IDEAS home Printed from https://ideas.repec.org/a/rfa/smcjnl/v4y2016i2p13-22.html
   My bibliography  Save this article

Moneyball for TV: A Model for Forecasting the Audience of New Dramatic Television Series

Author

Listed:
  • Starling D. Hunter III
  • Ravi Chinta
  • Susan Smith
  • Awais Shamim
  • Alya Bawazir

Abstract

The specific objective of the present study is to develop and test an early-stage, empirical model for predicting the audience of new television series. We test our model on a sample of 107 new dramatic television series that debuted on one of the four major US television networks during the 2010-2014 seasons. In particular we examine the role of three previously untested predictors of the performance of new television shows, all of which can be known prior to the decision to greenlight the pilot script. Those three are the originality of the concept of the show, the track record of success of the show¡¯s creative team, and the size of the conceptual network created from the teleplay of the pilot episode.

Suggested Citation

  • Starling D. Hunter III & Ravi Chinta & Susan Smith & Awais Shamim & Alya Bawazir, 2016. "Moneyball for TV: A Model for Forecasting the Audience of New Dramatic Television Series," Studies in Media and Communication, Redfame publishing, vol. 4(2), pages 13-22, December.
    • Handle: RePEc:rfa:smcjnl:v:4:y:2016:i:2:p:13-22
    as

    Download full text from publisher

    File URL: http://redfame.com/journal/index.php/smc/article/view/1611/1803
    Download Restriction: no
    File URL: http://redfame.com/journal/index.php/smc/article/view/1611
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. W. Walls, 2005. "Modeling Movie Success When ‘Nobody Knows Anything’: Conditional Stable-Distribution Analysis Of Film Returns," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 29(3), pages 177-190, August.
    2. Basuroy, Suman & Chatterjee, Subimal, 2008. "Fast and frequent: Investigating box office revenues of motion picture sequels," Journal of Business Research, Elsevier, vol. 61(7), pages 798-803, July.
    3. Randy Nelson & Robert Glotfelty, 2012. "Movie stars and box office revenues: an empirical analysis," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 36(2), pages 141-166, May.
    4. Richard E. Caves, 2003. "Contracts Between Art and Commerce," Journal of Economic Perspectives, American Economic Association, vol. 17(2), pages 73-83, Spring.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mateusz Piwowarski & Katarzyna Gadomska-Lila & Kesra Nermend, 2021. "Cognitive Neuroscience Methods in Enhancing Health Literacy," IJERPH, MDPI, vol. 18(10), pages 1-19, May.
    2. Van Reeth, Daam, 2019. "Forecasting Tour de France TV audiences: A multi-country analysis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 810-821.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaenssle Sophia & Budzinski Oliver & Astakhova Daria, 2018. "Conquering the Box Office: Factors Influencing Success of International Movies in Russia," Review of Network Economics, De Gruyter, vol. 17(4), pages 245-266, December.
    2. Hofmann, Julian & Clement, Michel & Völckner, Franziska & Hennig-Thurau, Thorsten, 2017. "Empirical generalizations on the impact of stars on the economic success of movies," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 442-461.
    3. Lili Kang & Fei Peng, 2024. "Star power as quality signal or marketing effect? A path analysis on China's motion‐picture industry," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3639-3655, July.
    4. Ana Suárez-Vázquez & José Quevedo, 2015. "Analyzing superstars’ power using support vector machines," Empirical Economics, Springer, vol. 49(4), pages 1521-1542, December.
    5. Daniel Kaimann, 2014. "Combining Qualitative Comparative Analysis and Shapley Value Decomposition: A Novel Approach for Modeling Complex Causal Structures in Dynamic Markets," Working Papers Dissertations 12, Paderborn University, Faculty of Business Administration and Economics.
    6. Frederick Derrick & Nancy Williams & Charles Scott, 2014. "A two-stage proxy variable approach to estimating movie box office receipts," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 38(2), pages 173-189, May.
    7. Mun, Mak Kit & Chong, Choo Wei, 2018. "Forecasting Movie Demand Using Total and Split Exponential Smoothing," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 81-94.
    8. Hwang, ShinYoung & Kim Seongcheol, 2017. "What triggers the use of mIM service provider’s sequel O2O service extensions?," 14th ITS Asia-Pacific Regional Conference, Kyoto 2017: Mapping ICT into Transformation for the Next Information Society 168494, International Telecommunications Society (ITS).
    9. W. D. Walls, 2005. "Modelling heavy tails and skewness in film returns," Applied Financial Economics, Taylor & Francis Journals, vol. 15(17), pages 1181-1188.
    10. McKenzie, Jordi, 2013. "Predicting box office with and without markets: Do internet users know anything?," Information Economics and Policy, Elsevier, vol. 25(2), pages 70-80.
    11. McMahon, James, 2013. "The Rise of a Confident Hollywood: Risk and the Capitalization of Cinema," EconStor Preprints 157854, ZBW - Leibniz Information Centre for Economics.
    12. Chien-Ping Chen, 2009. "A Puzzle or a Choice: Uniform Pricing for Motion Pictures at the Box," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 37(1), pages 73-85, March.
    13. Gans, Joshua S., 2015. "“Selling Out” and the impact of music piracy on artist entry," Information Economics and Policy, Elsevier, vol. 32(C), pages 58-64.
    14. Davide Lauria & Wyatt D. Phillips, 2021. "Insuring Hollywood: A Movie Returns Index and the American Stock Market," JRFM, MDPI, vol. 14(5), pages 1-33, April.
    15. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 2018. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 20(3), pages 577-588, June.
    16. John Sedgwick & Michael Pokorny, 2010. "Consumers as risk takers: Evidence from the film industry during the 1930s," Business History, Taylor & Francis Journals, vol. 52(1), pages 74-99.
    17. A. E. Scorcu & R. Zanola, 2018. "The drowned and the saved: the determinants of success in the Italian temporary art and cultural exhibitions market," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 42(2), pages 201-211, May.
    18. Baranowski Paweł & Korczak Karol & Zając Jarosław, 2020. "Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland," Business Systems Research, Sciendo, vol. 11(1), pages 73-88, March.
    19. Sällström, Susanna, 2007. "Practice Makes Perfect: On Professional Standards," CEPR Discussion Papers 6377, C.E.P.R. Discussion Papers.
    20. Riccardo Welters & Ryan Daniel & Katja Fleischmann, 2018. "Creative industries in a regional city: How much work is lost to rivals based elsewhere?," Local Economy, London South Bank University, vol. 33(1), pages 63-84, February.

    More about this item

    Keywords

    television; network analysis; pilot episodes; ratings; audience; dramatic series; content analysis; forecasting;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rfa:smcjnl:v:4:y:2016:i:2:p:13-22. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Redfame publishing (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.