Effectiveness of Home Advantage in Sport Psychology

Introduction to Sports Psychology and Home Advantage

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Introduction to the Sports Psychology and Home Advantage

The world of sports is becoming increasingly important in our society. For many, the sports section is the first part of the daily newspaper that they pick up every morning. In recent years, many cable channels have adopted an all sports format (ESPN, ESPN2, ESPN+, SportsChannel, SportsChannel+, CNN SportIllustrated, etc.). Additionally, the money involved in sports has grown exponentially. The cost to acquire a sports franchise has increased to over $200 million dollars. Athletes’ salaries have increased to over $10 million a year in baseball (Albert Belle, Gary Sheffield) and in basketball there is a player making $36 million dollars a year (Michael Jordan).

As a result of the increasing costs associated with operating a professional sport franchise, many teams are reporting that they do not make enough money from ticket sales, broadcast revenues, concessions and parking to offset the costs of running their teams. Additionally, today’s team owners are finding increased competition from other sports franchises and other entertainment options. Therefore, the pressure to field a consistently competitive team has become more apparent.

A consistently competitive, championship caliber team often draws more support from the community and surrounding metropolitan areas. This support can increase revenue from the team owners in two ways. First, a winning team can generate more attendance, resulting in increased revenue from ticket, concessions, and parking sales. Secondly, a winning team often generates a larger viewing audience. As a result of this larger viewing audience, the team charge higher sponsorship fees. Additionally, the team can increase advertising fees for the various arena signage areas such as the scoreboard, outfield walls, and other backdrops in the ballpark.

The pressure remains, therefore, for team management to identify the important factors involved in fielding a high quality team. Obviously, the talent level of the individual players and coaches is of utmost importance. However, there are other factors involved in winning and losing.

One such factor that is consistently mentioned with winning or losing is whether the team is playing at home or on the road. Literature and common folklore have consistently identified that the home team seems to win more often than the team on the road. The media often portrays the fact that the home team has a general advantage, and this is especially evident in pre-evaluations of playoff competitions and championship series. After a victory at home, players often allude to the support of the home crowd as being a factor in their win. Additionally, the Las Vegas sports books often take into account whether the team is at home or on the road in computing their lines and holds.

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The prevalence of opinion about this home advantage has led to increased scientific examination of the existence of and the factors relating to the home advantage. The importance of determining whether the home advantage exists is twofold. In the theoretical sense it is of importance to study the individual factors relating to the home advantage and their relative contribution to the home advantage. To the players and owners, it is of importance to understand why this home advantage exits, and possible steps that may be taken to eliminate the home advantage when the team is on the road.

The purpose of this study is to examine the home advantage in professional (Major League) football more extensively. Explanations for the home advantage can be categorized into four factors related to the location in which the game is being played. These factors include crowd support, learning or familiarity, differences in rules, and travel (Courneya & Carron, 1992).

Travel is one of the factors most frequently identified as contributing to the home advantage (Schwartz & Barsky, 1977; Palmer, 1978; Edwards, 1979; Varca, 1980). These authors propose that fatigue following travel, disruption of routine, and changes in the body’s biological processes affect the visiting team to the point that their physical performance is undermined.

Therefore, this study will attempt to isolate the travel factor and measure its relationships with the home advantage. This study will extend previous research and measure the relationship not only with won/loss records at home versus on the road, but will also assess the relationship between travel and specific performance measures.



In the world of sports, players, coaches, the media, and fans speculate about why a particular team wins or why an individual performs well. One of the factors identified as contributing to whether a team wins of loses in the location of the game. Studies of historical and archival data show that the home team is more likely to win. This phenomena has been termed the home advantage and it is seen across almost all team sports and is documented at many levels of competition. The literature indicates that the various explanations for the home advantage fall into four game location factors (Courneya & Carron, 1992). This chapter will review the literature that documents the existence of the home advantage, and then will examine why it has been proposed that this home advantage exists. Finally, in response to the future direction set forth by Courneya and Carron (1992), the study will attempt to isolate the travel factor and measure its effects on the home advantage.

