semester paper

Sport Marketing Quarterly, 2015, 24, 142-158, © 2015 West Virginia University

What Factors Determine the Fans’ Willingness to Pay for Bundesliga Tickets? An Analysis of Ticket Sales in the Secondary Market Using Data from ebay.de Christoph Kemper and Christoph Breuer Christoph Kemper, DPhil, is a researcher in the Department of Sport Economics and Sport Management at German Sport University Cologne. His research interests include pricing strategies, willingness to pay, and statistical modeling. Christoph Breuer, PhD, is a professor in the Department of Sport Economics and Sport Management at German Sport University Cologne. His research interests include sport organizational economics and economics of sponsoring.

Abstract In 2009, the San Francisco Giants of the United States’ Major League Baseball was the first sports club to implement a dynamic pricing system, and since that time many sport organizations have followed suit. To date, however, no German sports club has applied dynamic pricing. This paper lays the ground for such a development by identifying determinants of fans’ willingness to pay for Bundesliga tickets. The current study evaluates ticket sales in the German secondary market. Ticket prices of eBay auctions were collected daily during the second half of the Bundesliga season 2013-14. A data set of 6,510 auctions was analyzed by means of a two stage least squares regression. Results suggest that tickets in the secondary market are resold for nearly twice as much as the original face value of the ticket. The final model includes 23 significant vari­ ables and explains 59.6% of the variance. The overall results are comparable to previous studies, although there are a number of unique aspects to the current paper. Sport managers can apply these finding to fur­ ther differentiate their current variable ticket pricing strategy or to implement a more sophisticated dynam­ ic pricing approach.

Introduction Ticket pricing of sporting events, a much-discussed topic in the business of sports, has intensified in recent years due to the development of dynamic ticket pricing systems in the sports industry (Drayer, Shapiro, & Lee, 2012b). Throughout the 20*“ century most sports managers employed either a one-size-fits-all approach, by which prices for every ticket and every game are exactly the same, or a seat-location approach, by which prices are set in correlation to proximity to the field (Drayer et al., 2012b). Due to the increasing costs of player salaries and the continuous improvement of sta­ diums, increased income is required (Drayer et al., 2012b). Two examples of coping strategies for generat­ ing additional income are the growth of sport sponsor­ ships and the negotiation of stadium-naming rights

(Howard & Crompton, 2005). Another source of rev­ enues, examined in detail here, is the optimization of ticket pricing strategies.

For the 1999 season, the Colorado Rockies were the first team in professional sports to implement variable ticket pricing (VTP) (Drayer et a l, 2012b; Rovell, 2002). By applying VTP prices are differentiated by factors such as opponent and day of the week (Drayer et al., 2012b). However, the prices are set long before the season starts and are not supposed to change du r­ ing the season. This feature was modified as the San Francisco Giants introduced dynamic ticket pricing (DTP) to the sports industry in 2009. Now ticket prices varied on a daily basis and on several situational fac­ tors— such as team performance, player performance, and even weather conditions (Drayer et al., 2012b). The most recent development concerning dynamic

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ticket pricing was achieved when this pricing approach reached Europe as two English football clubs— Cardiff City and Derby County— adopted it in the 2012-13 season (“Demand based pricing,” 2012; “Demand tick­ et pricing,” 2012). However, to date no German sports club has implemented a dynamic pricing system.

Nevertheless, further optimization of ticket pricing strategies seems promising, with the very existence of ticket brokers indicating that the pricing strategy of many sports clubs is suboptimal (Volpano, 2003). Ticket brokers, although they contribute little to the production of a sporting event, benefit from pricing inefficiencies in the primary market (Drayer, Stotlar, & Irwin, 2008; Happel & Jennings, 2002; Rascher, McEvoy, Nagel, & Brown, 2007). Happel and Jennings (2002) estimated that more than 1,000 recognized tick­ et brokers exist in the United States alone, and Stecklow (2006) assumed that 20-30% of all tickets are resold. However, the knowledge of the secondary m ar­ ket price provides valuable information for price set­ ting in the primary market (Drayer & Shapiro, 2009). Therefore, it seems plausible that an understanding of the determinants that influence ticket prices in the sec­ ondary market is essential for more efficiently pricing tickets in the primary market and for designing a dynamic pricing system for use in that market.

It appears that no study has ever investigated the fac­ tors that influence the fans’ willingness to pay for the German football Bundesliga. Thus, the current paper extends previous research on secondary market ticket prices, all of which focused exclusively on the American sport leagues NFL and MLB (Drayer & Shapiro, 2009; Drayer, Rascher, & McEvoy, 2012a; Shapiro & Drayer, 2012; Shapiro & Drayer, 2014). Furthermore, it seems that only the study of Drayer and Shapiro (2009) actually analyzed auctions in a reselling setting. Based on pricing theory, auctions can be regarded as much more suitable for determining willingness to pay than posted price offerings, because prices can fluctuate freely and not the seller but the buyer of the product determines the price (Lusk & Shogren, 2007; Miller et al., 2011; Volkner, 2006a, 2006b). However, the study of Drayer and Shapiro (2009) seems limited in terms of the number of obser­ vations and the number of analyzed determinants. The authors only analyzed a data set of 10 NFL playoff games that preceded the Super Bowl. Therefore, the current study adds to the literature by analyzing a data set that is almost ninefold compared to the study of Drayer and Shapiro (2009) and that extends the num ­ ber of observed games from 10 to 153. Additionally, several determinants that previously have not been analyzed in an auction-based setting are now being taken into account. Furthermore, the current study

seems to be the first to evaluate a European soccer league, which can be distinguished from the American sport leagues as the Bundesliga is an open league, which incorporates promotion and relegation. In addi­ tion, the current study addresses the research question by applying a two stage least squares (2SLS) regression. Drayer and Shapiro (2009) simply applied a standard ordinary least squares (OLS) regression, which does not take into account the simultaneity problem in microeconomics.

Theoretical Framework The current paper is based on the economic model of sports ticket demand developed by Swofford (1999), Spindler (2003), and Drayer et al. (2012a). The model adopts a neoclassical perspective. On this view the con­ sumer, who is subject to a budget constraint, is assumed to choose a consumption bundle that maxi­ mizes utility based on the customer’s preferences. Thus, the price has a negative effect on the demanded quantity (Andreff & Szymanski, 2009). Many studies on sport demand are in support of this theoretical assumption (e.g., Borland & MacDonald, 2003; Villar & Guerrero, 2009).

