1 / 52

Blockbusters, Bombs & Sleepers The Income Distribution of Movies

Blockbusters, Bombs & Sleepers The Income Distribution of Movies. Sitabhra Sinha The Institute of Mathematical Sciences Chennai (Madras), India. “There’s no business like show business”. A Pareto Law for Movies. Why look at Movie Income ?. Movie income is a well-defined quantity;

tender
Download Presentation

Blockbusters, Bombs & Sleepers The Income Distribution of Movies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Blockbusters, Bombs & SleepersThe Income Distribution of Movies Sitabhra Sinha The Institute of Mathematical Sciences Chennai (Madras), India

  2. “There’s no business like show business” A Pareto Law for Movies Why look at Movie Income ? Movie income is a well-defined quantity; Income distribution can be empirically determined Pareto exponent for Movie Income :   2 But Asset exchange models for explaining Pareto Law in wealth/income distribution cannot be applied ! Movies don’t exchange anything between themselves !!

  3. Movies popularity distribution → a prominent member of the class of popularity distributions Popularity of Products/Ideas • Movies: S Sinha & S Raghavendra (2004) Eur Phys J B, 42, 293 • Scientific Papers: S Redner (1998) Eur Phys J B, 4, 131 • Books: D Sornette et al (2004) Phys Rev Lett, 93, 228701

  4. The Popularity of Scientific Papers Measure of popularity : citation distribution Relation between exponents for  : Cumulative probability (Pareto Law) 1+: Probability distrn (Power law) 1/  : Rank distribution (Zipfs Law) ISI 1+ = 3 1/  0.48 Phys Rev D  Pareto exponent  2

  5. The Popularity of Books Measure of popularity : Book sales at amazon.com  Pareto exponent  2

  6. A ‘ Hit ’ is Born: The Dynamics of Popularity Conjecture: Universality Pareto exponent for popularity distributions  2

  7. Outline of the Talk • Empirical : Distributions SS & S Raghavendra (2004) Eur Phys J B, 42: 293-296 • Empirical : Time evolution SS & R K Pan, in preparation • Model SS & S Raghavendra (2004) SFI Working Paper 04-09-028 SS & S Raghavendra (2005) to appear in Practical Fruits of Econophysics, Proc 3rd Nikkei Econophysics Symposium, Springer-Tokyo

  8. Outline of the Talk • Empirical : Distributions SS & S Raghavendra (2004) Eur Phys J B, 42: 293-296 • Empirical : Time evolution SS & R K Pan, in preparation • Model SS & S Raghavendra (2004) SFI Working Paper 04-09-028 SS & S Raghavendra (2005) to appear in Practical Fruits of Econophysics, Proc 3rd Nikkei Econophysics Symposium, Springer-Tokyo

  9. Measuring Popularity Popularity of a movie can be estimated in various ways: e.g., Number of votes received from registered users in IMDB database Or, DVD/Video rentals from Blockbuster Stores However, these are for movies released long ago: lot of information available for people to decide What about newly released movies still running in theatres ? What’s the income, dude ?

  10. Income Distribution Snapshot Each week, about 100-150 movies running in theatres across USA Too few data points, too much scatter Hard to make a call on the nature of the distribution !

  11. The Movie Year: Seasonal Fluctuations in Movie Income over a Year Makes sense to look at income distribution over a year: we can ignore seasonal variations

  12. Popularity Distribution of movies released in USA during 1999-2003 acc to weeks in Top 60 Gaussian distribution Long tail: the most popular movies do not fit a Gaussian! slope  - 0.25 Rank distribution of movies: explores the tail of the distribution containing the most popular movies Data for all years fall on the same curve after normalizing !!

  13. Gross Income Distrn of movies released in USA during 1997-2003 Opening Gross Distribution scaled by average gross to correct for inflation Kink indicating bimodality Bimodal distribution of opening gross Movies either do very badly or very well on opening !

  14. Gross Income Distrn of movies released in USA during 1997-2003 Opening Gross 1/  0.5 Distribution scaled by average gross to correct for inflation Total Gross 1/  0.5 • Pareto exponent  2 at opening week and remains so through the entire theatre lifespan Unimodal The only contribution of movies which perform well long after opening (sleepers)

  15. Relation between longevity at Top 60 & Total Gross GTotal ~ T 2 IMAX movies Slope ~ 2.14

  16. Outline of the Talk • Empirical : Distributions SS & S Raghavendra (2004) Eur Phys J B, 42: 293-296 • Empirical : Time evolution SS & R K Pan, in preparation • Model SS & S Raghavendra (2004) SFI Working Paper 04-09-028 SS & S Raghavendra (2005) to appear in Practical Fruits of Econophysics, Proc 3rd Nikkei Econophysics Symposium, Springer-Tokyo

  17. A Movie Bestiary • Classifying Movies according to the time evolution of their income • Blockbusters: High Opening Gross, High Total Gross Intermediate to long theatre lifespan • Bombs: Low Opening Gross, Low Total Gross Short theatre lifespan • Sleepers: Low Opening Gross, High Total Gross Long theatre lifespan

  18. Spiderman (2002) Peaks on weekends Exponential decay Daily earnings A classic blockbuster Weekend earnings

  19. Spiderman 2 (2004) A blockbuster … but like most sequels, earned less & ran fewer weeks than the original !

  20. The Blockbuster Strategy “If it doesn’t open, you are dead !”- Robert Evans, Hollywood producer The opening is the most critical event in a film’s commercial life FACT: > 80 % of all movies earn maximum box-office revenue in the first week after release • Jaws (1975) : the first movie to be released using the (now classic) blockbuster strategy : • Heavy pre-release advertising • Presence of star/stars with name recognition • Wide release Underlying assumption : ‘Herding’ effect among movie audience • A large opening will induce others to see the movie !