The Home Advantage

In the last two decades, several research studies have addressed the home advantage that seems to exist in all levels of sport. The home advantage refers to the “consistent finding that home teams in sport competitions win over 50% of the games played under a balanced home and away schedule” (Courneya & Carron, 1992, p. 13). Several studies have analyzed and documented the existence of the home advantage in several different encompassing different levels of competitions. The home advantage also raises the bet offers by the interesting parties so you may use the best bet calculator if you are going to bet on your favorite team.


Schwartz and Barsky (1977) first examined the existence of the home advantage in football. They found that during the 1971 professional football season, the home team won 58% of the games when excluding tie games. Additionally, in their analysis of collegiate football during the 1971 season, the home advantage was found to be 60% when excluding tie games (Schwartz and Barsky, 1977).

Edwards completed an extensive study on the home advantage in 1979. In examining professional football during the 1974-1976 seasons, he found that the home squad won 54.4% of the games. Furthermore, he found that the home team on average scored more points (21.1 to 18.3) and gave up fewer points (12.3 to 14.0).

In examining collegiate football during the same time period, Edwards (1979) found the home winning percentage to be a statistically significant 58.6%. The home teams on average scored more points (23.1 to 17.6) and allowed fewer points (11.1 to 13.0).

Pollard (1986) examined the home advantage in professional football. His analysis of the 1982 through 1984 seasons in the NFL showed a home advantage of 55%.

Jehue, Street, and Huizenga (1993) reexamined the existence of the home advantage in professional football. They analyzed the records of all professional games played from 1978-1987. The home advantage during this time period for professional football was 56.6%.

Crowd Support

The importance of crowd factors in contributing to the home advantage has been studied fairly extensively. “The crowd factors explanation is based on the assumption that conditions associated with the audience, including its size, density, supportiveness, and proximity are motivating to the home team and lead to enhanced performance” (Pace & Carron, 1992).

As a tribute to the importance of the crowd, some research notes that the game’s location may actually impact how coaches and players approach a game. Pace and Carron (1993) reasoned that many visiting teams altered their normal playing style in order to keep the home crowd out of the game. Likewise, Silva and Andrew (1987) pointed out that coaches “use a pressure defense more often at home in an attempt to rattle the visiting team and immediately involve the crowd” (p. 199). Additionally, several authors pointed out that the officiating may be subconsciously altered in the home team’s favor in response to a large, boisterous crowd (Pace & Carron, 1993; Greer, 1983; Varca, 1980).

Schwartz and Barsky (1977) first examined the contribution of the crowd to the home team’s advantage. In baseball, for example, they found that the home team’s winning percentage increased as attendance increased. When attendance was low, the home advantage was 48%; medium attendance levels saw a home advantage of 55%; and the home advantage increased to 57% when attendance was high (Schwartz & Barsky, 1977).

Home team runs per 100 at-bats follow the same pattern, increasing from 11.0 to 12.1 and 12.7 as attendance increases (1977). This effect is further pronounced when the home team is clearly superior. These statistics may be misleading, however, since a team with a higher winning percentage normally draws more fans than one with a losing record. Therefore, the higher winning percentage may actually cause the increased attendance, rather than being a result of it.

The social facilitation theory has been presented as a possible explanation for the decrease in performance seen by the visiting team. Silva and Andrew (1987) explained that the presence of an audience, especially a non-supportive one, can impair the performance of complex or cognitive skills. Other authors have countered this explanation since the audience is also present for the home team, yet their performance is still better than the performance of the visiting team (Edwards, 1979; Varca, 1980; Salminen, 1993). Salminen (1993) notes that in sports, the audience is not content simply to sit and watch, rather they actually try to influence the outcome of the game. Therefore, the social facilitation theory does not seem applicable to sport.

