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Dynamic brand evolution mechanism of professional sports teams: empirical analysis using comprehensive major league baseball data

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Abstract

In this study, we empirically examine the dynamic development mechanism of team brand equity by proposing a set of team brand equity development model and team brand demand models using comprehensive Major League Baseball data set. Taking advantage of the richness of the game-level data, we provide an alternative estimation approach to accurately measure team brand equity and identify its dynamic evolution process. We find various patterns to the brand equity development across teams over time. More importantly, we find a strong carry-over effect: team brand equity is 3.5–4.5 times larger when we consider dynamic impact, which implied serious underestimation in a static case. Also, we find that postseason performance is a vital element to construct strong team brand equity while seasonal performance has tenuous impact on the brand equity development. Furthermore, we find that a team’s brand community plays a critical role in developing its brand equity. A team that maintains 2–3 times larger brand community benefits from developing team brand equity as much as it achieves successful postseason performances (e.g., winning World Series). However, we find insignificant evidence of a star player to develop team brand equity. Also, we conduct several simulation analyses to provide meaningful managerial implications.

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Notes

  1. Source: Retrosheet (https://www.retrosheet.org/).

  2. Tuchman (2015, Forbes), “Why Sports Teams Still Selling Season Tickets Are Doomed”.

  3. Professional sports industry is considered the biggest industry in the entertainment business, and the team owners make investments of gigantic size every year. According to the USTODAY (2009) reports, the average annual payroll of a team in the Major League Baseball (MLB), the National Basketball Association (NBA), and the National Football League (NFL) was $88 million, $108 million, and $76 million, respectively.

  4. Box office revenue is computed by considering the attendance and average ticket prices. Detailed information is discussed in the simulation sections with the revenue projection (Table 12).

  5. We thank an anonymous reviewer to point out this issue.

  6. AR(1) means first-order autoregressive process (Davidson and MacKinnon 2004).

  7. In our model, we conservatively restrict a star player to be a team’s franchise star who spends most of his career with a focal team. This assumption is reasonable because we frequently observe many transfers of stars across teams, and a fan rarely considers this type of “journeyman” as part of the team history or culture.

  8. Source: Retrosheet.org. “The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at www.retrosheet.org”.

  9. For example, for Boston Red Sox, Facebook: https://www.facebook.com/RedSox/ and Twitter: https://twitter.com/redsox.

  10. N is the total number of samples.

  11. The full names and abbreviation of MLB teams are provided in Appendix I.

  12. If a team cannot participate in the postseason series even with a 70% winning percentage, which is not likely to happen in reality, its fan loyalty is increased only by approximately 0.006 point.

  13. The assumptions of initial equity level and winning percentage are given for the purpose of simulation. We check different levels of equity and winning percentages; this assumption does not influence our conclusions across scenarios.

  14. For our analysis of season attendance, we conservatively consider only home game attendances. The effect would be larger if one considers the impact on the away games as well.

  15. Source: Team Marketing Report (TMR).

  16. The TMR reports the Fan Cost Index (FCI) annually. We do not include the revenue projection along with the FCI here due to the space limitation. This projection is available from the authors upon request.

  17. MLB.com (2018), “5 Biggest Trade Deadline Blockbusters.”

  18. Maxwell (2018, Forbes), “How Major League Baseball Can Fix Its Attendance Problem With Better Marketing.”

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  1. HSBC Business School, Peking University, Shenzhen, China

    Yan Feng

  2. School of Management, Kyung Hee University, Seoul, South Korea

    Yeujun Yoon

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Table 13 Full name of MLB teams
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Feng, Y., Yoon, Y. Dynamic brand evolution mechanism of professional sports teams: empirical analysis using comprehensive major league baseball data. J Brand Manag 27, 237–265 (2020). https://doi.org/10.1057/s41262-019-00156-8

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