Tennis superstars: The relationship between star status and demand for tickets

Highlights

•	Players considerably influenced demand for attendance at the Australian Tennis Open.
•	Evident individual-player causal effects on ticket sales.
•	Performance ratings are imprecise representatives of the pulling power of players.
•	Star status can be used to inform business decisions and drive commercial outcomes.

Abstract

Akin to other sports, professional tennis is urged to adopt a consumer-centred strategy and understand the influence of the star status of elite players on demand for its core product. Measuring the impact that tennis players have on demand for match attendance remains a key element towards achieving that goal. Using data from the Australian Open ticket sales, the authors demonstrate how individual players have influenced stadium attendance at the Grand Slam. Findings indicate that some players are associated with a strong positive impact on demand for tickets, above and beyond their performance ratings, reflecting their value to the Australian Open. The authors discuss how this star status can be used to inform business decisions related to tournament management, match scheduling, and determining player appearance fees, to ultimately drive better commercial outcomes and deliver a world-class sporting event. The findings have implications for tournament organisers, player managers and those that market player activities.

Keywords:

• Demand for attendance
• Ticket sales
• Player effect
• Tennis superstars
• Australian open

Notes

1 This is not a comprehensive examination of Rosen (1981)’s versus Adler (1985)’s theory in the sport of tennis since the players’ income distributions (and market values) over different periods of time are not available.

2 The women’s singles data was not considered in this paper due to the limited sample size available for analysis. Moreover, men’s singles matches show higher overall attendance figures compared to women’s.

3 Technically this model uses an inverse probability weighting to balance covariates (and not matching). For simplicity, we will refer to this model as the PSM throughout this paper.

4 The presented models will become more accurate as subsequent Australian Open ticketing data is introduced in future analysis, thus minimising the standard errors for some players’ estimates.

5 We note that the price variable can be endogenous since price is influenced by demand, which is also influenced by price. Endogeneity can occur when correlation exists between the mean price and the error term in our model. We make a simplification in this paper by using the average ticket price for admission to a session. Ideally, we would correct for price endogeneity by performing a 2SLS that accounts for endogeneity. However, with the lack of access to historical sales data and/or data from comparable markets (where the events are relatively similar in characteristics to the Australian Open), we have no clear method to validate the instrument variables.