The Existence o f a Secondary Ticket Market Swofford (1999) discussed three reasons why second­ ary ticket markets might exist. First of all, the ticket broker in the secondary market might be less risk- averse than the producing firm in the primary market. The general concern of sports clubs is to sell out the stadium in order to enhance fan experience. Consequently, clubs set ticket prices below the optimal price. Another aspect is the selling of season tickets in order to yield a steady source of income prior to the season and to hedge against poor performances. Hence, sport clubs appear to trade off profit for cer­ tainty and, thereby, leave an opening for speculators who do not face these issues. Second, ticket resellers might have a lower cost-function due to lower infor­ mation costs, lower transactions costs, or lower taxes. In this case, then, ticket resellers act like any other middleman, because they can get better information about the consumer market or can offer the product to the consumer at a lower price than that of the original producer. Third, resellers might face a different rev­ enue function. Sports clubs are more interested in long-term relationships, whereas resellers can exploit the short-term revenue potential without worrying about their reputation. Therefore, sports clubs and ticket resellers are concerned with revenues over differ­ ent time frames. Furthermore, ticket resellers might be better at price discrimination than sports clubs and able to charge different prices depending on a variety

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of determinants. Another point is that sport clubs gen­ erate income not only from ticket sales but also from ancillary revenue streams such as merchandise, conces­ sions, and sponsors. Ticket resellers, however, optimize revenues based on a single product. Spindler (2003) extended the research of Swofford (1999) and devel­ oped a graphical representation, which was adapted and slightly adjusted by Drayer et al. (2012a). Figure 1 shows a combination of the illustrations of Spindler (2003) and Drayer et al. (2012a). The model assumes a fixed supply of tickets (the stadium capacity). Daon represents the demand if there is perfect price discrim­ ination (i.e., if each person pays exactly in accordance with her/his willingness to pay). This demand curve is often known as the all-or-none demand curve. If the sports club were able to price discriminate perfectly, it would make Q ^ P ^ in revenues. However, due to the club’s inability to price discriminate perfectly, it faces a lower demand curve. The demand curve D„ represents the average demand during the season for tne primary market in the case that there exists no secondary m ar­ ket. In order to sell-out the stadium, the sports club would chose the price Pj and would yield revenues of Ql*Pj. Following Spindler (2003) and Drayer et al.

(2012a), the existence of a secondary market would increase the demand for sports tickets and the sports club would face the demand curve DpS. The higher demand curve is mainly a result of two factors. First, ticket broker can enter the market, thus, creating addi­ tional demand in the primary market. Second, demand of fans increases as well, because the secondary market provides a form of insurance and mitigates the risk of losing money in the case that fans cannot attend the game (Rascher & Schwarz, 2012). As a result, the club would choose the price Pm in order to sell out the sta­ dium. Due to reasons detailed by Swofford (1999) (e.g., having better skills in price discrimination) the ticket broker faces an average demand curve D ^ , which lies between the demand curve of the sports club and the all-or-none demand curve.

Now two scenarios can be considered. In the first, the ticket broker sells tickets in the secondary market at a higher average price than the sports club in the primary market. Spindler (2003) provides the example of a tick­ et broker buying Q pQ m tickets from the sports club at a price of Pm , which he could then sell at a price P ^ and for a profit of (Q|-Qm )!('(Pt|3-Pm )-K, where K rep­ resents additional selling costs apart from the ticket

Price

Figure 1. Economic model o f sports ticket demand in the primary and secondary market

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purchase price Pm . In this scenario, both the club and the ticket broker benefit from the existence of a second­ ary market. The sports club faces the higher demand curve DpS and the ticket broker generates profits. In the second scenario, the primary seller uses the ticket bro­ ker as a discounter. Suppose the sports club is able to differentiate between the regular customers and ticket brokers. It charges a price of and sells a quantity of

tickets to regular customers. Then the club dumps the rest of the tickets (Qj-Qj-j) to the ticket broker at a price of Pj. Revenues of the sports club would account for (Qh*Ph)+ (Q rQ h)*f>h The ticket broker could resell the tickets at a price of P ^ . Hence, he could make prof­ its of (Qj-Qj1),<'(Pt|5-Pj)-K. Again in this scenario, both the sports club and the ticket broker would be better off with a secondary ticket market than without it (Spindler, 2003; Drayer et al„ 2012a).

The Principles of Price Discrimination Drayer et al. (2012a) applied the presented economic model in order to analyze determinants of ticket prices in the secondary market. The current paper follows the same approach in order to analyze ticket price deter­ minants in the secondary market for Bundesliga tick­ ets. These determinants can be applied by sports clubs to further differentiate their ticket prices and increase producer surplus. Pricing theory distinguishes between three degrees of price discrimination. First-degree or perfect price discrimination is when a firm is able to charge each consumer his or her individual willingness to pay. This is more a theoretical concept. However, well-designed auctions can approximate first-degree price discrimination (Rascher & Schwarz, 2012). Therefore, the evaluation of eBay auctions offers a great learning potential concerning the fans’ willing­ ness to pay. Second-degree price discrimination refers to the concept of offering different menus of consump­ tion options. Classical examples are volume discounts, multi-tiered pricing, bundled pricing, two-part tariffs, time, and quality discrimination. Examples of third- degree price discrimination include the offering of sen­ ior or student discounts. That means, discounts are granted for identifiable customer groups (Rascher & Schwarz, 2012).

Variable and dynamic ticket pricing— the focus of the current study—can be classified as forms of sec­ ond-degree price discrimination (Rascher & Schwarz, 2012) (e.g., games against opponents of higher quality results in a shift of the demand curve to the right). Consequently, a higher price can be charged (Rascher et al., 2007). Dynamic ticket pricing is essentially the extreme version of VTP (Rascher & Schwarz, 2012). Its objective is to capitalize on the shifts of demand and the fans’ willingness to pay by differentiating ticket

prices by multiple factors, like opponent, day and time of the game, or even weather conditions. Therefore, the objective of the current study is to examine which factors might be applied by Bundesliga clubs to further differentiate their ticket pricing structure.

Literature Overview The literature review is organized in four sections. First, the literature on sport demand in general is reviewed. Second, studies on ticket prices in the pri­ mary market are presented. Third, studies on the most recent development in sports ticket pricing, namely dynamic pricing, are illustrated. Fourth, studies on ticket prices in the secondary market are explored.

Sport Demand and the Role o f Pricing Sport demand, in terms of stadium attendance, has long been a much discussed topic among sport econo­ mists. Overviews are provided by Borland and MacDonald (2003), Downward and Dawson (2000), and Villar and Guerrero (2009). Ticket prices of sport­ ing events are generally negatively correlated with sta­ dium attendance. Furthermore, ticket prices are usually found to be in the inelastic portion of the demand curve (Andreff & Szymanski, 2009; Borland & MacDonald, 2003; Villar & Guerrero, 2009) as a conse­ quence of the ambiguous task for sports managers to maximize ticket revenues and attendance at the same time (Courty, 2003). In contrast to the vast am ount of studies on stadium attendance, the research on sports ticket prices in the primary and secondary market is limited.

Ticket Pricing in the Primary Market Studies concerning ticket pricing in the primary m ar­ ket were contributed by Reese and Mittelstaedt (2001), Rishe and Mondello (2003), and Rishe and Mondello (2004). While Reese and Mittelstaedt (2001) used a questionnaire format analyzing ticket price setting within the NFL, Rishe and Mondello (2003) analyzed a data set of the NFL for the seasons 1996-2001. Rishe and Mondello (2004) extended their 2003 study by analyzing NBA, NFL, NHL, and MLB ticket prices for a data set from 1996-2002. Generally, the price setting varied significantly from team to team and from league to league.

Dynamic Ticket Pricing in the Primary Market It seems only five papers have addressed the topic of dynamic pricing in the field of sport so far. Two papers discussed the topic on a general level and three publi­ cations analyzed a data set of ticket prices in order to identify determinants of the dynamic pricing system.

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An agenda for research and practice was developed by Drayer et al. (2012b). They discussed general issues from a management perspective concerning the speci­ fication of a dynamic ticket pricing system in the field of sport. Furthermore, they outlined potential areas for future studies and pointed out that more research is necessary in order to understand fans’ willingness to pay and the determinants that influence such willing­ ness. Nufer and Fischer (2013) analyzed the pricing strategies of European football leagues and came to the same conclusion— that dynamic pricing systems offer great revenue potentials for sport clubs.

