home team advantage. Usually, in association football, each two teams team A and team B play twice in a season. Once as a home team and once as a visiting team. This offers two results between teams A and B which are combined together to evaluate whether the team A against its opponent B recorded a result at the home field in comparison to the away field that is better, even, or worse. This leads to a random variable with three possible outcomes, i.e. trinomial distribution. Combination and comparison of home and away results of the same two teams is the key to eliminate problems with different strength of teams in the league. Using a uniform distribution as a prior we obtain a Dirichlet distribution as a posterior. This is later used to determine point and interval estimates of unknown parameters of the source trinomial distribution, i.e. the probability that the result at home will be better, even, or worse. Moreover, it is possible to test a hypothesis that the home team advantage for a selected team is statistically significant. This approach can be used to construct a measure of the home team advantage for a single team. Described procedure is demonstrated on English Premier League results from the 1992/1993 season to the 2015/2016 season. 1 Introduction Home team advantage is phenomenon that is well known. It is used in models that estimate probability of win, draw and loss in a match. Usage of home team advantage in modelling and predicting sports results can be traced back to Maher (1982) who used one parameter to adjust strength of team s attack and weakness of team s defence for matches played on away field. Home team advantage was later used in many papers that studied different sports, e.g. in association football by Dixon & Coles (1997), in water polo by Karlis & Ntzoufras (2003) and in ice hockey by Marek et al. (2014). Home team advantage as a self-standing phenomenon was deeply studied by Pollard & Pollard (2005). Their paper offers nice summary of previous research on this phenomenon and analysis of more than 400,000 matches in many sports played between years 1876 and They quantified home team advantage in association football as "the number of points obtained by the home team expressed as a percentage of all points obtained in all games played". The same definition of home team advantage was used by Allen & Jones (2014) in analysis of the English Premier League in the seasons 1992/ /2012. Their results showed that 60.77% (± 8.30) of total points was won in home games. 244

2 This paper offers a slightly different view on home team advantage and instead of points home team advantage is based on number of goals scored and their differences. The advantage of using goals can be demonstrated on results of a team that played the same opponent at the home and away field. Let us assume, that the result at home field was 3 0 win, and the result at away field was 2 1 win. Obviously, better result was recorded at the home field; however, based on points obtained, it is not possible to distinguish between these results as the team is always awarded by 3 points. Method described in the following part will allow to distinguish between these results, and it will offer to measure the home team advantage for individual teams and observe changes during the time. 2 Data and Methods English Premier League results from the 1992/1993 season to the 2015/2016 season were obtained from England Football Results and Betting Odds (2017). Data for the first English Premier League season (1992/1993) were obtained from official website Premier League Football News, Fixtures, Scores & Results (2017). This website was also used for basic control of all data, e.g. total number of scored goals by team in the whole season. Premier League consisted of 22 teams in the first 3 seasons and of 20 teams in the rest of seasons. Balanced schedule was used in all seasons, i.e. each team played each other team exactly two times, once as a home team and once as a visiting team. This means that for each team there are 19 opponents (21 in the first three seasons) with two results in a season. These two results are combined together and used to measure home team advantage which is evaluated according to Definitions 1, 2 or 3. Naturally, each season is analysed separately to eliminate changes in teams that form the league and to eliminate changes in rosters that are usually bigger between seasons. Definition 1. Active measure of home team advantage is a random variable A that can take values 1,0, and 1. A= 1 for team T 1 if two matches between teams T 1 and T 2 in a season ended with a result where team T 1 scored more goals on a field of team T 2 than on its own field. A = 0 for team T 1 if this team scored exactly the same number of goals on a home field and away field and A = 1 for team T 1 if this team scored more goals on its own field than on a field of team T 2. With results h T1 : a T2 on a home field of team T 1 and h T2 : a T1 on a home field of team T 2 the value of random variable A is determined as A = sgn(h T1 a T1 ). (1) Definition 2. Passive measure of home team advantage is a random variable P that can take values 1,0, and 1. P= 1 for team T 1 if two matches between teams T 1 and T 2 in a season ended with a result where team T 1 conceded more goals on a home field than on a field of team T 2. P = 0 for team T 1 if this team conceded exactly the same number of goals on a home field and away field and P = 1 for team T 1 if this team conceded more goals on a field of team T 2 than on its own field. With results h T1 : a T2 on a home field of team T 1 and h T2 : a T1 on a home field of team T 2 the value of random variable P is determined as P = sgn(h T2 a T2 ). (2) Definition 3. Combined measure of home team advantage is a random variable C that can take values 1,0, and 1. C= 1 for team T 1 if two matches between teams T 1 and T 2 in a season ended with a better result 245

