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How Success and Uncertainty Compel Interest in Related Goods: Playoff Probability and Out-of-Market Television Viewership in the National Football League

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Abstract

Professional sports league franchises are both on-field competitors and business partners. In this paper, we examine whether local franchise characteristics are associated with the consumption of goods that are produced by other franchises within the same league. Specifically, we investigate how uncertainty over whether the local market team reaches the postseason affects interest in out-of-market National Football League broadcasts. We find that consumers possess a conditional interest in other league contests depending on the degree to which the local franchise is in the race to reach the playoffs. This effect increases at a diminishing rate and reaches a maximum when local franchise playoff probability is approximately 60 %.

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Notes

  1. The exception is when the game is not a sellout, in which case the game is blacked out in the home market and a replacement game is not telecast.

  2. We chose the 2006–2009 time frame because it was the first year of a new broadcasting contract for the NFL. Therefore, the channels on which the national broadcasts took place did not change during the time of our data set.

  3. Relegation is the practice (e.g., in European soccer leagues) whereby, at the end of the season, the bottom few teams in a higher league are required to trade places with the top few teams in a lower league for the forthcoming season.

  4. With the use of advanced statistics becoming more common, mainstream media outlets (including ESPN) regularly reference both individual game win probabilities and week-to-week team playoff probabilities. The latter are included on “NFL Standings” pages on many mainstream media websites. Furthermore, media outlets habitually discuss late season "playoff scenarios” where playoff probabilities are either implied or provided.

  5. AdvancedFootballAnalytics.com was founded and is managed by Brian Burke, who is widely considered to be a pioneer in the area of advanced NFL statistics. He is a regular contributor to The New York Times and The Washington Post, among others.

  6. Accordingly, related to this example, the categorical variable Thursday1, with Thursday denoting day of week and 1 denoting early afternoon, is not included in the model.

  7. LocalSubstitute is additive to Substitute.

  8. We estimated an additional model that included an indicator variable that was equal to unity when the probability of the hometown team qualifying for the playoffs was nonzero; however, the effect was not significant (β = 0.00237, t(1247) = 0.08, p = 0.94).

  9. Using the estimates generated by the model, when LocalSub = 0, the function is given as \( f(x) = ax^{2} + bx + cz, \) where z represents the remaining covariates. The first order derivative of the function is set to zero such that \( x = - \frac{b}{2a} \) within \( \epsilon \left[ {0,100} \right] \).

  10. Rascher and Solmes (2007) find the optimal probability to be 66 % for National Basketball Association (NBA) games. McDonald and Rascher (2000) also find a curvelinear relationship between attendance and game uncertainty, while Whitney (1988) finds a quadratic effect of championship probability. Knowles, Sherony, and Haupert (1992) determine that MLB attendance is maximized when the probability of a home win is 60 %.

  11. A single Nielsen rating point is equivalent to one percent of homes in a given market that are tuned into a specific broadcast. However, Nielsen reports ratings on a 0-100 scale. We “transform” our dependent variable back to its original form [0,1] prior to using fractional regression.

  12. This is further substantiated by an alternative model that considered both whether a team was simply in the race to reach the playoffs and winning percentage. In this estimation, which is available upon request, neither the dummy variable nor the linear quality estimate was statistically significant.

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Acknowledgments

The authors wish to thank (without implicating) Craig Depken II and Brad Humphreys for their helpful suggestions.

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Authors and Affiliations

  1. University of Illinois, 104 Huff Hall MC-584, 1206 S. Fourth St., Champaign, IL, 61821, USA

    Scott Tainsky & Jie Xu

  2. Department of Tourism, Recreation and Sport Management, University of Florida, Gainesville, FL, 32611, USA

    Brian M. Mills

  3. Division of Sport Management, Texas A&M University, College Station, TX, 77843, USA

    Steven Salaga

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  1. Scott Tainsky
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  2. Jie Xu
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Correspondence to Scott Tainsky.

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Tainsky, S., Xu, J., Mills, B.M. et al. How Success and Uncertainty Compel Interest in Related Goods: Playoff Probability and Out-of-Market Television Viewership in the National Football League. Rev Ind Organ 48, 29–43 (2016). https://doi.org/10.1007/s11151-015-9479-7

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