Three empirical studies included Paul and Weinbach (2013) and Shapiro and Drayer (2012, 2014). Paul and Weinbach (2013) analyzed a data set of ticket prices for the 2011 season for the San Francisco Giants, St. Louis Cardinals, Chicago White Sox, and Houston Astros, the four American Major League Baseball baseball teams then using a dynamic ticket pricing system. However, results varied widely for the different teams. Shapiro and Drayer (2014) conducted another study about determi­ nants of dynamic ticket pricing systems based on an analysis of ticket prices for 12 selected San Francisco Giants games of the 2010 season. Shapiro and Drayer (2012) compared ticket prices of the San Francisco Giants of the 2010 season in the secondary market with ticket prices in the primary market and could show that dynamic pricing is not yet capable of tapping the cus­ tomer’s whole willingness to pay. Customers were will­ ing to pay nearly 50% more for tickets in the secondary market compared to the price of the dynamic pricing system. Therefore, it seems advisable to analyze ticket sales in the secondary market in order to identify and quantify the factors that influence the real customer’s willingness to pay for tickets of sporting events.

Ticket Pricing in the Secondary Market Despite the awareness of the lessons to be learned from the secondary market (Drayer et al., 2008), only four papers could be identified that used secondary market data in order to determine the factors that affect what fans are willing to pay for sports tickets. Drayer and Shapiro (2009) and Drayer et al. (2012a) analyzed data for the NFL, while Shapiro and Drayer (2012) and Shapiro and Drayer (2014) used data from the MLB in order to identify relevant determinants concerning the fan’s willingness to pay. However, only Drayer and Shapiro (2009) used data from ebay.com, which applies an auction format as well as a direct sales for­ mat. The other three studies used data from StubHub, where prices are posted. By comparison and based on pricing theory, auctions on secondary market websites such as eBay can be regarded as more suitable for determining the fans’ willingness to pay (Lusk &

Shogren, 2007; Miller et al., 2011; Volkner, 2006a, 2006b). The results of these studies will be presented in the following section while specifying the data model of the current study.

Model Specification

Econometric Specification In order to examine the factors that influence ticket prices in the secondary market, we apply a robust two stage least squares (2SLS) regression approach. Other studies on sport attendance and ticket pricing generally apply an ordinary least squares (OLS) approach, based on the assumption that supply is fixed. The secondary ticket market, however, can be regarded as a fluid m ar­ ket and supply as well as demand can change on a con­ tinual basis. The implication is the classic simultaneity problem in microeconomics (Greene, 2003). In order to resolve this issue, we first estimate the supply of sec­ ondary market tickets and use the forecasted quantity in a second stage in order to specify the regression of the transaction price. This is the standard use of 2SLS to solve simultaneity problems.

However, the validity of 2SLS relies on appropriate instrument variables in the first stage in order to esti­ mate supply. Drayer et al. (2012a), in a study about posted ticket prices in the secondary market, as well as Garcia and Rodriguez (2002), in a study about sport attendance, used stadium capacity as an instrument variable in order to forecast ticket quantity. It seems reasonable that the overall number of available tickets in the primary market should be correlated with the offered tickets in the secondary market, while at the same time the stadium capacity should only indirectly influence the ticket price in the secondary market. From an econometric point of view, these are the two requirements of an instrument variable (Greene, 2003). In the opinion of the authors, the instrument variable might be improved by using the actual stadi­ um attendance. This variable should be a better instru­ ment to predict the number of transactions in the secondary market because it offers a detailed differenti­ ation on game level, whereas the stadium capacity only represents a measure on team level. Nevertheless, both instrument variables were examined in order to find the best fit for the data. We specify the following two- equation system:

First Stage: Y\ = + P2 X2 + £i Quantity Regression (1)

Second Stage: Y2 — (*2 + P '3 ^ 1 + T l ^ l + £2 Price Regression (2)

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Y2 represents the secondary m arket ticket price, which is estimated in the second stage. Equation (1) is the first stage of the 2SLS regression model and repre­ sents the forecasted quantity that is an input into equa­ tion (2). Xj is a vector o f exogenous variables that affects both the quantity and the price regression. The vector X2 represents the instrum ent in the first stage. The vectors /?,(i = 1 ,2 ,3 ) are to be estimated. The variables oq and a.2 represent the intercepts of the models. The variables eq and 82 are error terms. Robust estim ation is used to control for any possible heteroscedasticity. In this respect, heteroscedasticity-

consistent standard errors are reported using W hite’s correction.

Variable Selection The variable selection of the econom etric m odel is based on the literature review on determ inants o f tick­ et prices in the prim ary m arket, sport ticket prices through dynamic pricing, and ticket prices in the sec­ ondary market. An evaluation of factors that influence stadium attendance com pleted the theoretical founda­ tion o f the model. Due to the specific situation o f the Bundesliga, and due to data availability, some variables which were previously used had to be adapted.

Table 1 Variable Description

Category Subcategory Variable Nam e D escription

eBay-related TICKET_PRICE_ The final selling price in € for one ticket o f an variables AUC eBay auction.

TRANS The total num ber o f transactions on ebay.de for each game.

N U M BER. The num ber o f sold tickets per auction. OF.TICKETS

BIDS The num ber o f bids that were m ade for a ticket in a specific auction.

Gam e-related O pponent DERBY A dichotom ous variable specifying whether variables

M atch DELTALP the m atch is of special interest. A continuous variable m easuring the level of

uncertainty competitiveness o f the game by the absolute difference in league positions.

Seasonal C H A M P . A categorical variable identifying whether the uncertainty DECISION cham pionship is already decided or not.

RELE. A categorical variable identifying w hether the DECISION teams to be relegated are already determ ined.

Tim e-related DAYSBEFORE The num ber o f days prior to the game that a variables

M A T C H . specific auction ended. The nu m b er o f games already played during

NUMBER the season plus 1. DAY. T IM E . The time slot of the game / day and tim e the

MATCH specific game was held.

Ticket-related TICKET.CAT A categorical variable specifying w hether the variables

SO LD .O U T

place w ithin the stadium was a standing or seated place. A dichotom ous variable, which specified whether a game was sold out or n o t at the time o f ticket purchase.

T IC K ET. The price o f the ticket in the prim ary market. FACE.VALUE This variable serves as a proxy for stadium

category and quality o f viewing.

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Table 1, continued Variable Description

C ateg o ry S ubcateg o ry V ariable N a m e D e sc rip tio n

T eam Season C U R _W IN _H O M E A c o n tin u o u s variable m easu rin g the p erfo rm an c e- p erfo rm an c e h o m e te a m ’s c u rre n t season w in n in g related variables percentage at th e tim e th e ticket was sold.

C U R _W IN _ A c o n tin u o u s variable m easu rin g th e away AWAY te a m ’s c u rre n t season w in n in g percentage at

th e tim e th e ticket was sold. C u rre n t form PO IN T S_ A c o n tin u o u s variable m easu rin g th e c u rre n t

o f th e co m p etin g 3GAMES_ com petitiveness o f th e h o m e team by the team s H O M E n u m b er o f obtained points in the last 3 matches.

P O IN T S _ A c o n tin u o u s variable m easu rin g th e c u rre n t 3GAMES_ com petitiveness o f th e aw ay team b y the

AWAY n u m b er o f obtained points in th e last 3 matches.

T eam quality LAGGED_ A c o n tin u o u s variable m easu rin g th e h o m e p rio r to th e season LP_H O M E te a m ’s league po sitio n at th e e n d o f the

previous season. LAGGED_ A c o n tin u o u s variable m easu rin g th e away LP_AWAY te a m ’s league p o sitio n at th e en d o f the

previous season. PRO M _TEA M _ A d ic h o to m o u s variable, w hich specified

H O M E w h e th e r th e h o m e team was p ro m o te d in the previous season.