3 measured by a goal difference in matches for team T 1 on an away field. C = 0 for team T 1 if goal difference in both matches was exactly the same from T 1 s point of view and C = 1 for team T 1 if this team recorded better result measured by a goal difference in matches on its own field. With results h T1 : a T2 on a home field of team T 1 and h T2 : a T1 on a home field of team T 2 the value of random variable C is determined as C = sgn((h T1 a T2 ) (a T1 h T2 )). (3) All three measures are defined so that value 1 means that a result was better on a home field, 0 means that there was no difference and 1 means that better result was recorded on an away field. Obviously, active measure for team T 1 is passive measure for team T 2. More or less, combination or results between two same teams as used in Definitions 1, 2 or 3 eliminates the fact that teams in league are of different quality. All three random variables can take same values with same interpretation; therefore, in following parts the combined measure C is used and it can be easily substituted by A or P to obtain results for other two measures. English Premier League used balanced schedule in all seasons with exactly two matches between each two teams. Let L denote number of teams in a league (for our data L = 22 or L = 20) then for each team in a season, there are K, K = L 1, opponents. Random sample C 1,C 2,...,C K is obtained as one season s results of given team and its opponents. C i s are considered to be identically distributed because there are no big changes in a team during one season. Therefore, probabilities p 1, p 0 and p 1 of possible outcomes 1,0 and 1 are considered constant in a season. The meaning is that during a season the home team advantage of a team is stationary. The second assumption is that C i s are independent. The interpretation is that matches with one opponent does not influence matches with other opponents. Remark 1. Assumption that C i, i = 1,2,...K, are i.i.d. may not be true in reality. However, it can be expected that violation of this assumption is not strong, and therefore, it is used in the same sense in majority of studies that deal with sports. Without this simplification it would be impossible to use statistics for sports as every single match could be played under slightly different conditions (for example, in different weather conditions). Moreover, undermentioned methods will be robust, and this simplification should not result in any problems with interpretation of obtained findings. Let Z r, r = 1,0,1, is random variable which describes number of cases in a season where it is possible to observe home team advantage (r = 1), away team advantage (r = 1) and no advantage (r = 0). Obviously, for K matches in a season Z 1 + Z 0 = K Z 1. Vector (Z 1,Z 0,Z 1 ) follows trinomial distribution with parameters K and p 1, p 0, p 1. Probability mass function under this notation is given by P(k 1,k 0,k 1 )= K! k 1!k 0!k 1! pk 1 1 p k 0 0 pk 1 1, (4) where K is total number of opponents in a season for one team, p 1, p 0, p 1 are probabilities of occurring a home team advantage (r = 1), an away team advantage (r = 1) and no advantage (r = 0). k 1,k 0,k 1, k 1 + k 0 + k 1 = K, are observations of appropriate advantage. Bayesian inference is used to estimate unknown parameters and consequently confidence intervals. Prior distribution of parameters p 1, p 0 and p 1 is set to be uniform, i.e. it does not matter where a team plays a match and probability in Equation 4 is used as conditional probability of observation under given parameters, i.e. P(k 1,k 0,k 1 p 1, p 0, p 1 ). This leads to posterior probability density of parameters p 1, p 0, p 1 given by 246

