The short run demand for major league baseball

Hill, James Richard; Madura, Jeff; Zuber, Richard A.

doi:10.1007/bf02300065

Cited by 69 publications

(54 citation statements)

References 4 publications

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“…Hill, Madura, and Zuber (1982), Marcum and Greenstein (1985), Medoff (1986), Whitney (1988, Baade and Tiehan (1990), Knowles, Sherony, and Haupert (1992), Bruggink and Eaton (1996), Kahane and Shmanske (1997), Schmidt andBerri (2002), andButler (2002) There are several results reported in the above studies that are worth highlighting. First, the effects of changes in income on attendance vary both within and across different sports leagues.…”

Section: Literature Reviewmentioning

confidence: 88%

An Analysis of Attendance at Major League Baseball Spring Training Games

Donihue

Findlay

Newberry

2007

Journal of Sports Economics

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This paper examines the determinants of game-day attendance during Major League Baseball's 2002 spring training season in Florida. Our model of game-day attendance includes location, quality of game, and time and weather variables. A censored Tobit estimation procedure is used to estimate our model. Our results indicate that the quality of the game, average ticket price, and several location-specific factors affect attendance. Specifically, our results suggest that changes in income have no effect on attendance while increases in ticket prices cause reductions in attendance. Furthermore, the estimated price elasticity of demand for Major League Baseball during the spring training season is unitary. We also find that a number of factors unique to spring training, such as a nonresident fan base and split squads of players, significantly affect game-day attendance.

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Section: Literature Reviewmentioning

confidence: 88%

An Analysis of Attendance at Major League Baseball Spring Training Games

Donihue

Findlay

Newberry

2007

Journal of Sports Economics

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“…In the economic category, studies have related to ticket price (Bird, 1982;Demmert, 1973;Fillingham, 1977;Noll, 1974;Siegfried & Eisenberg, 1980), per capita income (Bird, 1982;Hart et al, 1975;Noll, 1974), substitute forms of entertainment (Demmert, 1973;Hart et al, 1975;Hill, Madura, & Zuber, 1982;Medoff, 1976;Noll, 1974), television effects (Demmert, 1973;Drever & MacDonald, 1981;Hill et al, 1982), and the effects of other sports attractions in the area (Demmert, 1973;Fillingham, 1977;Hart et al, 1974;Hay & Thueson, 1986;Hill et al, 1982;Medoff, 1976;Noll, 1974). The latter three types of factors have been found to negatively affect attendance of baseball games.…”

mentioning

confidence: 99%

“…The one exception relates to the closeness of the pennant race, and here more analysis is needed to verify the positive influence on attendance. Factors in this category are related to promotions and special events (Hill et al, 1982;Jones, 1984;Siegfried & Eisenberg, 1980), star players (Fillingham, 1977;Hill et al, 1982;Jones, 1984;Medoff, 1976;Noll, 1974;Scully, 1974), the team as a contender or team placement in the standings (Bird, 1982;Hart et al, 1975;Hay & Thueson, 1986;Hill et al, 1982;Siegfried & Eisenberg, 1980), and the closeness of the pennant race (Demmert, 1973;Drever & MacDonald, 198 1;Hart et al, 1975;Jones, 1969Jones, , 1984Noll, 1974).…”

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confidence: 99%

Factors Affecting Attendance at Professional Sport Events

Hansen

Gauthier

1989

Journal of Sport Management

182

141

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Forty attendance items comprised a questionnaire using a Likert 5-point scale to describe the relative importance of each factor from the view of the following sample of 117: CFL (B), NFL (28), NJ3L (21), NBA (23), MISL (ll), American Baseball (14), and National Baseball (12). It was hypothesized that no difference exists between leagues on attendance factors; on factor categories; between winning, moderately winning, and losing teams; and between indoor and outdoor leagues. ANOVA and Tukey tests were used for significant differences. Factor analysis using the principal component model followed by Varimax rotation was applied to the 40 items. The response rate was 46%. Significant differences resulted. Factor analysis derived 10 factor categories. Baseball and the NFL accounted for most of the differences, followed by the MISL. Items generating differences were scheduling, team roster quality, price, forms of entertainment competition, and convenience for fans. This study provides current status, factor categories, and preliminary trends that point to the need for further study with a larger sample.Factors affecting attendance at professional sport events are based in sport marketing. Personnel of pro teams package their products so as to increase attendance, and the effects of the factors can enhance a marketing plan. Any plan is premised on objectives, and one's understanding of factors affecting attendance is a key to developing a plan, strategies, and processes leading to the achievement of those objectives. In professional sport the increase of attendance is a key objective. Hence the importance of gaining knowledge about attendance factors. Schofield (1983) in a recent study of attendance factors reviewed 17 articles and developed four categories to account for those factors: economic, demographic, game attractiveness, and residual preference. Further, Schofield distinguished between production function studies and demand studies. The former focus on team performance and the influence of player skills on team output as measured by percentage wins or by statistics of player performance. The latter focus

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“…Beginning with the work of Demmert (1973), many studies have examined the factors believed to impact gate attendance in professional baseball, including promotions (Boyd and Krehbiel, 1999;Hill, Madura, andZuber, 1982. McDonald andRascher (2000), new stadiums (Clapp and Hakes, 2005), work stoppages (Coates and Harrison, 2005;Schmidt and Berri, 2002), and team success (Baade and Tiehan, 1990;Gitterand Rhoads, 2010).…”

Section: Basketballmentioning

confidence: 99%

Predicting National Basketball Association Game Attendance Using Random Forests

King¹

2017

JCSIT

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Research predicting National Basketball Association game attendance using a random forest approach is presented. Attendance and other data obtained for the 2009 through 2013 basketball seasons are used. Predictor variables include: home team popularity, popularity of opponent, match type (regular season or playoff), day of the week on which the match occurs, home team winning percentage, home city's total personal income, capacity of home venue, conference of the home team, lagged variables on attendance and on winning percentage, and others. A random forest approach, using the R statistical modeling language, was selected in order to use numerous predictor variables without having to first deselect variables and not to over-fit the data. The random forest prediction is compared favorably with that of a multiple linear regression. Additional results indicate that some variables suggested by sports writers do not contribute much to the prediction and that a better measure of a team's popularity is needed. Keywords: ensemble method, R, regressionProfessional basketball team managers need to forecast attendance at matches to plan staff, decide on promotions, and estimate revenues. Numerous predictor variables come to mind from both academic literature and from the popular press. Because there is a large set of predictor variables, it is easy to over fit the data. One way to avoid over fitting is to use a prediction technique that minimizes it. Random forest satisfies this need or requirement. It is employed in this research purposely to avoid over fitting the data. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node.

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The short run demand for major league baseball

Cited by 69 publications

References 4 publications

An Analysis of Attendance at Major League Baseball Spring Training Games

An Analysis of Attendance at Major League Baseball Spring Training Games

Factors Affecting Attendance at Professional Sport Events

Predicting National Basketball Association Game Attendance Using Random Forests

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