PRO M _TEA M _ A d ic h o to m o u s variable, w hich specified AWAY w h e th e r th e aw ay te a m was p ro m o te d in th e

previous season.

E co n o m ic-related IN C O M E A c o n tin u o u s variable m easu rin g th e per variables

PO P capita incom e o f th e h o m e team (in th ousand). A c o n tin u o u s variable m easu rin g the p o p u la tio n o f th e h o m e team (in th o u sa n d ).

W e ath er T E M P _ M _ A c o n tin u o u s variable m easu rin g th e DAY te m p e ra tu re on th e day o f th e m atch.

PR EC IP_M _ A c o n tin u o u s variable m e asu rin g the DAY p re c ip ita tio n o n th e day o f th e m atch .

T E M P _ A c o n tin u o u s variable m easu rin g the E N D _A U C te m p e ra tu re o n th e day th e ticket was sold

o n eBay. PRECIP_ A c o n tin u o u s variable m easu rin g th e

E N D _A U C p re c ip ita tio n o n th e day th e ticket was sold o n eBay.

In s tru m e n t variables STA D IU M _ A c o n tin u o u s variable m easu rin g th e stad iu m CAPACITY capacity o f the h o m e team .

A TTEN - A c o n tin u o u s variable m easu rin g th e actual DANCE sta d iu m a tten d a n ce o n gam e level.

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Dependent Variable In the first stage of the 2SLS model the dependent vari­ able is the num ber o f transactions on ebay.de for each game (TRANS). As the dependent variable in the sec­ ond stage o f the 2SLS model, the final selling price in € for one ticket o f an eBay auction is used (TICKET_PRICE_AUC). M any auctions offered m ul­ tiple tickets. Hence, the num ber o f sold tickets per auc­ tion (NUMBER_OF_TICKETS) is used as a weighting variable.

Derivation o f Explanatory Variables The explanatory variables are grouped into eBay-relat­ ed variables, game-related variables, tim e-related vari­ ables, ticket-related variables, team perform ance-related variables, econom ic-related vari­ ables, and w eather-related variables. Table 1 provides a sum m ary of the explanatory variables as well as a short description o f these variables. eBay-related variables. N um ber o f transactions. The economic model o f sports ticket dem and suggests a negative relationship between quantity and price. Hence, the num ber o f transactions is included in the data m odel and a negative impact is expected. However, due to the sim ultaneity problem described above, the nu m b er o f transactions first ser­ ves as the dependent variable in the first stage of the 2SLS and then the predicted values are used as an explanatory variable in the second stage.

N um ber o f bids. The total num ber of bids is an aucti­ on-specific variable. It was only used in the study of Drayer and Shapiro (2009), who found that the secon­ dary m arket ticket price increased with the num ber of bids for that auction. Game-related variables. Team-specific effects. W hether a game is of special interest is a classic variable applied in studies about sport dem and. Shapiro and Drayer (2014) reported higher ticket prices in the secondary m arket when MLB teams played against an opponent o f the same division. Contrary, Drayer and Shapiro (2009) as well as Drayer et al. (2012a) found no significant effect of such a variable. Consequently, the current study inclu­ des a variable to specify w hether the game is a derby/rivalry game or not. Furtherm ore, Shapiro and Drayer (2014) included additional baseball-specific variables, like w hether a certain player was scheduled to pitch. However, these variables can be neglected due to the specific focus o f the current study. In European soccer the focus seems to be m ore on the team itself rather than on individual players like it may be the case in the American sports leagues MLB and NFL. Furtherm ore, Borland and M acDonald (2003) repor­

ted that a m ajority o f studies did not find a significant effect o f star players.

M atch uncertainty. The im pact of m atch uncertainty is a frequently discussed topic concerning stadium attendance (e.g., Borland & MacDonald, 2003; Villar & Guerrero, 2009). In the case o f secondary m arket ticket prices Shapiro and Drayer (2009) found no effect of m atch uncertainty, while Drayer et al. (2012a) reported that ticket prices increase for games with opponents that are expected to be of equal quality. These two stu ­ dies operationalized the concept of m atch uncertainty using betting odds. However, betting odds for games of the Bundesliga are only published prior to the game on quite short notice. Therefore, due to the focus o f the study on tracking ticket prices over time, we use diffe­ rences in league positions to m easure the level o f com ­ petitiveness. This m easure was also used, for example, by Garcia and Rodriguez (2002).

Seasonal uncertainty. Shapiro and Drayer (2014) reported a significant negative effect of season uncer­ tainty on the ticket price in the secondary m arket. As the Bundesliga incorporates prom otion and relegation, two variables concerning seasonal uncertainty are included in the data model: a categorical variable iden­ tifying whether the cham pionship is already decided or not and a categorical variable identifying whether the teams to be relegated are already determ ined. Studies on stadium attendance concerning soccer used similar operationalizations (e.g., Borland & M acDonald, 2003; Villar & G uerrero, 2009). Time-related variables. N um ber o f days prior to the gam e that a specific auc­ tion ended. Drayer and Shapiro (2009), Shapiro and Drayer (2012), as well as Shapiro and Drayer (2014) analyzed the effect o f tim e prior to the game. Drayer and Shapiro (2009) found that ticket prices in the secondary m arket decreased as the day o f the match approached. Shapiro and Drayer (2014) did not inclu­ de this variable in their final m odel concerning secon­ dary m arket ticket prices. Shapiro and Drayer (2012) reported that prices first increased and then decreased. However, the study o f Shapiro and Drayer (2012) seems limited, because the authors only evaluated four tim e points (20, 10, and 5 days prior to m atch day as well as the m atch day itself). The current study uses a continuous variable to analyze this effect.

N um ber o f the match in the season. Drayer and Shapiro (2009), Drayer et al. (2012a) as well as Shapiro and Drayer (2014) reported a positive effect o f the m atch num ber. That means, as the season progressed ticket prices increased in the secondary ticket market. The match num ber is also used in the current study to determ ine the part o f the season.

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Day and time o f the week. Drayer and Shapiro (2009) reported a significant effect of the day o f the week. Contrary, Drayer et al. (2012a) found no significant effect of the day o f the week. Similarly, Shapiro and Drayer (2014) found no effect concerning whether the game took place on a weekend day or during the week. Shapiro and Drayer (2014) seemed to be the only study that incorporated the tim e o f the match. The authors reported that games at 7 p.m. showed the highest ticket prices in the secondary market. The Bundesliga offers five prim ary tim e slots: Friday at 8:30 p.m ., Saturday at 3:30 p.m., Saturday at 6:30 p.m ., Sunday at 3:30 p.m., and Sunday at 5:30 p.m. Hence, the day and tim e of the m atch is specified using one categorical variable. Ticket-related variables. Standing vs. seated place. N either studies on dynamic ticket pricing nor studies on secondary market ticket prices have analyzed the im pact of whether a ticket corresponds to a standing or seating place. However, studies on stadium attendance suggest that this factor should be accounted for. Consequently, a dum m y variable is included in the current study.

Sold out status o f the stadium. Drayer et al. (2012a) reported a positive effect o f the sold stadium capacity on the ticket price in the secondary market. Shapiro and Drayer (2014) found no significant effect o f the sellout status. The current study includes a dum m y variable to evaluate this effect.