4 P(p 1, p 0, p 1 k 1,k 0,k 1 )= G(K + 3) G(k 1 + 1)G(k 0 + 1)G(k 1 + 1) pk 1 1 p k 0 0 pk 1 1, p 1, p 0, p 1 0, 1 Â p r = 1, (5) r= 1 where K is total number of opponents in a season for one team and k 1,k 0,k 1, k 1 + k 0 + k 1 = K, are observations of given advantage. Equation 5 is probability density function of a Dirichlet distribution Dir(a 1 = k 1 +1,a 2 = k 0 +1,a 3 = k 1 +1). Bayesian estimator of probabilities in 4 is given (using squarederror loss function) as mean value of this Dirichlet distribution, i.e. ˆp r = n r + 1, r = 1,0,1. (6) K + 3 If p 1, p 0, p 1 follows Dirichlet distribution Dir(a 1 = k 1 +1,a 2 = k 0 +1,a 3 = k 1 +1), k 1 +k 0 +k 1 = K, then marginal distribution of p r, r = 1,0,1, is Beta(a = k r +1,b = K k r +2) (see (Pitman 1993, p. 473)). This can be used to find individual (1 a l a u )-confidence intervals ( ˆp r,l, ˆp r,u ) for each p r which are given by and ˆp r,l = Beta 1 (a l,k r + 1,K k r + 2) (7) ˆp r,u = Beta 1 (a u,k r + 1,K k r + 2) (8) Remark 2. These individual confidence intervals can be used for simultaneous confidence interval of all three parameters. Based on Bonferroni inequality, they form together a (1 3(a l + a u ))-simultaneous confidence interval. For testing hypothesis it is necessary to obtain P(p 1 > p 1 ) from Equation 5. Using results of (Omar & Joarder 2012, p. 932) and observed values of k 1 and k 1 this probability is estimated as P(p 1 > p 1 )=1 I 1/2 (k 1 + 1,k 1 + 1), (9) where I 1/2 (k 1 + 1,k 1 + 1) is regularized incomplete beta function or cumulative distribution function of Beta distribution. Remark 3. P(p 1 > p 1 ) in this paper is an estimate based on observed values of k 1 and k 1. However, for better readability, the word estimate is omitted in the following text. P(p 1 > p 1 ) is the probability of occurrence of home team advantage, i.e. it can be used as a measure of home team advantage (the higher value of P(p 1 > p 1 ), the higher home team advantage). Hypothesis that the home team advantage is real can be accepted if P(p 1 > p 1 ) 1 a. 3 Results As mentioned before, we analysed English Premier League from the 1992/1993 season to the 2015/2016 season. Totally, 9,366 matches were played in these seasons, and, thanks to promotion and relegation, there are 47 teams that played at least one season in the English Premier League. Out of these teams, only 247