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Learning of Familiarity

A second game location hypothesized to influence the home advantage has been identified as the learning or familiarity factor (Schwartz & Barsky, 1977; Palmer, 1978; Edwards, 1979; Pollard, 1986; Irving & Goldstein, 1990; Courneya & Carron, 1991; Nelson & Carron, 1991; Courneya & Carron, 1992; Pace & Carron, 1992; Goodman & McAndrew, 1993; Schlenker, et al., 1995; Moore & Brylinsky, 1995). Familiarity with a facility allows the players to unconsciously make decisions based on environmental cues that they have learned well. These authors hypothesized that the visiting team is at a disadvantage in terms of performance because they are less familiar with the home team’s facility. In football, some stadiums are domed and allow for controlled climates while others are open to the changing weather conditions (Edwards, 1979). Additionally, some football fields utilize artificial turf, which often results in a different style of play than the fields that have natural grass (Edwards, 1979).

In 1977, Schwartz and Barsky first argues the role of familiarity in determining the home advantage. They hypothesized that if learning or familiarity was substantive in contributing to the home advantage, then the home advantage would be most significant in the sports where there was the most discrepancies in field dimensions (Schwartz & Barsky, 1977).

Pollard, however, concurred with Schwartz and Barsky’s (1977) reasoning. In his study of the Football League in England, Pollard found that the teams playing on significantly smaller or larger than average playing fields did not show a corresponding higher home advantage than those teams which had more standardized dimensions (1986). However, it is possible that it is the more subtle effects of familiarity that actually contribute to the home advantage (Pollard, 1986). For example, the situation, alignment, and pitch of the stadium and stands may affect how well visiting teams play in other facilities. Since these factors weren’t measured, it is possible that these issues of familiarity may effect the home advantage.


Several authors have proposed that the home advantage may somehow be correlated with the fact that there are rules to which the visiting team adheres that are different from those of the home team (Schwartz & Barsky, 1977; Varca, 1980; Silva & Andrew, 1987; Coruneya & Carron, 1991, 1992; Pace & Carron, 1992; Schlenker, et al., 1995). Related to this aspect is the fact that teams often use different tactics when they are on the road versus when they are at home (Pollard, 1986; Varca, 1980; Silva & Andrew, 1987).

Other studies have proposed, but not actually measured, the hypothesis that special tactics used may (1) include to the home advantage, or (2) mediate the effects of the other factors on the home advantage.


Perhaps the most frequently mentioned factor related to the home advantage is travel. It has been hypothesized that travel factors contribute to the advantage the home team enjoys (Schwartz & Barsky, 1977; Snyder & Purdy, 1985; Pollard, 1986; Silva & Andrew, 1987).

There are several ways in which it has been proposed that travel affects the home advantage. Some authors have proposed that the athletes become fatigued and do not perform up to their potential as a result of long distance travel (Schwartz & Barsky, 1977; Courneya & Carron, 1991). Other authors have alluded to the possibility that travel leads to a disruption of routine: the athletes aren’t sleeping in their own beds, their schedules are more controlled, and they are away from friends and family (Edwards, 1979, Schlenker et al., 1995). Still others believe that travel leads to an alteration of the biological systems leading to a condition commonly known as jet lag (O’Connor, et al., 1991; Winget, et al., 1985).

Pollard (1986) also dismissed the travel factor in explaining the home advantage. He reasoned that since travel has become faster and more comfortable in the last two decades, that there should be a significant decrease in the home advantage when compared to an earlier period. Yet in comparing the home advantage since the 1940’s, no significant decline has occurred (Pollard, 1986).


This research project explores the extent of how home advantage relates to performance and results in football for three divisions; Premier league, Championship league and League One. In particular the research focuses on the factors which affect results such as crowd attendance and refereeing decisions within the past five football seasons. The existence of home advantage is well documented, however it is unclear as to the reasons for why it exists. Courneya and Carron, (1992) highlight the need to identify when and why such an advantage exists. There have been numerous studies investigating the effects of crowd size and density on home advantage. Jurkovac provides evidence that crowds are able to increase competitors confidence to perform better in front of the home crowd.

This study is mainly based on data accessed from www.soccerstats.com, www.premiersoccerstats.com and www.premierleague.com websites. Internal Audit Role in Corporate Governance

The details extracted are:

Three divisions of English League Football Premier Division, Championship and League One over five seasons 2005/2006 to 2010/2011.