Original face value o f the ticket. The original face value o f the ticket is generally associated with a positive effect on the secondary m arket ticket price. This is su p ­ ported by Drayer and Shapiro (2009), Drayer et al. (2012a), Shapiro and Drayer (2012), and Shapiro and Drayer (2014). Consequently, this variable is included in the data m odel as well and serves as a proxy for sta­ dium category and quality o f viewing. Team performance-related variables. Current season performance. The season performance is measured by the current winning percentage o f the hom e and the away team. Drayer et al. (2012a) as well as Drayer and Shapiro (2009) found a positive effect of the hom e team ’s winning percentage on the ticket price in the secondary market. Shapiro and Drayer (2014) only reported a positive effect of the away team ’s win­ ning percentage on the ticket price in the secondary market. In the study of Drayer et al. (2012a) the away team ’s winning percentage was not significant.

Current form o f the competing teams. The current form o f the com peting teams is operationalized by the num ber of points in the last three m atches of the hom e and away team prior to the game. In the study of Shapiro and Drayer (2014) neither the current form of the hom e team nor the current form of the away team showed significant effects. Drayer et al. (2012a) repor­

ted a positive effect of the current form o f the hom e team m easured by a variable called power rank (a covers.com ranking that incorporated factors such as strength of schedule, recent perform ance, and team - based offensive and defense statistics).

Team quality prior to the season. Drayer and Shapiro (2009) as well as Drayer et al. (2012a) reported a posi­ tive effect o f the lagged hom e team ’s winning percenta­ ge on the ticket price in the secondary market. A significant effect of the lagged w inning percentage of the away team was only found by Drayer et al. (2012a). The current study uses the league position o f the hom e and away team at the end o f last season as a proxy for team quality prior to the season. R ottm ann and Seitz (2008) applied the same variables to predict attendance figures for the Bundesliga and found significant effects of the lagged league position o f the hom e and away team. Furtherm ore, as the Bundesliga is an open lea­ gue, R ottm ann and Seitz (2008) evaluated the impact o f prom oted teams and found higher attendance figu­ res for prom oted hom e teams b u t not for prom oted away teams o f the Bundesliga. Therefore, two dum m y variables are included to specify whether the hom e and away team was prom oted at the end of last season. Economic-related variables of the home team. Drayer and Shapiro (2009) reported a positive effect of the hom e team ’s per capita income and a negative effect o f m arket size. Contrary, in the study o f Drayer et al. (2012a) neither o f these two variables was rep o rt­ ed to be significant. In order to evaluate the im pact of these to variables, the hom e team ’s per capita income and the population of the hom e team are included in the current study. Weather-related variables. Based on stadium attendance studies, good weather conditions generally are associated with higher stadium attendance rates (Villar & Guerrero, 2009). However, the study o f Shapiro and Drayer (2014) reported no effect of forecasted tem peratures. O ther studies on sec­ ondary m arket ticket prices did not include weather variables in their study design. Only Paul and W einbach (2013) found mixed effects concerning weather conditions on the dynamic ticket price in the prim ary market. Hence, the current study incorporates the tem perature on the day of the game as well as pre­ cipitation on the day of the game. Due to data avail­ ability and for greater accuracy, we use actual weather data for the day o f the m atch rather than forecasted data. Furtherm ore, the current study also analyzes the effect o f the tem perature and precipitation at the end of the auction. These variables have not been taken into account thus far. Hence, the existing literature is extended in this respect.

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Data Collection The data set in the current study covers the whole sec­ ond half o f the Bundesliga season 2013-14. Every day the words Fussball Bundesliga Tickets, followed by the names of the com peting teams, were used to search for tickets on ebay.de. Based on the transaction code, all relevant auctions could be tracked and the final selling price could be evaluated. Starting date of the data col­ lection was January 1, 2014, because the first game of the second half of the season took place on January 24, 2014. End of the data collection period was May 10, 2014, which was the last m atch day o f the season. This period of tim e was chosen because it offered a broad variety o f games. Unfortunately, ebay.de was not will­ ing to provide researchers with the necessary inform a­ tion, so data had to be collected manually. No VIP tickets were taken into account because those tickets included additional services like food, beverages, and parking. Tickets for selected custom er groups such as students, pensioners, and disabled people were also not considered due to the reduced price and the possibility for upgrading tickets on m atch day in the stadium. The final data set accounted in total for 6,510 eBay auctions in which 11,637 tickets were sold. M any auctions offered the sale of m ore than one ticket. In this case the final price was divided by the num ber of tickets sold to calculate the price per ticket.

Apart from data that were directly collected on ebay.de, several other sources were used to build up an extensive database. Data concerning the date and time of each m atch were gathered from the official hom e- page of the first German Bundesliga (http://w w w .bun- desliga.de). The outcom e o f the games and data concerning league positions were tracked on http://www.ergebnisselive.com. The original face value o f each individual ticket was collected from the hom e- page o f the respective football team. Relevant inform a­ tion about derby/rival games were taken from http://w w w .derbysieg.com /derby-term ine. Attendance data was gathered from the hom epage http://fussball- daten.sport.de/bundesliga/2014. Data concerning p o p ­ ulation and income per capita were taken from MB-Research (2013). W eather data were collected from the G erm an W eather Service (http://w w w .dw d.de).

Results The results are illustrated in two steps. First, the descriptive results are presented. Second, the results of the regression m odel are shown.

Descriptive Results The descriptive results for the continuous variables are illustrated in Table 2.

Table 2 Descriptive Results for the Continuous Variables

Variable N Min Max Mean SD

TICKET_PRICE_AUC in € 6510 0.75 633.50 69.41 50.15 NUMBER_OF_TICKETS 6510 1 6 1.79 0.61 TRANS 6510 1 323 123.81 92.55 BIDS 6510 1 89 17.46 9.75 DELTALP 6510 1 17 7.58 4.54 DAYSBEFORE 6510 0 93 9.25 8.08 MATCH_NUMBER 6510 18 34 28.09 4.60 TICKET_FACEV 6510 10 94 34.73 17.45 CUR_W IN_HOM E in % 6510 20.0 95.06 52.44 23.74 CUR_WIN_AWAY in % 6510 20.0 95.06 52.34 19.47 POINTS_3GAMES_HOME 6510 0 9 4.68 2.76 POINTS_3GAMES_AWAY 6510 0 9 4.71 2.54 LP_HOME_LS 6510 1 16 8.19 5.72 LP_AWAY_LS 6510 1 16 7.83 5.35 INCOM E (thousand) 6510 17.9 28.3 23.14 3.19 POP (thousand) 6510 34.8 3,330.4 839.24 748.25 TEM P_CURRENT in °C 6510 -10.3 16.5 9.18 3.58 PRECIP_CURRENT in m m 6510 0.0 20.9 1.52 2.80 TEM P_ M_DAY in °C 6510 -11.3 18.9 8.44 4.11 PRECIP_M_DAY in mm 6510 0.0 24.8 1.08 2.48 STADIUM_CAPACITY (thousand) 6510 23.4 80.7 55.08 17.86 ATTENDANCE (thousand) 6510 22.0 80.6 53.96 18.04

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The observed prices for tickets of Bundesliga football games on ebay.de varied widely and ranged between €0.75 and €533.50 with an average selling price of €59.40. The average face value amounted to €34.73. That means the average fan participating in an online auction is willing to pay roughly twice as much as the current face value. Also the number of auctions per match differed significantly. Depending on the sched­ uled teams, between 1 and 323 auctions per match could be tracked.

