Existing theories of home advantage attempt to explain home advantage wherever it exists; that is, they attempt to explain home advantage as a phenomenon. They do not attempt to explain the differences in home advantage between sports. There are only two studies in the literature that attempt to do so, and one of them is limited to contrasting baseball with other sports (Gomez, Pollard, & Luis-Pascal, 2011; Jones, 2015). Fig. 1 presents home advantage from 2006-07 to 2015-16 by year for the five major professional sports for men in North America: soccer, basketball, baseball, American football, and ice hockey. The present paper asks the question "To what extent, if any, does the distance typically covered by an entire team in the course of a game associate with home advantage as depicted in Fig. 1? Such an association does not, of course, necessarily explain home advantage. Nevertheless, if it exists, then the association itself requires explanation. The five sports in Fig. 1 are represented by the elite leagues in each sport: soccer (English Premier League-EPL), basketball (National Basketball Association-NBA), baseball (Major League Baseball-MLB), American football (National Football League-NFL), and ice hockey (National Hockey League-NHL). The data are taken from the statistics pages of the league websites. Home advantage is taken as the percentage of home wins minus 50%. Ties are ignored; only decided games are considered. This way of calculating home advantage is the most commonly used. It is also unbiased. In basketball, baseball, football, and hockey all games are decided one way or the other in regular season play, but in soccer, ties are common, accounting for roughly a quarter of all games in the EPL. Anyway of calculating home advantage that awards a positive value to ties inevitably reduces home advantage and would therefore constitute a bias against soccer vis- a-vis the other four sports. The effect size of the difference between the proportions of all decided games won by the home team in soccer and baseball, 0.62 versus 0.54, is 0.16, as indicated by Cohen's h (Cohen, 1977, pp. 180-85). A value of h this size is accounted as "small" by Cohen's rule of thumb; and the difference between soccer and baseball is the largest of the ten differences. Therefore, all of the differences would be described as small by Cohen's rule, or smaller or smalleryet. Effect size, however, is not the most telling feature of Fig. 1 but, rather, the curves' robustness over time. Each curve consists of ten independent tests or demonstrations of home advantage. All the tests in a given curve indicate home advantage and almost all of the differences between two given curves are in the same direction. The sample sizes each year are large for baseball, basketball, and hockey and moderate for soccer and football. Most of the tests are statistically reliable, many at the 0.001 level; and the curves in Fig.1 are abbreviated. All five go back as far as the SecondWorldWar and two of them, baseball and soccer, go back more than a century (Pollard & Pollard, 2005). Home advantages are small in effect size but very robust with respect to time, at least at elite levels of play. Even differences in home advantage do not change often and, when they do, change slowly, requiring at least a decade to take place.
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