Home and away wins for the three divisions.

Referees disciplinary actions at home matches i.e. the number of yellow and red cards issued.

Mean attendance of crowds at home matches in the three divisions.

Data Collection

The data has been collected from the end of seasons League Tables, 2005/2006 through to 2010/2011 for each of the English Leagues, was collected over the five year period, to identify any significant difference in home advantage across the divisions of the English Football League. By looking at five football seasons, rather than just one season, provide a more accurate test of the hypotheses.

The advantage of using published data is that it has been collected by skilled observers. This type of data collection was considered best as it was obtained from a single source that is easily accessible to the researcher i.e. website. By using this data collection, it is possible to gather large quantities of data in a shorter period of time than it would be using an observational approach.


Major League rosters increase from twenty-five to forty players choose to bring up their younger players in order for them to get some experience at the Major League level. Teams that see their likelihood of advancing to the playoffs as impossible or unlikely often opt to play these younger players on a more regular basis. As a result, these inexperienced players often do not put up statistics comparable to the regular players they replace. Therefore, the games from September 1 through September 30 were also omitted.

Coding of Dependent Variables

There were several dependent variables measured in this study. First was whether the home and visiting teams won or lost. Additionally, several performance measures were analyzed. Each of these performance measures are components that affect the outcome of the game (win/loss). These performance measures for each team (home/visiting) analyzed included:

  1. Outcome of the game
  2. Total number of hits
  3. Total number of runs
  4. Total number of errors committed
  5. Total number of double plays
  6. Total number of runs allowed

Coding of Independent Variables

The first independent variable examined was the location of the game. Teams were designated either as playing at home (coded as 0) or playing on the road (coded as 1). There were a few exceptions during interleague play. In some cases the teams playing each other both represented the same city or same metropolitan area. The players on these teams stayed in their own homes and used their own mode of transportation to the field. Therefore both teams were coded as playing at home as this study was designed to measure the effects of travel and not the home advantage per se. cases

The second independent variable investigated was the amount of distance traveled the day before the game. The third one was the number of time zones crossed the day before the game. The fourth independent variable investigated was the direction of travel. The fifth one was the number of days spent on the road or at home.

Data analysis

Once the data was collected, it was prepared for analysis. The data was first sorted by location. This sort by location was done to separate the data for series played at home and series played on the road. There was the possibility that as teams spent more time on the road, their performance levels may decrease, while the longer they played at home, their performance levels would increase. There was concern, then, that if left unsorted for location, these effects would cancel each other out.

Once the data was sorted, a stepwise multiple regression analysis was performed in order to determine the amount the independent variables contributed to the variance in the dependent variables.

The following variables were investigated in the data analysis process:

  1. The relationship between the location of the game and the won/loss records
  2. The relationship between the number of miles traveled the night before, time zones crossed, direction of travel, and game number at home or away and the number of runs scored
  3. The relationship between the number of miles traveled the night before, time zones crossed, direction of travel, and game number at home or away and the number of hits generated
  4. The relationship between the number of miles traveled the night before, time zones crossed, direction of travel, and game number at home or away and the number of errors committed
  5. The relationship between the number of miles traveled the night before, time zones crossed, direction of travel, and game number at home or away and the number of runs allowed
  6. The relationship between the number of miles traveled the night before, time zones crossed, direction of travel, and game number at home or away and the number of double plays executed
  7. The relationship between the number of miles traveled the night before, time zones crossed, direction of travel, and game number at home or away and the outcome of the games.

There was a rationale for conducting multiple regression analyses for each individual team in addition to the composite analysis. Since some teams had a winning record both at home and on the road, and some teams generally lost wherever they played, there was concern that the individual team statistics of the good teams and bad teams would cancel each other out.




This study sought to examine the relationship between travel and several performance measures in English League Football Premier Division, Championship and League One over five seasons 2005/2006 to 2010/2011. This chapter presents the results of an analysis of the existence of the home advantage during this period. Additionally, it presents the contribution of travel factors to the variance in won/loss records and each of the performance measures.