The descriptive results concerning the categorical variables are presented in Table 3. The primary time slot for games of the Bundesliga is Saturday at 3:30 p.m., which is also reflected by the descriptive statis­ tics. The percentage of ticket sales for games on Saturday at 3:30 p.m. am ounted to 70.6% of all trans­ actions. Auctions for derby games accounted for 11.2% and roughly half of the transactions occurred when the championship was already decided. The variable that identified whether the teams to be relegated were already determined showed no variation, because in the season 2013-14 this decision took place on the last match day. Consequently, this variable was not consid­ ered in the regression model.

Regression Model The results of the regression model are reported in Tables 4 and 5. Table 4 shows the results of the first stage and Table 5 provides the results of the second stage of the 2SLS regression. To account for any het- eroskedasticity issue, robust standard errors (HCSE)

Table 3 Descriptive Results for the Categorical Variables

are reported using White’s correction (White, 1982). A Wu-Hausman test was applied to test for endogeneity. The significant test results, F(l, 6480) = 25.08, p < .001), support the application of a 2SLS regression. A test for weak instruments showed significant results, F(l, 6481) = 737.78, p < .001), suggesting a strong instrument variable.

In the final model attendance at game level was used as an instrument variable in the first stage, because this variable showed a strong significant effect and provid­ ed a better fit than stadium capacity. Furthermore, the weak instrument test for the model with attendance showed a higher F-statistic compared to the model with stadium capacity. However, using stadium capaci­ ty as an instrument variable did not change the general results in the second stage. Multicollinearity was no issue in the first stage. All variance inflator factors (VIF) were below 10, which is the most commonly rec­ ommended maximum level of the VIF (e.g., Hair, Anderson, Tatham, & Black, 1995; Kennedy, 1992; Neter, Wasserman, 8c Kutner, 1989; O ’Brian, 2007). In the second stage the predicted values of the number of transactions showed a VIF above 10. This is not sur­ prising because the number of transactions was pre­ dicted by all exogenous variables and the instrument variable in the first stage. However, the large sample size disintegrates any large bias effect on standard errors. Consequently, a lower VIF would only strengthen the already existing significant effect of the number of transactions in the second stage. By apply­ ing a log-transformation to the number of transactions

Variable Categories N Percentage in %

DERBY Yes 732 11.2 No 5778 88.8

CHAMP_ DECISION Yes 3265 50.2 No 3245 49.8

REFE_DECISION No 6510 100.0 DAY_TIME_MATCH Saturday 3:30 p.m. 4599 70.6

Saturday 6:30 p.m. 804 12.4 Sunday 3:30 p.m. 331 5.1 Sunday 5:30 p.m. 501 7.7 Friday 8:30 p.m. 275 4.2

TICKET_CAT Standing 1967 30.2 Seating 4543 69.8

SOFD_OUT Yes 4701 72.2 No 1809 27.8

PROM_TEAM_ HOME Yes 886 13.6 No 5624 86.4

PROM_TEAM_ AWAY Yes 398 6.1 No 6112 93.9

152 Volume 24 • Number 3 • 2015 • Sport Marketing Quarterly

Table 4 2SLS Regression Results - First Stage - and HCSE

Variable Beta HCSE f-statistic p-value Sig.

C onstant -589.74 14.075 -41.900 < .001 ***

BIDS -0.039 0.068 -0.580 0.562 DERBY 17.723 2.618 6.769 < .001 *** DELTALP 2.892 0.214 13.485 < .001 *** CHAM P_DECISION -7.430 2.611 -2.846 0.004 ** DAYSBEFORE 0.652 0.082 7.998 < .001 *** MATCH_NUMBER 4.202 0.377 11.135 < .001 *** DAY_TIME_MATCH Sat 3:30pm vs. Sat 6:30 p.m. 21.361 3.181 6.716 < .001 *** Sat 3:30pm vs. Sun 3:30 p.m. 24.983 4.200 5.948 < .001 *** Sat 3:30pm vs. Sun 5:30 p.m. 40.827 3.734 10.934 < .001 *** Sat 3:30pm vs. Fri 8:30 p.m. -10.951 2.396 -0.046 0.964 TICKET_CAT (Seating) -9.506 2.046 -4.647 < .001 *** SOLD_OUT 40.520 2.385 16.991 < .001 *** TICKET_FACE_VALUE 0.155 0.060 2.575 0.010 * CUR_W IN_HOM E 0.938 0.067 14.000 < .001 *** CUR_WIN_AWAY 0.700 0.061 11.423 < .001 *** POINTS_3GAMES_HOME -3.191 0.319 -10.004 < .001 *** POINTS_3GAMES_AWAY 2.207 0.271 8.141 < .001 *** LP_HOME_LS 6.247 0.281 22.248 < .001 *** LP_AWAY_LS 1.622 0.244 6.6550 < .001 *** PROM_TEAM_ HOM E -4.192 3.320 -1.263 0.207 PROM_TEAM_ AWAY -15.597 3.284 -4.749 < .001 *** INCOM E (thousand) 9.533 0.345 27.657 < .001 *** POP (thousand) 0.015 0.002 7.211 < .001 *** TEM P_ M_DAY 5.497 0.327 16.792 < .001 *** PRECIP_M_DAY -0.247 0.313 -0.789 0.430 T EM P_END_AU C -0.209 0.228 -0.919 0.358 PRECIP_ END_AUC -0.045 0.269 -0.169 0.866 ATTENDANCE (thousand) 2.024 0.083 24.458 < .001 ***

DF 6481 R2 0.734 R2 adj 0.733 F-statistic 962.53***

. Significance: 10% level * Significance: 5% level ** Significance: 1% level *** Significance: 0.1% level

in the first stage the issue of multicollinearity vanished completely and all VIF in the first and second stage were below 10. The log-transform ation did not change the overall results in the second stage.

The second stage o f the 2SLS regression model explained 59.6% o f the variance in the data set and m ost o f the variables showed significant effects on a 0.1% level. The num ber of points in the last three

matches of the away team and the tem perature at the end of the auction were significant on a 1% level. The num ber of transactions and whether the game was scheduled on Sunday at 5:30 p.m. com pared to the ref­ erence category Saturday at 3:30 p.m. showed signifi­ cant effects on a 5% level. The league position of the hom e team in the last season was only significant on a 10% level. No significant effects were observed for the

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Table 5 2SLS Regression Results - Second Stage - and HCSE

Variable Beta HCSE f-statistic p-value Sig.

Constant -160.28 10.756 -14.902 < .001 *** TRANS -0.041 0.019 -2.195 0.028 * BIDS 1.061 0.059 17.972 < .001 *** DERBY 12.411 1.344 9.236 < .001 *** DELTALP -0.700 0.148 -4.734 < .001 *** CHAMP_DECISION -6.917 1.867 -3.706 < .001 *** DAYSBEFORE 0.537 0.075 7.141 < .001 *** MATCH_NUMBER 2.404 0.258 9.333 < .001 *** DAY_TIME_M ATCH Sat 3:30pm vs. Sat 6:30 p.m. 11.799 2.055 5.742 < .001 *** Sat 3:30pm vs. Sun 3:30 p.m. 10.893 1.563 6.968 < .001 ** * Sat 3:30pm vs. Sun 5:30 p.m. 1.171 1.424 0.823 0.411 Sat 3:30pm vs. Fri 8:30 p.m. -3.341 1.554 -2.150 0.032 * TICKET_CAT (Seating) -17.261 1.552 -11.119 < .001 *** SOLD_OUT 18.102 1.408 12.854 < .001 *** TICKET_FACE_VALUE 1.063 0.059 17.894 < .001 *** CUR_WIN_HOME 0.756 0.041 18.313 < .001 *** CUR_WIN_AWAY 0.217 0.036 6.079 < .001 *** POINTS_3GAMES_HOME -0.233 0.226 -1.031 0.302 POINTS_3GAMES_AWAY 0.685 0.213 3.218 0.001 ** LP_HOME_LS -0.257 0.140 -1.834 0.067 LP_AWAY_LS -0.836 0.129 -6.486 < .001 *** PROM_TEAM_ HOME 28.677 2.071 13.848 < .001 *** PROM_TEAM_ AWAY 6.003 1.354 4.435 < .001 *** INCOME (thousand) 3.257 0.203 16.080 < .001 *** POP (thousand) -0.009 0.001 -8.972 < .001 *** TEMP_ M_DAY -0.712 0.203 -3.511 < .001 *** PRECIP_M_DAY -1.517 0.146 -10.372 < .001 *** TEMP_END_AUC 0.438 0.147 2.969 0.003 ** PRECIP_ END_AUC 0.0271 0.183 0.149 0.882

