Skip to main content

Movie Analytics and the Future of Film Finance. Are Oscars and Box Office Revenue Predictable?

  • Chapter
  • First Online:
Handbook of State Aid for Film

Abstract

This chapter discusses to which extent modern analytics techniques can help us understand the success of movies, as measured by their box office or attributed Oscars. Interesting lessons emerge from our analyses. Predicting box office revenue on the basis of data available before the release of the movie remains difficult, even with state-of-the-art techniques. Prediction markets are a remarkably powerful tool at predicting success at Oscars. A moderate amount of controversy, as measured by the number of underlying themes raised by movie reviewers, may prove to be helpful in obtaining an Academy Award for Best Picture .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Note that all these variables also give us an estimate of the production budget, even when it is not available. They therefore have a useful role to play in our predictions and have the advantage of being available very early in the shooting of the movie and can very easily be obtained since the movies themselves use the information to advertise.

References

  • Bone, P. F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of Business Research, 32(3), 213–223.

    Article  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Article  Google Scholar 

  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Brown. C. (2015a). Key considerations in film finance. www.slated.com

  • Brown, C. (2015b). Filmed entertainment as an attractive asset class. www.slated.com

  • El Assady, M., Hafner, D., Hund, M., Jäger, A., Jentner, W., Rohrdantz, C., et al. (2013). Visual analytics for the prediction of movie rating and box office performance. IEEE VAST Challenge USB Proceedings.

    Google Scholar 

  • Erikson, R. S., & Wlezien, C. (2008). Are political markets really superior to polls as election predictors? Public Opinion Quarterly, 72(2), 190–215.

    Article  Google Scholar 

  • Freund, Y., & Schapire, R. E. (1996, July). Experiments with a new boosting algorithm. ICML, 96, 148–156.

    Google Scholar 

  • Gold, M., McClarren, R., & Gaughan, C. (2013). The lessons Oscar taught us. Data science and media & entertainment. Big Data, 1(2), 105–109.

    Article  Google Scholar 

  • Haughton, D., McLaughlin, M.-D., Mentzer, K., & Zhang, C. (2015). Movie analytics. A Hollywood introduction to big data. Heidelberg: Springer.

    Google Scholar 

  • Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning. A conditional inference framework. Journal of Computational and Graphical Statistics, 15(3), 651–674.

    Article  Google Scholar 

  • Leamer, E. E. (2008). What’s a recession, anyway? (NBER working paper). National Bureau of Economic Research. http://www.nber.org/papers/w14221

  • Leonhardt, D. (2015, February 23). Oscars 2015. An excellent night for prediction markets. The New York Times.

    Google Scholar 

  • Liu, B. (2011). Web data mining: exploring hyperlinks, contents, and usage data. Berlin: Springer.

    Google Scholar 

  • McKenzie, J. (2012). The economics of movies. A literature survey. Journal of Economic Surveys, 26(1), 42–70.

    Article  Google Scholar 

  • Mestyán, M., Yasseri, T., & Kertész, J. (2013). Early prediction of movie box office success based on Wikipedia activity big data. PLoS One, 8(8), e71226. https://doi.org/10.1371/journal.pone.0071226

    Article  Google Scholar 

  • O’Leary, S., & Sheehan, K. (2008). Building buzz to beat the big boys: word-of-mouth marketing for small businesses. Westport, CT: Praeger.

    Google Scholar 

  • Rothschild, D., & Wolfers, J. (2008). Market manipulation muddies election outlook. http://online.wsj.com/article/SB122283114935193363.html

  • Saxon, I. (2010). Intrade prediction market accuracy and efficiency. An analysis of the 2004 and 2008 Democratic Presidential Nomination Contests. Dissertation, University of Nottingham.

    Google Scholar 

  • Shambaugh, J. C. (2012). The Euro’s three crises. Brookings Papers on Economic Activity, 43, 157–231.

    Article  Google Scholar 

  • Zhang, Z., & Li, X. (2010). Controversy in marketing. Mining sentiments in social media. In Proceedings of the 43rd Hawaii international conference on systems sciences.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dominique Haughton .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bruneel, C. et al. (2018). Movie Analytics and the Future of Film Finance. Are Oscars and Box Office Revenue Predictable?. In: Murschetz, P., Teichmann, R., Karmasin, M. (eds) Handbook of State Aid for Film. Media Business and Innovation. Springer, Cham. https://doi.org/10.1007/978-3-319-71716-6_30

Download citation

Publish with us

Policies and ethics