The Home Advantage

The home advantage for all Major League Football teams was calculated to be 53.25%. This figure, as explained earlier, coded some teams who played at an opponent’s park as playing at ‘home’. However, this coding involved only 10 data points out of 3,078 and therefore was relatively inconsequential.

In looking at the individual records, the difference between the home winning percentages and the away winning percentages ranged from 9.09%. The average difference in the home winning percentages and the away winning percentages was calculated to be 6.62%. In all, 23 out of the 28 teams had a higher winning percentage at home than they did on the road.

Table 1: The Difference Between Home and Away Winning Percentages

Team Home record Home win % Away record Away win % Difference Between home WP & away WP Overall record
Angles 33-22 60.00% 29-29 49.12% 10.88% 61-51
Astros 30-24 55.56% 24-31 43.64% 11.92% 54-55
Athletics 22-32 40.74% 18-39 31.58% 9.16% 40-71
Bluejays 28-29 49.12% 26-28 48.15% 0.97% 54-57
Braves 30-23 56.60% 35-21 62.50% -5,90% 65-44
Brewers 34-23 59.65% 21-32 39.62% 20.03% 55-55
Cardinals 29-29 50.00% 23-30 43.40% 6.60% 52-59
Cubs 30-26 53.58% 17-35 32.69% 20.89% 47-61
Dodgers 37-20 64.91% 28-26 51.85% 13.06% 65-46
Expos 28-25 52.83% 25-32 43.86% 8.97% 53-57
Giants 31-26 54.39% 27-29 48.21% 6.18% 58-55
Indians 32-26 55.17% 27-22 55.10% 0.07% 59-48
Mariners 31-23 57.41% 27-28 49.09% 8.32% 58-51
Marlins 30-21 58.82% 34-24 58.62% 0.20% 64-45
Mets 33-22 60.00% 27-26 50.94% 9.06% 60-48
Orioles 32-23 58.18% 37-18 67.27% -9.09% 69-41
Padres 27-27 50.00% 29-30 49.15% 0.85% 56-57
Phillies 25-28 47.17% 18-37 32.73% 14.44% 43-65
Red Sox 28-32 46.67% 27-25 51.92% -5.25% 55-57
Rockies 33-25 56.90% 1-38 32.14% 24.76% 51-63
Royals 24-36 40.00% 19-32 37.25% 2.75% 43-68
Tigers 30-26 53.57% 22-30 42.31% 11.26% 52-56
Twins 24-32 42.86% 21-31 40.38% 2.48% 45-63
White Sox 33-21 61.11% 26-29 47.27% 13.84% 59-50
Yankees 32-22 59.26% 32-19 62.75% -3.49% 64-41
Totals 827-726 53.25% 712-813 46.69% 46.69% 1539-1539


This study has shown that the data collected is consistent with data compiled in other studies in terms of the existence of the home advantage. The 53.25% home advantage calculated by this study is consistent with other authors’ findings in Major League Football. How to Write Dissertation Abstract



It is clear that there are many factors affecting the performance measures and outcome in the game of football. This study was not able to find a meaningful explanation for the variance seen in the won/loss records and the performance measures for each team. Obviously there are many other factors that need to be accounted for or controlled for which were not addressed in this study.

This study, however, did document the historical finding that the home team in Major League Football was 54% of the time a home advantage does exist. However, the travel factor did not adequately explain the variance seen in the won/loss records and the performance measures.

Perhaps the reason for not finding substantial explanation for the variance was that the nuances of travel contribute to the home advantage. Perhaps it is not the miles traveled, but the disruption of routine which gives the home team an advantage. The disruption of routine was not measured in this study. Therefore, a comprehensive field study involving comprehensive observation of habits on the road as compared to at home is warranted. This would involve an extensive multivariate analysis to be performed in order to try to explain the home advantage, the effects of travel, and the variance seen in the won/loss percentages, hits, runs, errors, runs allowed, and double plays executed.

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