DF R2 R2 adj F-statistic

6481 0.598 0.596

349.20***

. Significance: 10% level * Significance: 5% level ** Significance: 1% level *** Significance: 0.1% level

number of points in the last three matches of the home team and for the precipitation at the end of the auction.

All variables—except for the temperature on match day— showed the expected signs. Tickets for more suc­ cessful teams were prices higher. The current winning percentage of the home and away team, as well as the number of points in the last three matches of the away team showed a positive impact. The coefficients for the

league position of the home and away team at the end of the previous season are both negative. This means that a more successful team in the previous season caused higher secondary market ticket prices in the current season. Tickets for a sold-out game were trad­ ed with a mark-up of €18.10 and tickets for derby games were valued with a mark-up of €12.41. The vari­ able match number had a positive effect, which implies

154 Volume 24 • Number 3 • 2015 • Sport Marketing Quarterly

that as the season proceeded, ticket prices increased each match day by €2.40. The temperature on match day showed a negative effect. This was surprising because generally good weather conditions are associ­ ated with higher ticket prices. The precipitation on the day of the match as well as the temperature at the end of the auction, however, showed the expected signs. The variables concerning match and seasonal uncer­ tainty showed the expected signs, too. If the difference in league positions of the competing teams was reduced by 1, ticket prices increased by €0.70 and tick­ ets for games after the championship was decided were sold with a mark-down of €5.92. Also the general microeconomic relationship held true— if the number of eBay auctions increased by 1 the average ticket price decreased by€D.04.

Discussion The second stage of the 2SLS regression explained 59.6% of the variance in the data set. Drayer and Shapiro (2009) reported a R 2 of 63.6%, Drayer et al. (2012a) 56.0%, and Shapiro and Drayer (2014) 79.7%. Hence, the explained variance of the data model is comparable to previous studies about determinants of ticket prices in the secondary market. Drayer and Shapiro (2009) published the only study so far that used eBay data, analyzing 750 auctions. In comparison, the number of observations in the current study, which analyzed a data set of 6,510 eBay auctions, was almost ninefold. From an econometric point of view, however, the study of Drayer and Shapiro (2009) can be criti­ cized, because it applied a standard OLS approach, whereas the current study made use of a 2SLS regres­ sion in order to address the simultaneity problem in microeconomics. The determinants by which sports clubs could further differentiate their ticket prices are discussed in the following.

Both eBay-related variables showed significant effects. As expected, the number of eBay auctions per game had a negative impact on ticket prices. Interestingly, Drayer and Shapiro (2009) as well as Shapiro and Drayer (2014) found a positive effect of the number of available tickets on the secondary mar­ ket ticket price. Both studies argued that as ticket hold­ ers realize that fans are willing to pay high prices for tickets in the secondary market, they become more likely to sell their own tickets. However, while this is more a supply argument, the result of the current study seems more plausible as it corresponds to the general relationship between quantity and price in the economic model of sports ticket demand. The second eBay-related variable showed a positive impact. If the number of bids increased by 1, the ticket price increased by €1.06. Drayer and Shapiro (2009) report­

ed similar results in which the ticket price increased by $1.65 with each additional bid.

Game-related variables included the variables whether the game was a derby game or not, match uncertainty, and seasonal uncertainty. Derby games had a positive effect on the ticket price. A similar result was reported by Shapiro and Drayer (2014). Games of MLB teams playing against an opponent of the same division were traded at a higher price. However, in the studies of Drayer and Shapiro (2009) and Drayer et al. (2012a) the variable division was not significant. Both uncertainty-related variables, the difference in league position of the competing teams and whether the championship was already decided or not, had a nega­ tive effect on the ticket price in the secondary market. While Shapiro and Drayer (2009) found no effect of match uncertainty, Drayer et al. (2012a) reported a sig­ nificant negative effect, as is the case in the current study. That implies, the closer fans expect the game to be, the higher the price fans are willing to pay. Similar to the study at hand, a significant negative effect of sea­ son uncertainty was found by Shapiro and Drayer (2014). As relevant decisions in the season are already made, ticket prices dropped significantly.

Time related variables included the number of days prior to match day, match number, and day and time of the match. As the day of the match approached the ticket price decreased continuously. Similar effects were found by Drayer and Shapiro (2009) and Shapiro and Drayer (2012). Shapiro and Drayer (2014) did not include this variable in their final model. Drayer and Shapiro (2009) reported an effect of $4.89 per day compared to ’80.54 per day in the current study. The study of Shapiro and Drayer (2012) can be generalized, because the authors evaluated only four time points (20, 10, and 5 days prior to match day as well as the match day itself). In their study, ticket prices increased from 20 days prior to the game to five days before the game and dropped on game day. Dwyer, Drayer, and Shapiro (2013) analyzed the impact of time in advanced ticket purchase decisions based on an online questionnaire. The authors found that as time before the event decreased the consumer expected a lower ticket price. This result corresponds to the finding of the current study. The variable match number had a positive effect. Drayer and Shapiro (2009), Drayer et al. (2012a), and Shapiro and Drayer (2014) reported simi­ lar findings. Drayer and Shapiro (2009) found that the greatest price inefficiency occurred with regard to the round of the playoffs, and that teams are leaving sub­ stantial revenues on the table by inadequately raising prices between each round of the playoffs. This finding is also supported by the current study. On each succes­ sive match day customers were willing to pay an

Volume 24 • Number 3 • 2015 • Sport Marketing Quarterly 155

increase of €2.40. Significant effects were also found for the combined variable day and time of the day. Drayer and Shapiro (2009) reported a significant effect of the day of the week, while Shapiro and Drayer (2014) found a significant effect of the time of the day. Whether the game took place on a weekend day or during the week showed no significant effect in the previously mentioned study. Drayer et al. (2012a) ana­ lyzed the impact of the day of the week and found no significant effect. Based on the results of the current study, the most valuable time slots for games of the Bundesliga are Saturday at 6:30 p.m. and Sunday at 3:30 p.m., which showed significant mark-ups com­ pared to games on Saturday at 3:30 p.m.

Ticket-related variables included whether a standing or seating place was sold, whether the game was sold out at the time of observation, and the original face value of the ticket. The willingness to pay for standing places was €17.26 higher than for seated places. This is not surprising, because the original face value for tick­ ets in the stands is quite low. This result implies that it seems more important for fans to attend a game rather than to savor high quality seats. Other studies on ticket prices in the secondary market or on dynamic pricing in the primary market did not analyze this variable. Hence, the current study does extend the literature in this respect. Tickets for sold out games were traded with a mark-up of €18.10. Although Shapiro and Drayer (2014) found no significant effect of the sellout status, Drayer et al. (2012a) reported a positive effect of the sold stadium capacity. Thus, the results of the regression model confirmed the expectations. The original face value of the ticket had a positive effect on the ticket price in the secondary market. This result is consistent with the other four studies in the field of ticket prices in the secondary market, all of which found a positive effect.