5 seven played in each season (Arsenal, Aston Villa, Chelsea, Everton, Liverpool, Manchester United, and Tottenham). We also remind that in the first three seasons English Premier League consisted of 22 teams and of 20 teams in the following seasons. For each team in each season the hypothesis that home team advantage is real was tested (see Equation 9). The hypothesis is accepted in the case where P(p 1 > p 1 ) These tests were performed for the combined measure of home team advantage that was described in Definition 3. Numbers of teams for which the hypothesis about home team advantage was accepted are presented in Table 1. The highest number was recorded in the 2009/2010 season (17 teams out of 20), and the lowest number was recorded in the 2015/2016 season (2 teams out of 20). Season Teams Season Teams Season Teams 1992/ / / / / / / / / / / / / / / / / / / / / / / /16 2 Table 1: Numbers of teams for which the hypothesis about home team advantage was accepted. Table 2 contains numbers of cases where combined measure of home team advantage (C i ) took value of 1, 0, or 1 in the 2015/2016 season. Each team played with 19 opponents, and therefore 19 observations (samples) are obtained for each team. This table also contains P(p 1 > p 1 ) (based on C i s), and two teams Newcastle and Swansea where it is possible to accept the hypothesis that home team advantage exists are marked with an asterisk. Now, we will present evolution of P(p 1 > p 1 ), estimate ˆp 1, and 95% confidence interval ( ˆp 1,l, ˆp 1,u ) during the time. These results are presented for two selected teams (we choose among the previously mentioned seven teams that played in each season of English Premier League). The first presented team Liverpool is the team with the highest home team advantage (measured simply as an average of obtained probabilities P(p 1 > p 1 ) in all seasons). Liverpool is also the team with the lowest changes in P(p 1 > p 1 ). These changes were measured using two criteria; the first was sample standard deviation of P(p 1 > p 1 ), and the second was sum of absolute differences in P(p 1 > p 1 ) between two consecutive seasons. In both criteria, Liverpool recorded the lowest value out of the seven mentioned teams. Results of Liverpool are in Figure 1 and Figure 2; the first figure contains evolution of P(p 1 > p 1 ) and the second figure contains evolution of ˆp 1, ˆp 1,l, and ˆp 1,u. Seasons where it is possible to accept hypothesis that home team advantage exists, i.e. where P(p 1 > p 1 ) 0.95, are denoted by full bullets ( ) in Figure 1. The team with highest changes in P(p 1 > p 1 ) was Arsenal (this holds for both used criteria). Arsenal also had the second lowest home team advantage (i.e. average value of P(p 1 > p 1 )). The lowest home team advantage among the seven mentioned teams was recorded by Chelsea with average value of P(p 1 > p 1 ) equalling to For comparison, the average value of this probability for Arsenal was and for Liverpool Evolution of parameters for Arsenal are presented in Figure 3 and Figure

6 Team C i = 1 C i = 0 C i = 1 Sum P(p 1 > p 1 ) Arsenal Aston Villa Bournemouth Crystal Palace Everton Chelsea Leicester Liverpool Man City Man United Newcastle Norwich Southampton Stoke Sunderland Swansea Tottenham Watford West Brom West Ham Table 2: Results for the 2015/2016 season Figure 1: Evolution of P(p 1 > p 1 ) for Liverpool. 249

7 Figure 2: Evolution of Bayesian estimate and symmetric 95% confidence interval for p 1 for Liverpool. Figure 3: Evolution of P(p 1 > p 1 ) for Arsenal. 250

8 Figure 4: Evolution of Bayesian estimate and symmetric 95% confidence interval for p 1 for Arsenal. Evolution of P(p 1 > p 1 ) for all teams that played at least once between the 2012/13 season and the 2015/2016 season is presented in Table 3. Bold font is used for those results where it is possible to accept hypothesis that home team advantage exists. Norwich in the 2013/2014 season is nice example that the home team advantage does not ensure good results. It only ensures that results on a home field are better than on an away field but both can mean loss. Norwich in the 2013/2014 season recorded three times C i = 1, once C i = 0, and 15 times C i = 1. For example, Norwich lost 0 1 to Manchester United at home field and 0 4 in Manchester. Obviously, 0 1 is better results than 0 4, and therefore C i = 1 in this case, as described in Definition 3. In fact, home team advantage can be, in this sense, called away field disadvantage. The last presented results are extreme values obtained in all seasons. Five lowest values of P(p 1 > p 1 ) are presented in Table 4 and five highest values in Table 5. These tables also contain numbers of cases where combined measure of home team advantage (C i ) took value of 1,0, or 1 in the referred season. It can be seen that P(p 1 > p 1 ) is in many cases close to 1 but it is usually far from 0. 4 Discussion Methods were presented on English Premier League data between 1992/1993 season and 2015/2016 season. Each team was tested in each season to identify whether it is possible to accept hypothesis about the home team advantage. Results are diverse from two teams with the home team advantage in the 2015/16 season to 17 teams in the 2009/2010 season and with no clear trend. Full results for the 2015/2016 season were presented along with P(p 1 > p 1 ) (i.e. probability that probability of home team advantage is higher than probability of away team advantage) that can be used as a measure of the home team advantage; the higher value, the higher home team advantage. In the 2015/2016 season only Swansea and Newcastle had this probability over 0.95, and hypothesis about existing home team advantage can be accepted for them. 251