  21. BLOCKBUSTERS: Examples • Very high opening gross • Exponential decay in subsequent earnings

  22. Lord of the Rings 3: Return of the King (2003) Top grosser of the year !

  23. Harry Potter and the Sorcerer’s Stone (2001)

  24. The Sixth Sense ( 1999) Blockbuster…. but behaved like a sleeper very late in its theatre lifespan ! (longest time at top 60 for non-IMAX movie - 40 weeks)

  25. BOMBS: Examples • Very low opening gross • Exponential decay in subsequent earnings • Earns significantly less than budget

  26. Bulletproof Monk (2003) Spectacular flop ! Production budget: $ 50 Million Advertising budget: $ 25 Million

  27. American Psycho (2000)

  28. SLEEPERS: Examples • Very low opening gross • Sudden rise in subsequent earnings before eventual exponential decay

  29. My Big Fat Greek Wedding (2002) Gradual rise in income Subsequent exponential decay A classic sleeper ! Produced outside Hollywood Extremely long theatre lifespan

  30. The Blair Witch Project (1999) Another Hollywood outsider sleeper

  31. Mystic River (2003) Publicity Buildup to Oscar Awards A Hollywood insider sleeper ! Unusual: Multiple rises in income during theatre lifespan

  32. To compare 2004 Spiderman 2 2003 Lord of the Rings 3: Return of the King Mystic River Bulletproof Monk 2002 Spiderman My Big Fat Greek Wedding 2001 Harry Potter and the Sorcerers' Stone 2000 American Psycho 1999 The Sixth Sense Blair Witch Project Color code: Blockbuster Sleeper Bomb

  33. Comparing the Income Growth / Decay of Movies Scaled by opening gross Income of most movies decay exponentially with the same decay rate < 5 weeks

  34. Outline of the Talk • Empirical : Distributions SS & S Raghavendra (2004) Eur Phys J B, 42: 293-296 • Empirical : Time evolution SS & R K Pan, in preparation • Model SS & S Raghavendra (2004) SFI Working Paper 04-09-028 SS & S Raghavendra (2005) to appear in Practical Fruits of Econophysics, Proc 3rd Nikkei Econophysics Symposium, Springer-Tokyo

  35. Puzzle • The Pareto tail appears at the opening week itself Asset exchange models don’t apply Can’t be explained by information exchange about a movie through interaction between people Need a different approach

  36. Popularity = Collective Choice Process of emergence of collective decision • in a society of agents free to choose • constrained by limited information • having heterogeneous beliefs. • Example: Movie popularity.

  37. Collective Choice: A Naive Approach • Each agent chooses randomly independent of all other agents. • Collective decision: sum of all individual choices. • Example: YES/NO voting on an issue • For binary choice Individual agent: S = 0 or 1 Collective choice: M = Σ S • Result: Normal distribution. NO YES 0 % Collective Decision M 100%

  38. Modeling emergence of collective choice Agent’s choice depends on • Personal belief (expectation from a particular choice) • Herding (through interaction with neighbors) 2 factors affect the evolution of an agent’s belief • Adaptation (to previous choice): Belief changes with time to make subsequent choice of the same alternative less likely • Learning (by global feedback through media): The agent will be affected by how her previous choice accorded with the collective choice (M).

  39. The Model:‘Adaptive Field’ Ising Model • Binary choice :2 possible choice states (S = ± 1). • Choice dynamics of the ith agent at time t: for square lattice • Belief dynamics of the ith agent at time t: is the collective decision where • μ: Adaptation timescale • λ: Learning timescale

  40. Results • Long-range order for λ > 0

  41. Initial state of the S field: 1000 × 1000 agents

  42. μ =0.1 λ = 0: No long-range order N = 1000, T = 10000 itrns Square Lattice (4 neighbors)

  43. μ =0.1 λ > 0: clustering λ = 0.05 N = 1000, T = 200 itrns Square Lattice (4 neighbors)

  44. Results • Long-range order for λ > 0 • Self-organized pattern formation

  45. μ =0.1 Ordered patterns emerge asymptotically λ = 0.05

  46. Results • Long-range order for λ > 0 • Self-organized pattern formation • Multiple ordered domains • Behavior of agents belonging to each such domain is highly correlated • Distinct ‘cultural groups’ (Axelrod).

  47. Results • Long-range order for λ > 0 • Self-organized pattern formation • Multiple ordered domains • Behavior of agents belonging to each such domain is highly correlated • Distinct ‘cultural groups’ (Axelrod). • Phase transition • Unimodal to bimodal distribution as λ increases.

  48. Bimodality with increasing λ

  49. Results • Long-range order for λ > 0 • Self-organized pattern formation • Multiple ordered domains • Behavior of agents belonging to each such domain is highly correlated • Distinct ‘cultural groups’ (Axelrod). • Phase transition • Unimodal to bimodal distribution as λ increases. • Similar results for agents on scale-free network

  50. OK… but does it explain reality ? Rank distribution: Compare real data with model US Movie Opening Gross Model: randomly distributed λ Model

More Related