All performance-related variables support the con­ clusion that fans want to see a successful team. The current season winning percentage of the home and away team, and the number of points of the away team in the last three matches were found to have a positive impact on ticket prices in the secondary market. The league position of the home and away team at the end of last season showed a negative effect. That means, tickets for better teams in the last season were priced higher. These results are consistent with results from previous studies. Drayer et al. (2012a) as well as Drayer and Shapiro (2009) reported a positive effect of the home team’s current winning percentage, and Shapiro and Drayer (2014) reported a positive effect of the away team’s current winning percentage. Drayer et al. (2012a) found no significant effect of the away team’s winning percentage. Concerning the current form of

the competing team, Drayer et al. (2012a) found a pos­ itive effect for the home team, whereas Shapiro and Drayer (2014) reported no significant effect. A positive effect of the lagged home team’s winning percentage was reported by Drayer and Shapiro (2009) as well as Drayer et al. (2012a), while a significant effect of the lagged winning percentage of the away team was only found by Drayer et al. (2012a). These results also cor­ respond to the findings of Rottmann and Seitz (2008), which analyzed attendance figures of the Bundesliga. Since the current study is the first to evaluate ticket price determinants for an open league, the variables whether the home or away team was promoted at the end of last season could be evaluated as well. Both vari­ ables showed a positive effect on ticket prices in the secondary market. Therefore, it seems that fans of pro­ moted teams are willing to pay significant higher ticket prices in order to see the new club of the Bundesliga. Similarly, Rottmann and Seitz (2008) reported higher attendance figures when the home team was promoted at the end of last season.

The included economic-related variables showed the expected signs as well. Like in the study of Drayer and Shapiro (2009) the home team’s per capita income showed a positive effect and market size showed a neg­ ative impact on ticket prices in the secondary market. These findings are also consistent with studies about stadium attendance. It seems plausible that a higher income level provokes higher ticket prices. The nega­ tive effect of market size can be interpreted in the way that a smaller market provides less competitive offer­ ings for leisure activities, resulting in higher ticket prices.

Significant weather-related variables included the temperature on the day of the game, precipitation on the day of the game, and temperature at the end of the auction. Temperature on the day of the game was used as a proxy for the forecasted temperature and showed a negative effect. Only Shapiro and Drayer (2014) took forecasted temperature into account but reported no significant effect. However, generally good weather conditions are associated with higher stadium atten­ dance (Villar & Guerrero, 2009). Hence, it appears that it would have been more likely to find a positive effect in this case. Precipitation on the day of the game showed the expected negative effect. Yet, Shapiro and Drayer (2014) found no significant effect of the vari­ able forecasted precipitation. A variable that has not been evaluated in similar studies is the temperature at the end of the auction. Higher temperatures on the day the ticket was sold on eBay increased the selling price. Therefore, the current study does extend the determi­ nants of ticket prices in the secondary market in this respect, too.

156 Volume 24 • Number 3 • 2015 • Sport Marketing Quarterly

The identified determinants can be applied by sports clubs of the Bundesliga to further differentiate their current variable ticket pricing strategy or to implement a dynamic pricing approach. As a consequence, the sports club would be able to capitalize on the fans’ willingness to pay, which varies from game to game. In other words, the sports club could take advantage of the different demand functions of games with different characteristics and, thus, would face a higher average demand curve over the whole season. The former demand curve DpS would shift upwards in the direc­ tion of the demand curve of the ticket broker D ^ . The am ount of the upward shift, however, would depend on the level of further price discrimination. Therefore, by applying a more detailed pricing approach the sports club would reduce the consumer surplus and gain additional producer surplus. The ticket broker, on the other hand, would lose producer surplus. Consequently, the resale activity of ticket brokers should be expected to decline because their competi­ tive advantage, like better skills at price discrimination (Swofford, 1999), would be diminished.

In summary, the conducted analysis lays the founda­ tion for sports clubs of the Bundesliga to increase their ticket revenues and, therefore, to cope with increasing financial obligations like rising player salaries, transfer fees, or stadium maintenance costs (Drayer et al., 2012).

Conclusion and Limitations The present study is the first to examine by means of analyzing auctions on ebay.de the determinants of fan’s willingness to pay for football tickets of the Bundesliga. This study thus extends previous research by Drayer and Shapiro (2009), Drayer et al. (2012a), Drayer and Shapiro (2012), and Shapiro and Drayer (2014), which investigated ticket sales in the secondary market of the American sports leagues NFL and MLB. However, only the first of these four studies used actu­ al auctions from ebay.com, whereas the other three used posted price data from stubhub.com. The auction based format of eBay can be regarded as a data source offering a much higher level of validity concerning the evaluating of the fans’ willingness to pay than stub- hub.com, which uses a posted price format.

Ticket prices in the secondary market were found to be roughly twice as much as the actual face value of the ticket. Interestingly, Drayer and Shapiro (2009) report­ ed the same ratio in their study about ticket prices on eBay for NFL playoff games. Hence, the current study draws the same conclusion as Drayer and Shapiro (2009)— that tickets in the primary market are clearly priced inefficiently. This study found 23 determinants to significantly influence the ticket price in the second­ ary market for tickets of the Bundesliga. Although

overall results of the current study are comparable to those of the four studies previously published about ticket prices in the secondary market, the current study adds several previously unanalyzed determinants to the literature.

The identified determinants would be applicable if Bundesliga football clubs chose to further differentiate their ticket prices or decided to develop a more sophis­ ticated dynamic pricing approach. Although major sports clubs in Europe may be more resistant to inno­ vative pricing methods than teams of American sports leagues (Nufer & Fischer, 2013), increasing financial obligations (Bundesliga Report 2014) might force the football clubs of the Bundesliga to reconsider their cur­ rent pricing strategies. As the present study shows, fans are willing to pay prices for football tickets via online auction that are much higher than the current face value. Therefore, if the football clubs want to benefit from at least a part of fans’ willingness to pay higher prices, adopting a dynamic pricing approach could yield a substantial revenue increase.

Due to its innovative approach of analyzing eBay auctions, the current study is not free from limitations. First, future studies should repeat the current study in order to test for reliability. Because of the phenome­ non of ticket sales in an online market being relatively new, there should be investigation regarding the ques­ tion of whether and how significant determinants change over time. Second, this study covered only games of the second half the 2013-14 Bundesliga sea­ son. Although this period of time was sufficient for the purpose of the current study, the factors that influence the fan’s willingness to pay might vary compared to the first half of the season. Therefore, a future study should extend the data collection period. Furthermore, roughly 20% of the transactions in the data set could be assigned to home games of Bayern Munich. This might have led to a bias of the results. Third, as this study only covered ticket sales for football games, a future study should investigate other sports leagues. Fourth, the perceived value of tickets might change due to cultural differences. Therefore, the approach of this study should also be applied in other countries. In addition to the limitations mentioned above, it has to be pointed out that the current study only included auctions on ebay.de. Other popular secondary market websites like viagogo.de might reveal different effects. However, it has to be noted that viagogo.de applies a posted price format compared to an auction format of ebay.de. Hence, future studies could also examine the determinants that influence ticket prices in the second­ ary market for different distribution channels.

Volume 24 • Number 3 • 2015 • Sport Marketing Quarterly 157

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