9 Team Season 12/13 13/14 14/15 15/16 Arsenal Aston Villa Bournemouth Burnley Cardiff Chelsea Crystal Palace Everton Fulham Hull Leicester Liverpool Man City Man United Newcastle Norwich QPR Reading Southampton Stoke Sunderland Swansea Tottenham Watford West Brom West Ham Wigan Table 3: Evolution of P(p 1 > p 1 ) for all teams in the seasons 2012/ /16. Team Season P(p 1 > p 1 ) C i = 1 C i = 0 C i = 1 Hull 2008/ Norwich 1993/ Blackburn 2003/ Wolves 2011/ Crystal Palace 1997/ Table 4: Five lowest obtained values of P(p 1 > p 1 ). 252

10 Team Season P(p 1 > p 1 ) C i = 1 C i = 0 C i = 1 Blackburn 2009/ Leeds 1992/ West Ham 1997/ Arsenal 1997/ Bolton 2005/ Table 5: Five highest obtained values of P(p 1 > p 1 ) (more decimal places of estimates are shown only for illustration, all results can be considered as equivalent). Since the 1992/1993 season, only seven teams played all seasons of English Premier League. Among these teams, Liverpool had the highest home team advantage and Chelsea had the lowest. It is necessary to remind that the home team advantage means that a result at a home field is better than on an away field, and both results can be loss. Therefore, the home team advantage does not imply good results. In fact, home team advantage can be also named away field disadvantage. In results for all teams and all seasons, the lowest value of P(p 1 > p 1 ) was obtained for Hull in the 2008/2009 season. This probability was 0.038, and it is based on observation that out of 19 opponents Hull recorded better result on away field for 11 of them. On the other side is Blackburn in the 2009/2010 season with the highest recorded value of P(p 1 > p 1 ). Out of 19 opponents, Blackburn played better on a home field in 15 cases, and in 4 cases there was no advantage on either side. 5 Conclusion This paper offers alternative approach for identification of home team advantage in results. The new method is based on goals scored rather than on points awarded. This allows to distinguish matches that looks identical when points are used; for example, a 0 2 loss is not as bad as a 1 5 loss. Three measures of home team advantage were defined: active, passive, and their combination. Later, the Bayesian estimator and confidence intervals for probabilities of appropriate states home team advantage, no advantage, and away team advantage were found. The last theoretical part contains test of the home team advantage. The new method was presented on English Premier League, and results suggest that home team advantage is real; however, it cannot be taken for granted. Acknowledgement This publication was supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports. References Allen, M. S. & Jones, M. V. (2014), The home advantage over the first 20 seasons of the English Premier League: Effects of shirt colour, team ability and time trends., International Journal Of Sport And Exercise Psychology 12(1),

11 Dixon, M. J. & Coles, S. G. (1997), Modelling Association Footbal Scores and Inefficiencies in the Football Betting Market, Journal of the Royal Statistical Society. Series C (Applied Statistics) 46(2), England Football Results and Betting Odds (2017), Premiership Results & Betting Odds.. Karlis, D. & Ntzoufras, I. (2003), Analysis of sports data by using bivariate Poisson models, The Statistician 52(3), Maher, M. J. (1982), Modelling association football scores, Statistica Neerlandica 36(3), Marek, P., Šedivá, B. & Ťoupal, T. (2014), Modeling and prediction of ice hockey match results, Journal of Quantitative Analysis in Sports 10(3), Omar, M. H. & Joarder, A. H. (2012), Some Mathematical Characteristics of the Beta Density Function of Two Variables, Bulletin of the Malaysian Mathematical Sciences Society 35(4), Pitman, J. (1993), Probability, 1 edn, Springer. Pollard, R. & Pollard, G. (2005), Long-term trends in home advantage in professional team sports in North America and England ( ), Journal of Sports Sciences 23(4), Premier League Football News, Fixtures, Scores & Results (2017), Premier League Football Scores, Results & Season Archives.. 254

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