Comparison of television and gate demand in the National Basketball Association

Mongeon, Kevin; Winfree, Jason A.

doi:10.1016/j.smr.2011.09.001

Cited by 48 publications

(24 citation statements)

References 44 publications

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“…The inclusion of this variable in the empirical investigation aims to measure the potential crowding-out effect of competitive matches on our observed event, as in Mongeon and Winfree (2012).…”

Section: The Italian Football Broadcasting Settingmentioning

confidence: 99%

The Determinants of the TV Demand for Soccer: Empirical Evidence on Italian Serie A for the Period 2008-2015

Caruso

Addesa

Domizio

2017

Journal of Sports Economics

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Section: The Italian Football Broadcasting Settingmentioning

confidence: 99%

The Determinants of the TV Demand for Soccer: Empirical Evidence on Italian Serie A for the Period 2008-2015

Caruso

Addesa

Domizio

2017

Journal of Sports Economics

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“…Recent research by Mongeon and Winfree (2012) reported that previous studies examining European soccer leagues and the National Football League found television demand and live attendance demand to be complements, not substitutes.3 Thus, ticket pricing strategies by decision makers may be related to the demand for the games shown on television and the amount of revenue teams can earn from local television contracts. Furthermore, Mongeon and Winfree (2012) examined television and live atten dance demand in the National Basketball Association. They found ticket price, measured by a weighted average ticket price, was insignificant to live attendance demand but had a positive and significant relationship to television demand.…”

Section: Resultsmentioning

confidence: 99%

The Effect of Price Dispersion on Major League Baseball Team Attendance

Soebbing¹,

Watanabe

2014

Journal of Sport Management

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Price dispersion reflects ignorance in the marketplace in which different prices exist from the same or different sellers for a similar good. One of the sources of price dispersion is uncertain demand for a business's good or service. Ticket markets are good opportunities to examine a firm's pricing strategy under demand uncertainty, because professional sports teams have to price their tickets well in advance of the actual event and before actual demand is known. The purpose of the present research is to examine the relationship between price dispersion and regular season average attendance in Major League Baseball. Using a two-step generalized method of moments (GMM) model, the present research finds that an increase in price dispersion leads to a decrease in average attendance.

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“…Snipes and Ingram (2007) remark that promotions have a positive impact on attendance. Mongeon and Winfree (2012) report that winning is more important to a television audience than to match attendance. Jane (2014b) comments that "Closer wins by the competing teams within a league and a larger gap in terms of the point spread between two teams in the betting market lead to higher attendance.…”

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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Comparison of television and gate demand in the National Basketball Association

Cited by 48 publications

References 44 publications

The Determinants of the TV Demand for Soccer: Empirical Evidence on Italian Serie A for the Period 2008-2015

The Determinants of the TV Demand for Soccer: Empirical Evidence on Italian Serie A for the Period 2008-2015

The Effect of Price Dispersion on Major League Baseball Team Attendance

Predicting National Basketball Association Game Attendance Using Random Forests

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